Collaborative Information Retrieval: Concepts, Models and Evaluation
1. 1. Collaboration and Information Retrieval 2. Collaborative IR techniques and models 3. Evaluation 4. Challenges ahead 5. Discussion
Collaborative Information Retrieval: Concepts, Models and
Evaluation
Lynda Tamine
Paul Sabatier University
IRIT, Toulouse - France
Laure Soulier
Pierre and Marie Curie University
LIP6, Paris - France
April 10, 2016
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2. 1. Collaboration and Information Retrieval 2. Collaborative IR techniques and models 3. Evaluation 4. Challenges ahead 5. Discussion
OVERVIEW OF THE RESEARCH AREA
c [Shah, 2012]
• Publications
Papers in several conferences (SIGIR, CIKM, ECIR, CHI, CSCW,...) and journals (IP&M,
JASIST, JIR, IEEE, ...)
Books on ”Collaborative Information Seeking”
[Morris and Teevan, 2009, Shah, 2012, Hansen et al., 2015]
Special issues on ”Collaborative Information Seeking” (IP&M, 2010; IEEE, 2014)
• Workshops and Tutorials
Collaborative Information Behavior: GROUP 2009
Collaborative Information Seeking: GROUP 2010, CSCW 2010, ASIST 2011 and CSCW 2013
Collaborative Information Retrieval: JCDL 2008 and CIKM 2011
Evaluation in Collaborative Information Retrieval: CIKM 2015
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3. 1. Collaboration and Information Retrieval 2. Collaborative IR techniques and models 3. Evaluation 4. Challenges ahead 5. Discussion
COLLABORATION IN FEW NUMBERS [MORRIS, 2008, MORRIS, 2013]
• On which occasion do you collaborate?
Collaboration purposes
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4. 1. Collaboration and Information Retrieval 2. Collaborative IR techniques and models 3. Evaluation 4. Challenges ahead 5. Discussion
COLLABORATION IN FEW NUMBERS [MORRIS, 2008, MORRIS, 2013]
• On which occasion do you collaborate?
Collaboration purposes
Task Frequency
Travel planing 27.5%
Online shopping 25.7%
Bibliographic search 20.2 %
Technical search 16.5 %
Fact-finding 16.5 %
Social event planing 12.8 %
Health search 6.4 %
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5. 1. Collaboration and Information Retrieval 2. Collaborative IR techniques and models 3. Evaluation 4. Challenges ahead 5. Discussion
COLLABORATION IN FEW NUMBERS [MORRIS, 2008, MORRIS, 2013]
• On which occasion do you collaborate?
Collaboration purposes
Task Frequency
Travel planing 27.5%
Online shopping 25.7%
Bibliographic search 20.2 %
Technical search 16.5 %
Fact-finding 16.5 %
Social event planing 12.8 %
Health search 6.4 %
Application domains
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6. 1. Collaboration and Information Retrieval 2. Collaborative IR techniques and models 3. Evaluation 4. Challenges ahead 5. Discussion
COLLABORATION IN FEW NUMBERS [MORRIS, 2008, MORRIS, 2013]
• On which occasion do you collaborate?
Collaboration purposes
Task Frequency
Travel planing 27.5%
Online shopping 25.7%
Bibliographic search 20.2 %
Technical search 16.5 %
Fact-finding 16.5 %
Social event planing 12.8 %
Health search 6.4 %
Application domains
Domain Example
Medical Physician/Patient - Physician/Nurse
Digital library Librarians/Customers
E-Discovery Fee-earners/Customers - Contact reviewer/Lead counsel
Academic groups of students
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7. 1. Collaboration and Information Retrieval 2. Collaborative IR techniques and models 3. Evaluation 4. Challenges ahead 5. Discussion
COLLABORATION IN FEW NUMBERS [MORRIS, 2008, MORRIS, 2013]
• How do you collaborate?
How often?
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8. 1. Collaboration and Information Retrieval 2. Collaborative IR techniques and models 3. Evaluation 4. Challenges ahead 5. Discussion
COLLABORATION IN FEW NUMBERS [MORRIS, 2008, MORRIS, 2013]
• How do you collaborate?
How often?
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9. 1. Collaboration and Information Retrieval 2. Collaborative IR techniques and models 3. Evaluation 4. Challenges ahead 5. Discussion
COLLABORATION IN FEW NUMBERS [MORRIS, 2008, MORRIS, 2013]
• How do you collaborate?
How often? Group size?
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10. 1. Collaboration and Information Retrieval 2. Collaborative IR techniques and models 3. Evaluation 4. Challenges ahead 5. Discussion
COLLABORATION IN FEW NUMBERS [MORRIS, 2008, MORRIS, 2013]
• How do you collaborate?
How often? Group size?
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11. 1. Collaboration and Information Retrieval 2. Collaborative IR techniques and models 3. Evaluation 4. Challenges ahead 5. Discussion
COLLABORATION IN FEW NUMBERS [MORRIS, 2008, MORRIS, 2013]
• How do you collaborate?
How often? Group size?
Collaborative settings?
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12. 1. Collaboration and Information Retrieval 2. Collaborative IR techniques and models 3. Evaluation 4. Challenges ahead 5. Discussion
COLLABORATION IN FEW NUMBERS [MORRIS, 2008, MORRIS, 2013]
• How do you collaborate?
How often? Group size?
Collaborative settings?
22% 11.9% 66.1%
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13. 1. Collaboration and Information Retrieval 2. Collaborative IR techniques and models 3. Evaluation 4. Challenges ahead 5. Discussion
OUTLINE
1. Collaboration and Information Retrieval
2. Collaborative IR techniques and models
3. Evaluation
4. Challenges ahead
5. Discussion
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14. Collaboration and Information Retrieval 2. Collaborative IR techniques and models 3. Evaluation 4. Challenges ahead 5. Discussion
PLAN
1. Collaboration and Information Retrieval
Users and Information Retrieval
The notion of collaboration
Collaboration paradigms
Collaborative search approaches
Collaborative search interfaces
2. Collaborative IR techniques and models
3. Evaluation
4. Challenges ahead
5. Discussion
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15. Collaboration and Information Retrieval 2. Collaborative IR techniques and models 3. Evaluation 4. Challenges ahead 5. Discussion
AD-HOC INFORMATION RETRIEVAL
LET’S START BY WHAT YOU ALREADY KNOW...
• Ranking documents with respect to a query
• How?
Term weighting/Document scoring [Robertson and Walker, 1994, Salton, 1971]
Query Expansion/Reformulation [Rocchio, 1971]
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16. Collaboration and Information Retrieval 2. Collaborative IR techniques and models 3. Evaluation 4. Challenges ahead 5. Discussion
USERS AND INFORMATION RETRIEVAL
LET’S START BY WHAT YOU ALREADY KNOW...
• Personalized IR [Kraft et al., 2005, Gauch et al., 2003, Liu et al., 2004]
Personalizing search results to user’s context, preferences
and interests
How?
Modeling user’s profile
Integrating the user’s context and preferences within the
document scoring
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17. Collaboration and Information Retrieval 2. Collaborative IR techniques and models 3. Evaluation 4. Challenges ahead 5. Discussion
USERS AND INFORMATION RETRIEVAL
LET’S START BY WHAT YOU ALREADY KNOW...
• Personalized IR [Kraft et al., 2005, Gauch et al., 2003, Liu et al., 2004]
Personalizing search results to user’s context, preferences
and interests
How?
Modeling user’s profile
Integrating the user’s context and preferences within the
document scoring
• Collaborative filtering [Resnick et al., 1994]
Recommending search results using ratings/preferences
of other users
How?
Inferring user’s own preferences from other users’
preferences
Personalizing search results
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18. Collaboration and Information Retrieval 2. Collaborative IR techniques and models 3. Evaluation 4. Challenges ahead 5. Discussion
USERS AND INFORMATION RETRIEVAL
LET’S START BY WHAT YOU ALREADY KNOW...
• Personalized IR [Kraft et al., 2005, Gauch et al., 2003, Liu et al., 2004]
Personalizing search results to user’s context, preferences
and interests
How?
Modeling user’s profile
Integrating the user’s context and preferences within the
document scoring
• Collaborative filtering [Resnick et al., 1994]
Recommending search results using ratings/preferences
of other users
How?
Inferring user’s own preferences from other users’
preferences
Personalizing search results
• Social Information Retrieval [Amer-Yahia et al., 2007, Pal and Counts, 2011]
Exploiting social media platforms to retrieve
document/users...
How?
Social network analysis (graph structure, information
diffusion, ...)
Integrating social-based features within the document
relevance scoring
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19. Collaboration and Information Retrieval 2. Collaborative IR techniques and models 3. Evaluation 4. Challenges ahead 5. Discussion
USERS AND INFORMATION RETRIEVAL
LET’S START BY WHAT YOU ALREADY KNOW...
• Personalized IR [Kraft et al., 2005, Gauch et al., 2003, Liu et al., 2004]
Personalizing search results to user’s context, preferences
and interests
How?
Modeling user’s profile
Integrating the user’s context and preferences within the
document scoring
• Collaborative filtering [Resnick et al., 1994]
Recommending search results using ratings/preferences
of other users
How?
Inferring user’s own preferences from other users’
preferences
Personalizing search results
• Social Information Retrieval [Amer-Yahia et al., 2007, Pal and Counts, 2011]
Exploiting social media platforms to retrieve
document/users...
How?
Social network analysis (graph structure, information
diffusion, ...)
Integrating social-based features within the document
relevance scoring
Let’s have a more in-depth look on...
Collaborative Information Retrieval 8 / 111
20. Collaboration and Information Retrieval 2. Collaborative IR techniques and models 3. Evaluation 4. Challenges ahead 5. Discussion
THE NOTION OF COLLABORATION
DEFINITION
Definition
‘A process through which parties who see different aspects of a problem can constructively explore
their differences and search for solutions that go beyond their own limited vision of what is possible.”
[Gray, 1989]
Definition
‘Collaboration is a process in which autonomous actors interact through formal and informal
negotiation, jointly creating rules and struc- tures governing their relationships and ways to act or
decide on the issues that brought them together ; it is a process involving shared norms and mutually
beneficial interactions.” [Thomson and Perry, 2006]
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21. Collaboration and Information Retrieval 2. Collaborative IR techniques and models 3. Evaluation 4. Challenges ahead 5. Discussion
THE NOTION OF COLLABORATION
THE 5WS OF THE COLLABORATION [MORRIS AND TEEVAN, 2009, SHAH, 2010]
What?
Tasks: Complex, exploratory or fact-finding tasks, ...
Application domains: Bibliographic, medical, e-Discovery, academic search
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22. Collaboration and Information Retrieval 2. Collaborative IR techniques and models 3. Evaluation 4. Challenges ahead 5. Discussion
THE NOTION OF COLLABORATION
THE 5WS OF THE COLLABORATION [MORRIS AND TEEVAN, 2009, SHAH, 2010]
What?
Tasks: Complex, exploratory or fact-finding tasks, ...
Application domains: Bibliographic, medical, e-Discovery, academic search
Why?
Shared interests
Insufficient knowledge
Mutual beneficial goals
Division of labor
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23. Collaboration and Information Retrieval 2. Collaborative IR techniques and models 3. Evaluation 4. Challenges ahead 5. Discussion
THE NOTION OF COLLABORATION
THE 5WS OF THE COLLABORATION [MORRIS AND TEEVAN, 2009, SHAH, 2010]
What?
Tasks: Complex, exploratory or fact-finding tasks, ...
Application domains: Bibliographic, medical, e-Discovery, academic search
Why?
Shared interests
Insufficient knowledge
Mutual beneficial goals
Division of labor
Who?
Groups vs. Communities
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24. Collaboration and Information Retrieval 2. Collaborative IR techniques and models 3. Evaluation 4. Challenges ahead 5. Discussion
THE NOTION OF COLLABORATION
THE 5WS OF THE COLLABORATION [MORRIS AND TEEVAN, 2009, SHAH, 2010]
What?
Tasks: Complex, exploratory or fact-finding tasks, ...
Application domains: Bibliographic, medical, e-Discovery, academic search
Why?
Shared interests
Insufficient knowledge
Mutual beneficial goals
Division of labor
Who?
Groups vs. Communities
When?
Synchronous vs. Asynchronous
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25. Collaboration and Information Retrieval 2. Collaborative IR techniques and models 3. Evaluation 4. Challenges ahead 5. Discussion
THE NOTION OF COLLABORATION
THE 5WS OF THE COLLABORATION [MORRIS AND TEEVAN, 2009, SHAH, 2010]
What?
Tasks: Complex, exploratory or fact-finding tasks, ...
Application domains: Bibliographic, medical, e-Discovery, academic search
Why?
Shared interests
Insufficient knowledge
Mutual beneficial goals
Division of labor
Who?
Groups vs. Communities
When?
Synchronous vs. Asynchronous
Where?
Colocated vs. Remote
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26. Collaboration and Information Retrieval 2. Collaborative IR techniques and models 3. Evaluation 4. Challenges ahead 5. Discussion
THE NOTION OF COLLABORATION
THE 5WS OF THE COLLABORATION [MORRIS AND TEEVAN, 2009, SHAH, 2010]
What?
Tasks: Complex, exploratory or fact-finding tasks, ...
Application domains: Bibliographic, medical, e-Discovery, academic search
Why?
Shared interests
Insufficient knowledge
Mutual beneficial goals
Division of labor
Who?
Groups vs. Communities
When?
Synchronous vs. Asynchronous
Where?
Colocated vs. Remote
How?
Crowdsourcing
Implicit vs. Explicit intent
User mediation
System mediation
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27. Collaboration and Information Retrieval 2. Collaborative IR techniques and models 3. Evaluation 4. Challenges ahead 5. Discussion
THE NOTION OF COLLABORATION
COLLABORATIVE INFORMATION RETRIEVAL (CIR) [FOSTER, 2006, GOLOVCHINSKY ET AL., 2009]
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28. Collaboration and Information Retrieval 2. Collaborative IR techniques and models 3. Evaluation 4. Challenges ahead 5. Discussion
THE NOTION OF COLLABORATION
COMPARING CIR WITH OTHER IR APPROACHES
Exercice
How do you think that CIR differs from Personalized IR, Collaborative Filtering, or Social IR?
• User (unique/group)
• Personalization (yes/no)
• Collaboration (implicit/explicit)
• Concurrency (collocated/remote)
• Collaboration benefit (symmetric/asymmetric)
• Communication (yes/no)
• ...
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29. Collaboration and Information Retrieval 2. Collaborative IR techniques and models 3. Evaluation 4. Challenges ahead 5. Discussion
THE NOTION OF COLLABORATION
COMPARING CIR WITH OTHER IR APPROACHES
Exercice
How do you think that CIR differs from Personalized IR, Collaborative Filtering, or Social IR?
Perso. IR Collab. Filtering Social IR Collab. IR
User
unique
group
Personalization
no
yes
Collaboration
implicit
explicit
Concurrency
synchronous
asynchronous
Benefit
symmetric
asymmetric
Communication
no
yes
Information usage
Information exchange
Information retrieval
Information synthesis
Sensemaking
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30. Collaboration and Information Retrieval 2. Collaborative IR techniques and models 3. Evaluation 4. Challenges ahead 5. Discussion
COLLABORATION PARADIGMS [FOLEY AND SMEATON, 2010,
KELLY AND PAYNE, 2013, SHAH AND MARCHIONINI, 2010]
Division of labor • Role-based division of labor
• Document-based division of labor
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31. Collaboration and Information Retrieval 2. Collaborative IR techniques and models 3. Evaluation 4. Challenges ahead 5. Discussion
COLLABORATION PARADIGMS [FOLEY AND SMEATON, 2010,
KELLY AND PAYNE, 2013, SHAH AND MARCHIONINI, 2010]
Division of labor • Role-based division of labor
• Document-based division of labor
Sharing of knowledge • Communication and shared workspace
• Ranking based on relevance judgements
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32. Collaboration and Information Retrieval 2. Collaborative IR techniques and models 3. Evaluation 4. Challenges ahead 5. Discussion
COLLABORATION PARADIGMS [FOLEY AND SMEATON, 2010,
KELLY AND PAYNE, 2013, SHAH AND MARCHIONINI, 2010]
Division of labor • Role-based division of labor
• Document-based division of labor
Sharing of knowledge • Communication and shared workspace
• Ranking based on relevance judgements
Awareness • Collaborators’ actions
• Collaborators’ context
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33. Collaboration and Information Retrieval 2. Collaborative IR techniques and models 3. Evaluation 4. Challenges ahead 5. Discussion
COLLABORATIVE INFORMATION RETRIEVAL
COLLABORATIVE SEARCH SESSION
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34. Collaboration and Information Retrieval 2. Collaborative IR techniques and models 3. Evaluation 4. Challenges ahead 5. Discussion
STRUCTURE OF THE COLLABORATIVE SEARCH SESSIONS
• The 3 phases
of the social
search model
[Evans and Chi, 2010]
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35. Collaboration and Information Retrieval 2. Collaborative IR techniques and models 3. Evaluation 4. Challenges ahead 5. Discussion
STRUCTURE OF THE COLLABORATIVE SEARCH SESSIONS
• The 3 phases of the
collaborators
behavioral model
[Karunakaran et al., 2013]
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36. Collaboration and Information Retrieval 2. Collaborative IR techniques and models 3. Evaluation 4. Challenges ahead 5. Discussion
COLLABORATIVE SEARCH APPROACHES [JOHO ET AL., 2009]
• “Development of new IR models that can take collaboration into account in retrieval.”
• “Leverage IR techniques such as relevance feedback, clustering, profiling, and data
fusion to support collaborative search while using conventional IR models.”
• “Develop search interfaces that allow people to perform search tasks in
collaboration.interfaces”
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37. Collaboration and Information Retrieval 2. Collaborative IR techniques and models 3. Evaluation 4. Challenges ahead 5. Discussion
COLLABORATIVE SEARCH INTERFACES
What could be collaborative in search interfaces [Shah, 2012, Thomson and Perry, 2006]:
• Communication tools for defining search strategies, users’ roles as well as sharing relevant
information [Golovchinsky et al., 2011, Kelly and Payne, 2013]
• Awareness tools for reporting collaborators’ actions
[Diriye and Golovchinsky, 2012, Rodriguez Perez et al., 2011]
• Individual and shared workspace to ensure mutual beneficial goals
• Algorithmic mediation to monitor collaborators’ actions
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38. Collaboration and Information Retrieval 2. Collaborative IR techniques and models 3. Evaluation 4. Challenges ahead 5. Discussion
COLLABORATIVE SEARCH INTERFACES
What could be collaborative in search interfaces [Shah, 2012, Thomson and Perry, 2006]:
• Communication tools for defining search strategies, users’ roles as well as sharing relevant
information [Golovchinsky et al., 2011, Kelly and Payne, 2013]
• Awareness tools for reporting collaborators’ actions
[Diriye and Golovchinsky, 2012, Rodriguez Perez et al., 2011]
• Individual and shared workspace to ensure mutual beneficial goals
• Algorithmic mediation to monitor collaborators’ actions
• User-driven collaborative interfaces
Collaborators fully active
Collaboration support through devices
(interactive tabletop) or tools (web interfaces)
• System-mediated collaborative interfaces
Collaborators partially active
Collaboration support through algorithmic
mediation (e.g., document distribution
according roles or not)
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39. Collaboration and Information Retrieval 2. Collaborative IR techniques and models 3. Evaluation 4. Challenges ahead 5. Discussion
COLLABORATIVE SEARCH INTERFACES
USER-DRIVEN COLLABORATIVE INTERFACES
• Coagmento [Shah and Gonz´alez-Ib´a˜nez, 2011a]
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40. Collaboration and Information Retrieval 2. Collaborative IR techniques and models 3. Evaluation 4. Challenges ahead 5. Discussion
COLLABORATIVE SEARCH INTERFACES
USER-DRIVEN COLLABORATIVE INTERFACES
• CoFox [Rodriguez Perez et al., 2011]
Others interfaces: [Erickson, 2010] [Vivian and Dinet, 2008]... 21 / 111
41. Collaboration and Information Retrieval 2. Collaborative IR techniques and models 3. Evaluation 4. Challenges ahead 5. Discussion
COLLABORATIVE SEARCH INTERFACES
USER-DRIVEN COLLABORATIVE INTERFACES
• TeamSearch [Morris et al., 2006]
Others interfaces: Fischlar-DiamondTouch [Smeaton et al., 2006] - WeSearch
[Morris et al., 2010]... 22 / 111
42. Collaboration and Information Retrieval 2. Collaborative IR techniques and models 3. Evaluation 4. Challenges ahead 5. Discussion
COLLABORATIVE SEARCH INTERFACES
SYSTEM-MEDIATED COLLABORATIVE INTERFACES
• Cerchiamo [Golovchinsky et al., 2008]
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43. Collaboration and Information Retrieval 2. Collaborative IR techniques and models 3. Evaluation 4. Challenges ahead 5. Discussion
COLLABORATIVE SEARCH INTERFACES
SYSTEM-MEDIATED COLLABORATIVE INTERFACES
• Querium [Diriye and Golovchinsky, 2012]
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44. 1. Collaboration and Information Retrieval Collaborative IR techniques and models 3. Evaluation 4. Challenges ahead 5. Discussion
PLAN
1. Collaboration and Information Retrieval
2. Collaborative IR techniques and models
Challenges and issues
Understanding Collaborative IR
Overview
System-mediated CIR models
User-Driven System-mediated CIR models
Roadmap
3. Evaluation
4. Challenges ahead
5. Discussion
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45. 1. Collaboration and Information Retrieval Collaborative IR techniques and models 3. Evaluation 4. Challenges ahead 5. Discussion
CHALLENGES
• Conceptual models of IR:
Static IR: system-based IR, does not learn from users
eg. VSM [Salton, 1971], BM25 [Robertson et al., 1995] LM [Ponte and Croft, 1998], PageRank
and Hits [Brin and Page, 1998]
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46. 1. Collaboration and Information Retrieval Collaborative IR techniques and models 3. Evaluation 4. Challenges ahead 5. Discussion
CHALLENGES
• Conceptual models of IR:
Static IR: system-based IR, does not learn from users
eg. VSM [Salton, 1971], BM25 [Robertson et al., 1995] LM [Ponte and Croft, 1998], PageRank
and Hits [Brin and Page, 1998]
Interactive IR: exploiting feedback from users
eg. Rocchio [Rocchio, 1971], Relevance-based LM [Lavrenko and Croft, 2001]
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47. 1. Collaboration and Information Retrieval Collaborative IR techniques and models 3. Evaluation 4. Challenges ahead 5. Discussion
CHALLENGES
• Conceptual models of IR:
Static IR: system-based IR, does not learn from users
eg. VSM [Salton, 1971], BM25 [Robertson et al., 1995] LM [Ponte and Croft, 1998], PageRank
and Hits [Brin and Page, 1998]
Interactive IR: exploiting feedback from users
eg. Rocchio [Rocchio, 1971], Relevance-based LM [Lavrenko and Croft, 2001]
Dynamic IR: learning dynamically from past user-system interactions and predicts future
eg. iPRP [Fuhr, 2008], interactive exploratory search [Jin et al., 2013]
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48. 1. Collaboration and Information Retrieval Collaborative IR techniques and models 3. Evaluation 4. Challenges ahead 5. Discussion
CHALLENGES
• Conceptual models of IR:
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49. 1. Collaboration and Information Retrieval Collaborative IR techniques and models 3. Evaluation 4. Challenges ahead 5. Discussion
CHALLENGES
• Conceptual models of IR:
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50. 1. Collaboration and Information Retrieval Collaborative IR techniques and models 3. Evaluation 4. Challenges ahead 5. Discussion
CHALLENGES
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51. 1. Collaboration and Information Retrieval Collaborative IR techniques and models 3. Evaluation 4. Challenges ahead 5. Discussion
CHALLENGES
1 Learning from user and user-user past interactions
2 Adaptation to multi-faceted and multi-user contexts: skills, expertise, role, etc.
3 Aggregating relevant information nuggets
4 Supporting synchronous vs. asynchronous coordination
5 Modeling collaboration paradigms: division of labor, sharing of knowledge
6 Optimizing the search cost: balance in work (search) and group benefit (task outcome)
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52. 1. Collaboration and Information Retrieval Collaborative IR techniques and models 3. Evaluation 4. Challenges ahead 5. Discussion
EMPIRICAL UNDERSTANDING OF CIR
Objectives
1 Investigating user behavior and search patterns
Search processes [Shah and Gonz´alez-Ib´a˜nez, 2010, Yue et al., 2014]
Search tactics and practices [Hansen and J¨arvelin, 2005, Morris, 2008, Morris, 2013,
Amershi and Morris, 2008, Tao and Tombros, 2013, Capra, 2013]
Role assignement [Imazu et al., 2011, Tamine and Soulier, 2015]
2 Studying the impact of collaborative search settings on performance
Impact of collaboration on search performance
[Shah and Gonz´alez-Ib´a˜nez, 2011b, Gonz´alez-Ib´a˜nez et al., 2013]
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53. 1. Collaboration and Information Retrieval Collaborative IR techniques and models 3. Evaluation 4. Challenges ahead 5. Discussion
EMPIRICAL UNDERSTANDING OF CIR
GOAL: EXPLORING COLLABORATIVE SEARCH PROCESSES
• Study objective: Testing the feasibility of the Kuhlthau’s model of the information
seking process in a collaborative information seeking situation
[Shah and Gonz´alez-Ib´a˜nez, 2010]
Stage Feeling Thoughts Actions
(Affective) (Cognitive)
Initiation Uncertainty General/Vague Actions
Selection Optimism
Exploration Confusion, Frustration, Doubt Seeking relevant informa-
tion
Formulation Clarity Narrowed, Clearer
Collection Sense of direction,
Confidence
Increased interest Seeking relevant or focused
information
Presentation Relief, Satisfaction or disap-
pointment
Clearer or focused
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54. 1. Collaboration and Information Retrieval Collaborative IR techniques and models 3. Evaluation 4. Challenges ahead 5. Discussion
EMPIRICAL UNDERSTANDING OF CIR
GOAL: EXPLORING COLLABORATIVE SEARCH PROCESSES
• Study objective: Testing the feasibility of the Kuhlthau’s model in collaborative
information seeking situations [Shah and Gonz´alez-Ib´a˜nez, 2010]
Participants: 42 dyads, students or university employees who already did a collaborative work
together
System: Coagmento 1
Sessions: two sessions (S1, S2) running in 7 main phases: (1) tutorial on system, (2)
demographic questionnaire, (3) task description, (4) timely-bounded task achievement, (5)
post-questionnaire, (6) report compilation, (7) questionnaire and interview
Tasks: simulated work tasks.
eg. Task 1: Economic recession
”A leading newspaper has hired your team to create a comprehensive report on the causes and consequences
of the current economic recession in the US. As a part of your contract, you are required to collect all the
relevant information from any available online sources that you can find. ... Your report on this topic should
address the following issues: reasons behind this recession, effects on some major areas, such as health-care,
home ownership, and financial sector (stock market), unemployment statistics over a period of time, proposal
execution, and effects of the economy simulation plan, and people’s opinions and reactions on economy’s
downfall”
1
http://www.coagmento.org/
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55. 1. Collaboration and Information Retrieval Collaborative IR techniques and models 3. Evaluation 4. Challenges ahead 5. Discussion
EMPIRICAL UNDERSTANDING OF CIR
GOAL: EXPLORING COLLABORATIVE SEARCH PROCESSES
• (Main) Study results:
The Kuhlthau’s model stages map collaborative tasks
• Initiation: number of chat
messages at the stage and
between stages
• Selection: number of chat
messages discussing the
strategy
• Exploration: number of
search queries
• Formulation: number of
visited webpages
• Collection: number of
collected webpages
• Presentation: number of
moving actions for
organizing collected
snippets
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56. 1. Collaboration and Information Retrieval Collaborative IR techniques and models 3. Evaluation 4. Challenges ahead 5. Discussion
EMPIRICAL UNDERSTANDING OF CIR
GOAL: EXPLORING COLLABORATIVE SEARCH PROCESSES
• (Main) Study results:
The Kuhlthau’s model stages map collaborative tasks
• Initiation: number of chat
messages at the stage and
between stages
• Selection: number of chat
messages discussing the
strategy
• Exploration: number of
search queries
• Formulation: number of
visited webpages
• Collection: number of
collected webpages
• Presentation: number of
moving actions for
organizing collected
snippets
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57. 1. Collaboration and Information Retrieval Collaborative IR techniques and models 3. Evaluation 4. Challenges ahead 5. Discussion
EMPIRICAL UNDERSTANDING OF CIR
GOAL: EXPLORING SEARCH TACTICS AND PRACTICES
• Study objective: Analyzing query (re)formulations and related term sources based on
participants’ actions [Yue et al., 2014]
Participants: 20 dyads, students who already knew each other in advance
System: Collabsearch
Session: one session running in running in 7 main phases: (1) tutorial on system, (2)
demographic questionnaire, (3) task description, (4) timely-bounded task achievement, (5)
post-questionnaire, (6) report compilation, (7) questionnaire and interview
Tasks: (T1) academic literature search, (T2) travel planning
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58. 1. Collaboration and Information Retrieval Collaborative IR techniques and models 3. Evaluation 4. Challenges ahead 5. Discussion
EMPIRICAL UNDERSTANDING OF CIR
GOAL: EXPLORING SEARCH TACTICS AND PRACTICES
• (Main) Study results:
Individual action-based query reformulation (V, S, Q):
No (significant) new findings
Collaborative action-based query reformulation (SP, QP, C):
Influence of communication (C) is task-dependent.
Influence of collaborators’ queries (QP) is significantly higher than previous own queries (Q).
Less influence of collaborators’ workspace (SP) than own workspace (S).
• V: percentage of queries for which
participants viewed results, one
term originated from at least one
page
• S: percentage of queries for which
participants saved results, one term
originated from at least one page
• Q: percentage of queries with at
least one overlapping term with
previous queries
• SP: percentage of queries for which
at least one term originated from
collaborators’ workspace
• QP: percentage of queries for which
at least one term originated from
collaborators’ previous queries
• C: percentage of queries for which
at least one term originated from
collaborators’ communication
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59. 1. Collaboration and Information Retrieval Collaborative IR techniques and models 3. Evaluation 4. Challenges ahead 5. Discussion
EMPIRICAL UNDERSTANDING OF CIR
GOAL: STUDYING ROLE ASSIGNMENT
• Study objective: Understanding differences in users’ behavior in role-oriented and
non-role- oriented collaborative search sessions
Participants: 75 dyads, students who already knew each other
Settings: 25 dyads without roles, 50 dyads with roles (25 PM roles, 25 GS roles)
System: open-source Coagmento plugin
Session: one session running in 7 main phases: (1) tutorial on system, (2) demographic
questionnaire, (3) task description, (4) timely-bounded task achievement, (5)
post-questionnaire, (6) report compilation, (7) questionnaire and interview
Tasks: Three (3) exploratory search tasks, topics from Interactive TREC track2
Tamine, L. and Soulier, L. (2015). Understanding the impact of the
role factor in collaborative information retrieval. In Proceedings of
the ACM International on Conference on Information and
Knowledge Management, CIKM 15, pages 4352.
2
http://trec.nist.gov/data/t8i/t8i.html
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60. 1. Collaboration and Information Retrieval Collaborative IR techniques and models 3. Evaluation 4. Challenges ahead 5. Discussion
EMPIRICAL UNDERSTANDING OF CIR
GOAL: STUDYING ROLE ASSIGNMENT
• (Main) Study results
Users with assigned roles significantly behave differently than users with roles
Mean(s.d.)
npq dt nf qn ql qo nbm
W/Role
GS
Group 1.71(1.06) 9.99(3.37) 58.52(27.13) 65.91(31.54) 4.64(1.11) 0.44(0.18) 20(14.50)
IGDiffp
-0.52 -3.47*** 1.30*** 2.09*** 1.16*** 0.14*** 2.23***
PM
Group 1.88(1.53) 10.47(3.11) 56.31(27.95) 56.31(27.95) 2.79(0.70) 0.39(0.08) 15(12.88)
IGDiffp
0.24*** 1.45*** -2.42*** -1.69*** 0.06*** 0-0.23*** 0.05***
W/oRole
Group 2.09(1.01) 13.16(3.92) 24.13(12.81) 43.58(16.28) 3.67(0.67) 0.45(0.10) 19(11.34)
p-value/GS *** *** *** *** *** ***
p-value/PM *** *** *** *** *** *** *
W/Role
vs.
W/oRole
ANOVA p-val.
** *** ** *
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61. 1. Collaboration and Information Retrieval Collaborative IR techniques and models 3. Evaluation 4. Challenges ahead 5. Discussion
EMPIRICAL UNDERSTANDING OF CIR
GOAL: STUDYING ROLE ASSIGNMENT
• (Main) Study results
Early and high level of coordination of participants without role
Role drift for participants with PM role
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62. 1. Collaboration and Information Retrieval Collaborative IR techniques and models 3. Evaluation 4. Challenges ahead 5. Discussion
EMPIRICAL UNDERSTANDING OF CIR
GOAL: EVALUATING THE IMPACT OF COLLABORATION ON SEARCH PERFORMANCE
• Study objective: Evaluating the synergic effect of collaboration in information seeking
[Shah and Gonz´alez-Ib´a˜nez, 2011b]
Participants: 70 participants, 10 as single users, 30 as dyads
Settings: C1 (single users), C2 (artificial formed teams), C3 (co-located teams, different
computers), C4 (co-located teams, same computer), C5 remotely located teams
System: Coagmento
Session: one session running in running in 7 main phases: (1) tutorial on system, (2)
demographic questionnaire, (3) task description, (4) timely-bounded task achievement, (5)
post-questionnaire, (6) report compilation, (7) questionnaire and interview
Tasks: One exploratory search task, topic ”gulf oil spill”
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63. 1. Collaboration and Information Retrieval Collaborative IR techniques and models 3. Evaluation 4. Challenges ahead 5. Discussion
EMPIRICAL UNDERSTANDING OF CIR
GOAL: EVALUATING THE IMPACT OF COLLABORATION ON SEARCH PERFORMANCE
• (Main) Study results
Value of remote collaboration when the task has clear independent components
Remotely located teams able to leverage real interactions leading to synergic collaboration
Cognitive load in a collaborative setting not significantly higher than in an individual one
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64. 1. Collaboration and Information Retrieval Collaborative IR techniques and models 3. Evaluation 4. Challenges ahead 5. Discussion
EMPIRICAL UNDERSTANDING OF CIR
Lessons learned
• Small-group (critical mass) collaborative search is a common practice despite the lack of
specific tools
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65. 1. Collaboration and Information Retrieval Collaborative IR techniques and models 3. Evaluation 4. Challenges ahead 5. Discussion
EMPIRICAL UNDERSTANDING OF CIR
Lessons learned
• Small-group (critical mass) collaborative search is a common practice despite the lack of
specific tools
• The whole is greater than the sum of all
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66. 1. Collaboration and Information Retrieval Collaborative IR techniques and models 3. Evaluation 4. Challenges ahead 5. Discussion
EMPIRICAL UNDERSTANDING OF CIR
Lessons learned
• Small-group (critical mass) collaborative search is a common practice despite the lack of
specific tools
• The whole is greater than the sum of all
• Collaborative search behavior differs from individual search behavior while some
phases of theoretical models of individual search are still valid for collaborative search
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67. 1. Collaboration and Information Retrieval Collaborative IR techniques and models 3. Evaluation 4. Challenges ahead 5. Discussion
EMPIRICAL UNDERSTANDING OF CIR
Lessons learned
• Small-group (critical mass) collaborative search is a common practice despite the lack of
specific tools
• The whole is greater than the sum of all
• Collaborative search behavior differs from individual search behavior while some
phases of theoretical models of individual search are still valid for collaborative search
• Algorithmic mediation lowers the coordination cost
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68. 1. Collaboration and Information Retrieval Collaborative IR techniques and models 3. Evaluation 4. Challenges ahead 5. Discussion
EMPIRICAL UNDERSTANDING OF CIR
Lessons learned
• Small-group (critical mass) collaborative search is a common practice despite the lack of
specific tools
• The whole is greater than the sum of all
• Collaborative search behavior differs from individual search behavior while some
phases of theoretical models of individual search are still valid for collaborative search
• Algorithmic mediation lowers the coordination cost
• Roles structure the collaboration but do not guarantee performance improvement in
comparison to no roles
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69. 1. Collaboration and Information Retrieval Collaborative IR techniques and models 3. Evaluation 4. Challenges ahead 5. Discussion
EMPIRICAL UNDERSTANDING OF CIR
Lessons learned
• Small-group (critical mass) collaborative search is a common practice despite the lack of
specific tools
• The whole is greater than the sum of all
• Collaborative search behavior differs from individual search behavior while some
phases of theoretical models of individual search are still valid for collaborative search
• Algorithmic mediation lowers the coordination cost
• Roles structure the collaboration but do not guarantee performance improvement in
comparison to no roles
Design implications: revisit IR models and techniques
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70. 1. Collaboration and Information Retrieval Collaborative IR techniques and models 3. Evaluation 4. Challenges ahead 5. Discussion
EMPIRICAL UNDERSTANDING OF CIR
Lessons learned
• Small-group (critical mass) collaborative search is a common practice despite the lack of
specific tools
• The whole is greater than the sum of all
• Collaborative search behavior differs from individual search behavior while some
phases of theoretical models of individual search are still valid for collaborative search
• Algorithmic mediation lowers the coordination cost
• Roles structure the collaboration but do not guarantee performance improvement in
comparison to no roles
Design implications: revisit IR models and techniques
• Back to the axiomatic relevance hypothesis (Fang et al. 2011)
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71. 1. Collaboration and Information Retrieval Collaborative IR techniques and models 3. Evaluation 4. Challenges ahead 5. Discussion
EMPIRICAL UNDERSTANDING OF CIR
Lessons learned
• Small-group (critical mass) collaborative search is a common practice despite the lack of
specific tools
• The whole is greater than the sum of all
• Collaborative search behavior differs from individual search behavior while some
phases of theoretical models of individual search are still valid for collaborative search
• Algorithmic mediation lowers the coordination cost
• Roles structure the collaboration but do not guarantee performance improvement in
comparison to no roles
Design implications: revisit IR models and techniques
• Back to the axiomatic relevance hypothesis (Fang et al. 2011)
• Role as a novel variable in the IR models ?
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72. 1. Collaboration and Information Retrieval Collaborative IR techniques and models 3. Evaluation 4. Challenges ahead 5. Discussion
EMPIRICAL UNDERSTANDING OF CIR
Lessons learned
• Small-group (critical mass) collaborative search is a common practice despite the lack of
specific tools
• The whole is greater than the sum of all
• Collaborative search behavior differs from individual search behavior while some
phases of theoretical models of individual search are still valid for collaborative search
• Algorithmic mediation lowers the coordination cost
• Roles structure the collaboration but do not guarantee performance improvement in
comparison to no roles
Design implications: revisit IR models and techniques
• Back to the axiomatic relevance hypothesis (Fang et al. 2011)
• Role as a novel variable in the IR models ?
• Learning to rank from user-system, user-user interactions within multi-session search
tasks?
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73. 1. Collaboration and Information Retrieval Collaborative IR techniques and models 3. Evaluation 4. Challenges ahead 5. Discussion
OVERVIEW OF IR MODELS AND TECHNIQUES
DESIGNING COLLABORATIVE IR MODELS: A YOUNG RESEARCH AREA
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74. 1. Collaboration and Information Retrieval Collaborative IR techniques and models 3. Evaluation 4. Challenges ahead 5. Discussion
OVERVIEW OF IR MODELS AND TECHNIQUES
DESIGNING COLLABORATIVE IR MODELS: A YOUNG RESEARCH AREA
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75. 1. Collaboration and Information Retrieval Collaborative IR techniques and models 3. Evaluation 4. Challenges ahead 5. Discussion
OVERVIEW OF IR MODELS AND TECHNIQUES
Collaborative IR models are based on algorithmic mediation:
Systems re-use users’ search activity data to mediate the search
• Data?
Click-through data, queries, viewed results, result rankings, ...
User-user communication
• Mediation?
Rooting/suggesting/enhance the queries
Building personalized document rankings
Automatically set-up division of labor
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76. 1. Collaboration and Information Retrieval Collaborative IR techniques and models 3. Evaluation 4. Challenges ahead 5. Discussion
OVERVIEW OF IR MODELS AND TECHNIQUES
Collaborative IR models are based on algorithmic mediation:
Systems re-use users’ search activity data to mediate the search
• Data?
Click-through data, queries, viewed results, result rankings, ...
User-user communication
• Mediation?
Rooting/suggesting/enhance the queries
Building personalized document rankings
Automatically set-up division of labor
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77. 1. Collaboration and Information Retrieval Collaborative IR techniques and models 3. Evaluation 4. Challenges ahead 5. Discussion
OVERVIEW OF IR MODELS AND TECHNIQUES
Notations
Notation Description
d Document
q Query
uj User j
g Collaborative group
ti term i
RSV(d, q) Relevance Status Value given (d,q)
N Document collection size
ni Number of documents in the collection in which term ti occurs
R Number of relevant documents in the collection
Ruj
Number of relevant documents in the collection for user uj
r
uj
i Number of relevant documents of user uj in which term ti occurs
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78. 1. Collaboration and Information Retrieval Collaborative IR techniques and models 3. Evaluation 4. Challenges ahead 5. Discussion
SYSTEM-MEDIATED CIR MODELS
USER GROUP-BASED MEDIATION
• Enhancing collaborative search with users’ context
[Morris et al., 2008, Foley and Smeaton, 2009a, Han et al., 2016]
Division of labor: dividing the work by non-overlapping browsing
Sharing of knowledge: exploiting personal relevance judgments, user’s authority
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79. 1. Collaboration and Information Retrieval Collaborative IR techniques and models 3. Evaluation 4. Challenges ahead 5. Discussion
SYSTEM-MEDIATED CIR MODELS
USER/GROUP-BASED MEDIATION: GROUPIZATION, SMART SPLITTING, GROUP-HIGHLIGHTING [MORRIS ET AL., 2008]
• Hypothesis setting: one or a few synchronous search query(ies)
• 3 approaches
Smart splitting: splitting top ranked web results using a round-robin technique,
personalized-splitting of remaining results (document ranking level)
Groupization: reusing individual personalization techniques towards groups (document ranking
level)
Hit Highlighting: highlighting user’s keywords (document browsing level)
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80. 1. Collaboration and Information Retrieval Collaborative IR techniques and models 3. Evaluation 4. Challenges ahead 5. Discussion
SYSTEM-MEDIATED CIR MODELS
USER/GROUP-BASED MEDIATION: SMART-SPLITTING [MORRIS ET AL., 2008]
Personalizing the document ranking: use the revisited BM25 weighting scheme
[Teevan et al., 2005]
RSV(d, q, uj) =
ti∈d∩q
wBM25(ti, uj) (1)
wB2M5(ti, uj) = log
(ri + 0.5)(N − ni − Ruj + r
uj
i + 0.5)
(ni − r
uj
i + 0.5)(Ruj − r
uj
i + 0.5
(2)
N = (N + Ruj ) (3)
ni = ni + r
uj
i (4)
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81. 1. Collaboration and Information Retrieval Collaborative IR techniques and models 3. Evaluation 4. Challenges ahead 5. Discussion
SYSTEM-MEDIATED CIR MODELS
USER/GROUP-BASED MEDIATION: SMART-SPLITTING [MORRIS ET AL., 2008]
Example
Smart-splitting according to personalized scores.
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82. 1. Collaboration and Information Retrieval Collaborative IR techniques and models 3. Evaluation 4. Challenges ahead 5. Discussion
SYSTEM-MEDIATED CIR MODELS
USER/GROUP-BASED MEDIATION: COLLABORATIVE RELEVANCE FEEDBACK [FOLEY ET AL., 2008, FOLEY AND SMEATON, 2009B]
• Hypothesis setting: multiple independent synchronous search queries
• Collaborative relevance feedback: sharing collaborator’s explicit relevance judgments
Aggregate the partial user relevance scores
Compute the user’s authority weighting
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83. 1. Collaboration and Information Retrieval Collaborative IR techniques and models 3. Evaluation 4. Challenges ahead 5. Discussion
SYSTEM-MEDIATED CIR MODELS
USER/GROUP-BASED MEDIATION: COLLABORATIVE RELEVANCE FEEDBACK [FOLEY ET AL., 2008, FOLEY AND SMEATON, 2009B]
• A: Combining inputs of the RF process
puwo(ti) =
U−1
u=0
ruiwBM25(ti) (5)
wBM25(ti) = log
( U−1
u=0 αu
ru
i
Ru
)(1 − U−1
u=0 αu
ni − rui
N − Ru
)
( U−1
u=0 αu
ni − rui
N − Ru
)(1 − U−1
u=0 αu
rui
Ru
)
(6)
U−1
u=0
αu = 1 (7)
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84. 1. Collaboration and Information Retrieval Collaborative IR techniques and models 3. Evaluation 4. Challenges ahead 5. Discussion
SYSTEM-MEDIATED CIR MODELS
USER/GROUP-BASED MEDIATION: COLLABORATIVE RELEVANCE FEEDBACK [FOLEY ET AL., 2008, FOLEY AND SMEATON, 2009B]
• B: Combining outputs of the RF process
crwo(ti) =
U−1
u=0
αuwBM25(ti, u) (8)
wBM25(ti, u) = log
(
ru
i
Ru
)(1 −
ni − rui
N − Ru
)
(
ni − rui
N − Ru
)(1 −
rui
Ru
)
(9)
• C: Combining outputs of the ranking process
RSV(d, q) =
U−1
u=0
αuRSV(d, q, u) (10)
RSV(d, q, u) =
ti∈d∩q
wBM25(ti, u) (11)
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85. 1. Collaboration and Information Retrieval Collaborative IR techniques and models 3. Evaluation 4. Challenges ahead 5. Discussion
SYSTEM-MEDIATED CIR MODELS
USER/GROUP-BASED MEDIATION: CONTEXT-BASED COLLABORATIVE SEARCH [HAN ET AL., 2016]
• Exploit a 3-dimensional context:
Individual search history HQU: queries, results, bookmarks etc.)
Collaborative group HCL: collaborators’ search history (queries, results, bookmarks etc.)
Collaboration HCH: collaboration behavior chat (communication)
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86. 1. Collaboration and Information Retrieval Collaborative IR techniques and models 3. Evaluation 4. Challenges ahead 5. Discussion
SYSTEM-MEDIATED CIR MODELS
USER/GROUP-BASED MEDIATION: CONTEXT-BASED COLLABORATIVE SEARCH [HAN ET AL., 2016]
1 Building a document ranking RSV(q, d) and generating Rank(d)
2 Building the document language model θd
3 Building the context language model θHx
p(ti|Hx) =
1
K
K
k=1
p(ti|Xk) (12)
p(ti|Xk) =
nk
Xk
(13)
4 Computing the KL-divergence between θHx and θd
D(θd, θHx ) = −
ti
p(ti|θd) log p(ti|Hx) (14)
5 Learning to rank using pairwise features (Rank(d), D(θd, θHx))
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87. 1. Collaboration and Information Retrieval Collaborative IR techniques and models 3. Evaluation 4. Challenges ahead 5. Discussion
SYSTEM-MEDIATED CIR MODELS
ROLE-BASED MEDIATION
Enhancing collaborative search with user’s role
[Pickens et al., 2008, Shah et al., 2010, Soulier et al., 2014b]
• Division of labour: dividing the work based on users’ role peculiarities
• Sharing of knowledge: splitting the search results
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88. 1. Collaboration and Information Retrieval Collaborative IR techniques and models 3. Evaluation 4. Challenges ahead 5. Discussion
SYSTEM-MEDIATED CIR MODELS
ROLE-BASED MEDIATION: PROSPECTOR AND MINER [PICKENS ET AL., 2008]
• Prospector/Miner as functional roles supported by algorithms:
Prospector: ”..opens new fields for exploration into a data collection..”.
→ Draws ideas from algorithmically suggested query terms
Miner: ”..ensures that rich veins of information are explored...”.
→ Refines the search by judging highly ranked (unseen) documents
• Collaborative system architecture:
Algorithmic layer: functions
combining users’ search activities to
produce fitted outcomes to roles
(queries, document rankings).
Regulator layer: captures inputs
(search activities), calls the
appropriate functions of the
algorithmic layer, roots the outputs
of the algorithmic layer to the
appropriate role (user).
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89. 1. Collaboration and Information Retrieval Collaborative IR techniques and models 3. Evaluation 4. Challenges ahead 5. Discussion
SYSTEM-MEDIATED CIR MODELS
ROLE-BASED MEDIATION: PROSPECTOR AND MINER [PICKENS ET AL., 2008]
• Prospector function: The highly-relevant terms are suggested based on:
Score(ti) =
Lq∈L
wr(Lq)wf (Lq)rlf(ti; Lq) (15)
rlf(ti; Lq): number of documents in Lq in which ti occurs.
• Miner function: The unseen documents are queued according to
RSV(q, d) =
Lq∈L
wr(Lk)wf (Lq)borda(d; Lq) (16)
wr(Lq) =
|seen ∈ Lq|
|seen ∈ Lq|
(17)
wf (Lq) =
|rel ∈ Lq|
|seen ∈ Lq|
(18)
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90. 1. Collaboration and Information Retrieval Collaborative IR techniques and models 3. Evaluation 4. Challenges ahead 5. Discussion
SYSTEM-MEDIATED CIR MODELS
ROLE-BASED MEDIATION: GATHERER AND SURVEYOR [SHAH ET AL., 2010]
• Gatherer/Surveyor as functional roles supported by algorithms:
Gatherer: ”..scan results of joint search activity to discover most immediately relevant documents..”.
Surveyor: ”..browse a wider diversity of information to get a better understanding of the collection
being searched...”.
• Main functions:
Merging: merging (eg. CombSum) the
documents rankings of collaborators
Splitting: rooting the appropriate
documents according to roles (eg.
k-means clustering). High precision for
the Gatherer, high diversity for the
Surveyor
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91. 1. Collaboration and Information Retrieval Collaborative IR techniques and models 3. Evaluation 4. Challenges ahead 5. Discussion
SYSTEM-MEDIATED CIR MODELS
ROLE-BASED MEDIATION: DOMAIN EXPERT AND DOMAIN NOVICE
Domain expert/Domain novice as knowledge-based roles supported by algorithms:
• Domain expert: ”..represent problems at deep structural levels and are generally interested in
discovering new associations among different aspects of items, or in delineating the advances in
a research focus surrounding the query topic..”.
• Domain novice: ”..represent problems in terms of surface or superficial aspects and are
generally interested in enhancing their learning about the general query topic..”.
Soulier, L., Tamine, L., and Bahsoun, W. (2014b). On domain
expertise-based roles in collaborative information retrieval.
Information Processing & Management (IP&M), 50(5):752774.
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92. 1. Collaboration and Information Retrieval Collaborative IR techniques and models 3. Evaluation 4. Challenges ahead 5. Discussion
SYSTEM-MEDIATED CIR MODELS
ROLE-BASED MEDIATION: DOMAIN EXPERT AND DOMAIN NOVICE [SOULIER ET AL., IP&M 2014B]
A two step algorithm:
1 Role-based document relevance scoring
Pk
(d|uj, q) ∝ Pk(uj|d) · Pk(d|q) (19)
P(q|θd) ∝ (ti,wiq)∈q[λP(ti|θd) + (1 − λ)P(ti|θC)]wiq (20)
Pk
(uj|d) ∝ P(π(uj)k|θd)
∝ (ti,wk
ij
)∈π(uj)k [λk
dj
P(ti|θd) + (1 − λk
dj
)P(ti|θC)]
wk
ij (21)
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93. 1. Collaboration and Information Retrieval Collaborative IR techniques and models 3. Evaluation 4. Challenges ahead 5. Discussion
SYSTEM-MEDIATED CIR MODELS
ROLE-BASED MEDIATION: DOMAIN EXPERT AND DOMAIN NOVICE [SOULIER ET AL., IP&M 2014B]
A two step algorithm:
1 Role-based document relevance scoring : parameter smoothing using evidence from
novelty and specificity
λk
dj =
Nov(d, D(uj)k) · Spec(d)β
maxd ∈D Nov(d, D(uj)k) · Spec(d )β
(22)
with β
1 if uj is an expert
−1 if uj is a novice
Novelty
Nov(d, D(uj)
k
) = mind ∈D(uj)k d(d, d ) (23)
Specificity
Spec(d) = avgti∈dspec(ti) = avgti∈d(
−log(
fdti
N )
α
) (24)
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94. 1. Collaboration and Information Retrieval Collaborative IR techniques and models 3. Evaluation 4. Challenges ahead 5. Discussion
SYSTEM-MEDIATED CIR MODELS
ROLE-BASED MEDIATION: DOMAIN EXPERT AND DOMAIN NOVICE [SOULIER ET AL., IP&M 2014B]
A two step algorithm:
1 Document allocation to collaborators
Classification-based on the Expectation Maximization algorithm (EM)
E-step: Document probability of belonging to collaborator’s class
P(Rj = 1|x
k
dj) =
αk
j · φk
j (xk
dj)
αk
j
· φk
j
(xk
dj
) + (1 − αk
j
) · ψk
j
(xk
dj
)
(25)
M-step : Parameter updating and likelihood estimation
Document allocation to collaborators by comparison of document ranks within collaborators’
lists
r
k
jj (d, δ
k
j , δ
k
j ) =
1 if rank(d, δk
j ) < rank(d, δk
j
)
0 otherwise
(26)
Division of labor: displaying distinct document lists between collaborators
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95. 1. Collaboration and Information Retrieval Collaborative IR techniques and models 3. Evaluation 4. Challenges ahead 5. Discussion
SYSTEM-MEDIATED CIR MODELS
ROLE-BASED MEDIATION: DOMAIN EXPERT AND DOMAIN NOVICE [SOULIER ET AL., IP&M 2014B]
Example
Applying the Expert/Novice CIR model
Let’s consider:
• A collaborative search session with two users u1 (expert) and u2 (novice).
• A shared information need I modeled through a query q.
• A collection of 10 documents and their associated relevance score with respect to the
shared information need I.
t1 t2 t3 t4
q 1 0 1 0
d1 2 3 1 1
d2 0 0 5 3
d3 2 1 7 6
d4 4 1 0 0
d5 2 0 0 0
d6 3 0 0 0
d7 7 1 1 1
d8 3 3 3 3
d9 1 4 5 0
d10 0 0 4 0
Weighting vectors of documents and query:
q = (0.5, 0, 0.5, 0) ;
d1 = (0.29, 0.43, 0.14, 0.14)
d2 = (0, 0, 0.63, 0.37)
d3 = (0.12, 0.06, 0.44, 0.28)
d4 = (0.8, 0.2, 0, 0)
d5 = (1, 0, 0, 0)
d6 = (0.3, 0, 0, 0.7)
d7 = (0.7, 0.1, 0.1, 0.1)
d8 = (0.25, 0.25, 0.25, 0.25)
d9 = (0.1, 0.4, 0.5, 0)
d10 = (0, 0, 1, 0).
Users profile: π(u1)0 = π(u2)0 = q
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96. 1. Collaboration and Information Retrieval Collaborative IR techniques and models 3. Evaluation 4. Challenges ahead 5. Discussion
SYSTEM-MEDIATED CIR MODELS
ROLE-BASED MEDIATION: DOMAIN EXPERT AND DOMAIN NOVICE [SOULIER ET AL., IP&M 2014B]
Example
Applying the Expert/Novice CIR model
RSV(q, d) rank(d) Spec(d)
d1 0.24 2 0.19
d2 0.02 7 0.23
d3 0.17 3 0.19
d4 0.03 6 0.15
d5 0.01 9 0.1
d6 0.02 8 0.1
d7 0.10 4 0.19
d8 0.31 1 0.19
d9 0.09 5 0.16
d10 0.01 10 0.15
• The document specificity is estimated as:
α = 3 (If a term has a collection frequency equals to 1, −log(1/10) = 2.30)
d1 =
−log( 8
10
)
3
−log( 6
10
)
3
−log( 7
10
)
3
−log( 5
10
)
3
4 = 0.19
d2 = 0.23, d3 = 0.19, d4 = 0.15, d5 = 0.01, d6 = 0.1, d7 = 0.19, d8 = 0.19, d9 = 0.16,
d10 = 0.15
• Iteration 0: Distributing top (6) documents to users: 3 most specific to the expert and
the 3 less specific to the novice.
Expert u1: l0
(u1, D0
ns) = {d8, d1, d3}
Novice u2: l0
(u2, D0
ns) = {d7, d9, d4}
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97. 1. Collaboration and Information Retrieval Collaborative IR techniques and models 3. Evaluation 4. Challenges ahead 5. Discussion
SYSTEM-MEDIATED CIR MODELS
ROLE-BASED MEDIATION: DOMAIN EXPERT AND DOMAIN NOVICE [SOULIER ET AL., IP&M 2014B]
Example
Applying the Expert/Novice CIR model
• Iteration 1. Let’s consider that user u2 selected document d4 (D(u1)1 = {d4, d5}).
Building the user’s profile.
π(u1)1
= (0.5, 0, 0.5, 0)
π(u2)1
= ( 0.5+0.8
2 , 0.2
2 , 0.5
2 , 0) = (0.65, 0.1, 0.25, 0).
Estimating the document relevance with respect to collaborators.
For user u1 : P1
(d1|u1) = P1
(d1|q) ∗ P1
(u1|d1) = 0.24 ∗ 0.22 = 0.05.
P1
(d1|q) = 0.24.
P1
(u1|d1) = (0.85 ∗ 2
7
+ 0.15 ∗ 24
84
)0.05
+ (0.85 ∗ 3
7
+ 0.15 ∗ 13
84
)0
+ (0.85 ∗ 1
7
+ 0.15 ∗ 26
84
)0.05
+
(0.85 ∗ 1
7
+ 0.15 ∗ 21
84
)0
= 0.22
λ1
11 = 1∗0.19
0.23
= 0.85 where 0.19 expresses the specificity of document d1 and 1 is the document
novelty score, and 0.23 the normalization score.
The normalized
document scores
for each
collaborators are
the following:
P1
(d|u1) P2
(d|u2)
d1 0.23 0.28
d2 0 0.03
d3 0.16 0.11
d5 0.01 0.01
d6 0.03 0.02
d7 0.12 0.14
d8 0.34 0.34
d9 0.10 0.06
d10 0.01 0.01 64 / 111
98. 1. Collaboration and Information Retrieval Collaborative IR techniques and models 3. Evaluation 4. Challenges ahead 5. Discussion
SYSTEM-MEDIATED CIR MODELS
ROLE-BASED MEDIATION: DOMAIN EXPERT AND DOMAIN NOVICE [SOULIER ET AL., IP&M 2014B]
Example
Applying the Expert/Novice CIR model
• Iteration 1. Let’s consider that user u2 selected document d4 (D(u1)1 = {d4, d5}).
Building the user’s profile.
π(u1)1
= (0.5, 0, 0.5, 0)
π(u2)1
= ( 0.5+0.8
2 , 0.2
2 , 0.5
2 , 0) = (0.65, 0.1, 0.25, 0).
Estimating the document relevance with respect to collaborators.
For user u1 : P1
(d1|u1) = P1
(d1|q) ∗ P1
(u1|d1) = 0.24 ∗ 0.22 = 0.05. P1
(d1|q) = 0.24 since that the
user’s profile has not evolve.
λ1
11 = 1∗0.19
0.23
= 0.85 where 0.19 expresses the specificity of document d1 and 1 is the document
novelty score, and 0.23 the normalization score.
P1
(u1|d1) = (0.85 ∗ 2
7
+ 0.15 ∗ 24
84
)0.05
+ (0.85 ∗ 3
7
+ 0.15 ∗ 13
84
)0
+ (0.85 ∗ 1
7
+ 0.15 ∗ 26
84
)0.05
+
(0.85 ∗ 1
7
+ 0.15 ∗ 21
84
)0
= 0.22
The normalized
document scores
for each
collaborators are
the following:
P1
(d|u1) P2
(d|u2)
d1 0.23 0.28
d2 0 0.03
d3 0.16 0.11
d5 0.01 0.01
d6 0.03 0.02
d7 0.12 0.14
d8 0.34 0.34
d9 0.10 0.06
d10 0.01 0.01 65 / 111
99. 1. Collaboration and Information Retrieval Collaborative IR techniques and models 3. Evaluation 4. Challenges ahead 5. Discussion
USER-DRIVEN SYSTEM-MEDIATED CIR MODELS
MINE USERS’ ROLES THEN PERSONALIZE THE SEARCH
Soulier, L., Shah, C., and Tamine, L. (2014a). User-driven
System-mediated Collaborative Information Retrieval. In
Proceedings of the Annual International SIGIR Conference on
Research and Development in Information Retrieval, SIGIR 14,
pages 485494. ACM.
66 / 111
100. 1. Collaboration and Information Retrieval Collaborative IR techniques and models 3. Evaluation 4. Challenges ahead 5. Discussion
USER-DRIVEN SYSTEM-MEDIATED CIR MODELS
MINE USERS’ ROLES THEN PERSONALIZE THE SEARCH [SOULIER ET AL., SIGIR 2014A]
• Identifying users’ search behavior differences: estimating significance of differences
using the Kolmogrov-Smirnov test
• Characterizing users’ role
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101. 1. Collaboration and Information Retrieval Collaborative IR techniques and models 3. Evaluation 4. Challenges ahead 5. Discussion
USER-DRIVEN SYSTEM-MEDIATED CIR MODELS
MINE USERS’ ROLES THEN PERSONALIZE THE SEARCH [SOULIER ET AL., SIGIR 2014A]
• Categorizing users’ roles Ru
argmin R1,2
||FR1,2 C
(tl)
u1,u2
|| (27)
subject to :
∀
(fj,fk)∈K
R1,2 FR1,2 (fj, fk) − C
(tl)
u1,u2
(fj, fk)) > −1
where defined as:
FR1,2 (fj, fk) C
(tl)
u1,u2
(fj, fk) =
FR1,2 (fj, fk) − C
(tl)
u1,u2
(fj, fk) if FR1,2 (fj, fk) ∈ {−1; 1}
0 otherwise
• Personalizing the search: [Pickens et al., 2008, Shah, 2011]...
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102. 1. Collaboration and Information Retrieval Collaborative IR techniques and models 3. Evaluation 4. Challenges ahead 5. Discussion
USER-DRIVEN SYSTEM-MEDIATED CIR MODELS
MINE USERS’ ROLES THEN PERSONALIZE THE SEARCH [SOULIER ET AL., SIGIR 2014A]
• User’s roles modeled through patterns
Intuition
Number of visited documents
Number of submitted queries
Negative correlation
Role pattern PR1,2
Search feature kernel KR1,2
Search feature-based correlation matrix FR1,2
F
R1,2
=
1 if positively correlated
−1 if negatively correlated
0 otherwise
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103. 1. Collaboration and Information Retrieval Collaborative IR techniques and models 3. Evaluation 4. Challenges ahead 5. Discussion
USER-DRIVEN SYSTEM-MEDIATED CIR MODELS
MINE USERS’ ROLES THEN PERSONALIZE THE SEARCH [SOULIER ET AL., SIGIR 2014A]
Example
Mining role of collaborators
A collaborative
search session
implies two users
u1 and u2 aiming
at identifying
information
dealing with
“global warming”.
We present search
actions of
collaborators for
the 5 first minutes
of the session.
u t actions additional information
u2 0 submitted query “global warming”
u1 1 submitted query “global warming”
u2 8 document d1: visited comment: “interesting”
u2 12 document d2: visited
u2 17 document d3: visited rated: 4/5
u2 19 document d4: visited
u1 30 submitted query “greenhouse effect”
u1 60 submitted query “global warming definition”
u1 63 document d20: visited rated: 3/5
u1 70 submitted query “global warming protection”
u1 75 document d21: visited
u2 100 document d5: visited rated: 5/5
u2 110 document d6: visited rated: 4/5
u2 120 document d7: visited
u1 130 submitted query “gas emission”
u1 132 document d22: visited rated: 4/5
u2 150 document d8: visited
u2 160 document d9: visited
u2 170 document d10: visited
u2 200 document d11: visited comment: “great”
u2 220 document d12: visited
u2 240 document d13: visited
u1 245 submitted query “global warming world protection”
u1 250 submitted query “causes temperature changes”
u1 298 submitted query “global warming world politics” 70 / 111
104. 1. Collaboration and Information Retrieval Collaborative IR techniques and models 3. Evaluation 4. Challenges ahead 5. Discussion
USER-DRIVEN SYSTEM-MEDIATED CIR MODELS
MINE USERS’ ROLES THEN PERSONALIZE THE SEARCH [SOULIER ET AL., SIGIR 2014A]
Example
Mining role of collaborators: matching with role patterns
• Role patterns
Roles of reader-querier
F
Rread,querier =
1 −1
−1 1
, K
Rread,querier = {(Nq, Np)}
Role : (S
(tl)
u1
, S
(tl)
u2
, Rread,querier) → {(reader, querier), (querier, reader)}
(S
(tl)
u1
, S
(tl)
u2
, Rread,querier) →
(reader, querier) if S
(tl)
u1
(tl, Np) > S
(tl)
u2
(tl, Np)
(querier, reader) otherwise
Role of judge-querier
F
Rjudge,querier =
1 −1
−1 1
, K
Rjudge,querier = {(Nq, Nc)}
Role : (S
(tl)
u1
, S
(tl)
u2
, Rjudge,querier → {(judge, querier), (querier, judge)}
(S
(tl)
u1
, S
(tl)
u2
, Rjudge,querier) →
(judge, querier) if S
(tl)
u1
(tl, Nc) > S
(tl)
u2
(tl, Nc)
(querier, judge) otherwise
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105. 1. Collaboration and Information Retrieval Collaborative IR techniques and models 3. Evaluation 4. Challenges ahead 5. Discussion
USER-DRIVEN SYSTEM-MEDIATED CIR MODELS
MINE USERS’ ROLES THEN PERSONALIZE THE SEARCH [SOULIER ET AL., SIGIR 2014A]
Example
Mining role of collaborators
• Track users’ behavior each 60 seconds
• F = {Nq, Nd, Nc, Nr}, respectively, number of queries, documents, comments, ratings.
• Users’ search behavior
S
(300)
u1
=
3 0 0 0
4 2 0 1
5 3 0 2
5 3 0 2
8 3 0 2
S
(300)
u2
=
1 4 1 1
1 7 1 3
1 10 1 3
1 13 2 3
1 13 2 3
• Collaborators’ search differences (matrix and Kolmogorov-Smirnov test)
∆
(300)
u1,u2
=
2 −4 −1 −1
3 −5 −1 −2
4 −7 −1 −1
4 −10 −2 −1
7 −10 −2 −1
- Number of queries : p
(tl)
u1,u2
(Nq) = 0.01348
- Number of pages : p
(tl)
u1,u2
(Nd) = 0.01348
- Number of comments : p
(tl)
u1,u2
(Nc) = 0.01348
- Number of ratings : p
(tl)
u1,u2
(Nr) = 0.08152
72 / 111
106. 1. Collaboration and Information Retrieval Collaborative IR techniques and models 3. Evaluation 4. Challenges ahead 5. Discussion
USER-DRIVEN SYSTEM-MEDIATED CIR MODELS
MINE USERS’ ROLES THEN PERSONALIZE THE SEARCH [SOULIER ET AL., SIGIR 2014A]
Example
Mining role of collaborators: matching with role patterns
• Collaborators’ search action complementarity: correlation matrix between search
differences
C
(300)
u1,u2
=
1 −0.8186713 −0.731925 0
−0.8186713 1 0.9211324 0
−0.731925 0.9211324 1 0
0 0 0 0
• Role mining: comparing the role pattern with the sub-matrix of collaborators’
behaviors
Role of reader-querier
||F
Rread,querier C
(300)
u1,u2
|| =
0 −1 − (−0.8186713)
−1 − (−0.8186713) 0
=
0 0.183287
0.183287 0
The Frobenius norm is equals to:
√
0.1832872 = 0.183287.
Role of judge-querier
||F
Rjudge,querier C
(300)
u1,u2
|| =
0 −1 − (−0.731925)
−1 − (−0.731925) 0
=
0 0.268174
0.268174 0
The Frobenius norm is equals to:
√
0.2681742 = 0.268174.
→ Collaborators acts as reader/querier with u1 labeled as querier and u2 as reader (highest
Np).
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107. 1. Collaboration and Information Retrieval Collaborative IR techniques and models 3. Evaluation 4. Challenges ahead 5. Discussion
OVERVIEW OF IR MODELS AND TECHNIQUES
[FoleyandSmeaton,2009a]
[Morrisetal.,2008]“smart-splitting”
[Morrisetal.,2008]“groupization”
[Pickensetal.,2008]
[Shahetal.,2010]
[Soulieretal.,IP&M2014b]
[Soulieretal.,SIGIR2014a]
Relevance
collective
individual
Evidence source
feedback
interest
expertise
behavior
role
Paradigm
division of labor
sharing of knowledge
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108. 1. Collaboration and Information Retrieval 2. Collaborative IR techniques and models Evaluation 4. Challenges ahead 5. Discussion
PLAN
1. Collaboration and Information Retrieval
2. Collaborative IR techniques and models
3. Evaluation
Evaluation challenges
Protocols
Protocols
Protocols
Metrics and ground truth
Baselines
Tools and datasets
4. Challenges ahead
5. Discussion
75 / 111
109. 1. Collaboration and Information Retrieval 2. Collaborative IR techniques and models Evaluation 4. Challenges ahead 5. Discussion
EVALUATION CHALLENGES
• Learning from user and user-user past
interactions
• Adaptation to multi-faceted and multi-user
contexts: skills, expertise, role, etc
• Aggregating relevant information nuggets
Evaluating the collective relevance
• Supporting synchronous vs. asynchronous
coordination
• Modeling collaboration paradigms: division of
labor, sharing of knowledge
• Optimizing search cost: balance in work (search)
and group benefit (task outcome)
Measuring the collaborative
effectiveness
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110. 1. Collaboration and Information Retrieval 2. Collaborative IR techniques and models Evaluation 4. Challenges ahead 5. Discussion
PROTOCOLS
CATEGORIES OF PROTOCOLS
• Standard evaluation frameworks
Without humans: batch-based evaluation (TREC, INEX, CLEF, ...)
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111. 1. Collaboration and Information Retrieval 2. Collaborative IR techniques and models Evaluation 4. Challenges ahead 5. Discussion
PROTOCOLS
CATEGORIES OF PROTOCOLS
• Standard evaluation frameworks
Without humans: batch-based evaluation (TREC, INEX, CLEF, ...)
With humans in the process (recommended)
c [Dumais, 2014]
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112. 1. Collaboration and Information Retrieval 2. Collaborative IR techniques and models Evaluation 4. Challenges ahead 5. Discussion
PROTOCOLS
CATEGORIES OF PROTOCOLS
• Standard evaluation frameworks
Without humans: batch-based evaluation (TREC, INEX, CLEF, ...)
With humans in the process (recommended)
• CIR-adapted evaluation frameworks
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113. 1. Collaboration and Information Retrieval 2. Collaborative IR techniques and models Evaluation 4. Challenges ahead 5. Discussion
PROTOCOLS
BATCH: COLLABORATION SIMULATION [MORRIS ET AL., 2008, SHAH ET AL., 2010]
• Real users formulating queries w.r.t. the shared information need
15 individual users asked to list queries they would associate to 10 TREC topics. Then, pairs
of collaborators are randomly built [Shah et al., 2010]
10 groups of 3 participants asked to list collaboratively 6 queries related to the information
need [Morris et al., 2008]
• Simulating the collaborative rankings on the participants’ queries
80 / 111
114. 1. Collaboration and Information Retrieval 2. Collaborative IR techniques and models Evaluation 4. Challenges ahead 5. Discussion
PROTOCOLS
BATCH: COLLABORATION SIMULATION [MORRIS ET AL., 2008, SHAH ET AL., 2010]
• Real users formulating queries w.r.t. the shared information need
15 individual users asked to list queries they would associate to 10 TREC topics. Then, pairs
of collaborators are randomly built [Shah et al., 2010]
10 groups of 3 participants asked to list collaboratively 6 queries related to the information
need [Morris et al., 2008]
• Simulating the collaborative rankings on the participants’ queries
Advantages:
• Larger number of experimental tests
(parameter tuning, more baselines, ...)
• Less costly and less time consuming
than user studies
Limitations:
• Small manifestation of the collaborative
aspects
• No span of the collaborative search
session
• Difficult to evaluate the generalization of
findings
80 / 111
115. 1. Collaboration and Information Retrieval 2. Collaborative IR techniques and models Evaluation 4. Challenges ahead 5. Discussion
PROTOCOLS
LOG-STUDY: COLLABORATION SIMULATION [FOLEY AND SMEATON, 2009A, SOULIER ET AL., 2014B]
• Individual search logs (from user studies or official benchmarks)
81 / 111
116. 1. Collaboration and Information Retrieval 2. Collaborative IR techniques and models Evaluation 4. Challenges ahead 5. Discussion
PROTOCOLS
LOG-STUDY: COLLABORATION SIMULATION [FOLEY AND SMEATON, 2009A, SOULIER ET AL., 2014B]
• Individual search logs (from user studies or official benchmarks)
• Chronological synchronization of individual search actions
81 / 111
117. 1. Collaboration and Information Retrieval 2. Collaborative IR techniques and models Evaluation 4. Challenges ahead 5. Discussion
PROTOCOLS
LOG-STUDY: COLLABORATION SIMULATION [FOLEY AND SMEATON, 2009A, SOULIER ET AL., 2014B]
• Individual search logs (from user studies or official benchmarks)
• Chronological synchronization of individual search actions
• Simulating the collaborative rankings on the users’ queries
81 / 111
118. 1. Collaboration and Information Retrieval 2. Collaborative IR techniques and models Evaluation 4. Challenges ahead 5. Discussion
PROTOCOLS
LOG-STUDY: COLLABORATION SIMULATION [FOLEY AND SMEATON, 2009A, SOULIER ET AL., 2014B]
• Individual search logs (from user studies or official benchmarks)
• Chronological synchronization of individual search actions
• Simulate the collaborative rankings on the users’ queries
82 / 111
119. 1. Collaboration and Information Retrieval 2. Collaborative IR techniques and models Evaluation 4. Challenges ahead 5. Discussion
PROTOCOLS
LOG-STUDY: COLLABORATION SIMULATION [FOLEY AND SMEATON, 2009A, SOULIER ET AL., 2014B]
• Individual search logs (from user studies or official benchmarks)
• Chronological synchronization of individual search actions
• Simulate the collaborative rankings on the users’ queries
Advantages:
• Modeling of a collaborative session
• Larger number of experimental tests
(parameter tuning, more baselines, ...)
• Less costly and less time consuming
than user studies
Limitations:
• Any manifestation of the collaborative
aspects
• Difficult to evaluate the generalization of
findings
82 / 111
120. 1. Collaboration and Information Retrieval 2. Collaborative IR techniques and models Evaluation 4. Challenges ahead 5. Discussion
PROTOCOLS
LOG-STUDIES: COLLABORATIVE SEARCH LOGS [SOULIER ET AL., 2014A]
• Real logs of collaborative search sessions
• CIR ranking model launched on the participant queries
83 / 111
121. 1. Collaboration and Information Retrieval 2. Collaborative IR techniques and models Evaluation 4. Challenges ahead 5. Discussion
PROTOCOLS
LOG-STUDIES: COLLABORATIVE SEARCH LOGS [SOULIER ET AL., 2014A]
• Real logs of collaborative search sessions
• CIR ranking model launched on the participant queries
Advantages:
• A step forward to realistic collaborative
scenarios
• Queries resulting from a collaborative
search process
Limitations:
• Costly and time-consuming, unless
available data
• Implicit feedback on the retrieved
document lists
83 / 111
122. 1. Collaboration and Information Retrieval 2. Collaborative IR techniques and models Evaluation 4. Challenges ahead 5. Discussion
PROTOCOLS
USER-STUDIES [PICKENS ET AL., 2008]
• Real users performing the collaborative task
• CIR models launched in real time in response to users’ actions
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123. 1. Collaboration and Information Retrieval 2. Collaborative IR techniques and models Evaluation 4. Challenges ahead 5. Discussion
PROTOCOLS
USER-STUDIES [PICKENS ET AL., 2008]
• Real users performing the collaborative task
• CIR models launched in real time in response to users’ actions
Advantages:
• One of the most realistic scenario
(instead of panels)
Limitations:
• Costly and time-consuming
• Controlled tasks in laboratory
84 / 111
124. 1. Collaboration and Information Retrieval 2. Collaborative IR techniques and models Evaluation 4. Challenges ahead 5. Discussion
METRICS
CATEGORIES OF METRICS
Evaluation Objectives in collaborative search
• Measuring the retrieval effectiveness of the ranking models
• Measuring the search effectiveness of the collaborative groups
• Measuring collaborators’ satisfaction and cognitive effort
• Analyzing collaborators’ behavior
• User-driven metrics/indicators aiming
at evaluating:
The collaborators’ awareness and
satisfaction [Aneiros and Morris, 2003,
Smyth et al., 2005]
The cognitive effort
The search outcomes
• System-oriented metrics/indicators
aiming at evaluating:
The retrieval effectiveness of the ranking
models
The insurance of the collaborative
paradigms of the ranking models
(division of labor)
The collaborative relevance of
documents ( → ground truth)
85 / 111
125. 1. Collaboration and Information Retrieval 2. Collaborative IR techniques and models Evaluation 4. Challenges ahead 5. Discussion
METRICS
USER-DRIVEN METRICS
• Search log analysis
Behavioral analysis: collaborators’ actions [Tamine and Soulier, 2015]
Feature Description
npq Average number of visited pages by query
dt Average time spent between two visited pages
nf Average number of relevance feedback information (snippets, annotations
& bookmarks)
qn Average number of submitted queries
ql Average number of query tokens
qo Average ratio of shared tokens among successive queries
nbm Average number of exchanged messages within the search groups
86 / 111
126. 1. Collaboration and Information Retrieval 2. Collaborative IR techniques and models Evaluation 4. Challenges ahead 5. Discussion
METRICS
USER-DRIVEN METRICS
• Search log analysis
Behavioral analysis: collaborators’ actions [Tamine and Soulier, 2015]
Feature Description
npq Average number of visited pages by query
dt Average time spent between two visited pages
nf Average number of relevance feedback information (snippets, annotations
& bookmarks)
qn Average number of submitted queries
ql Average number of query tokens
qo Average ratio of shared tokens among successive queries
nbm Average number of exchanged messages within the search groups
Behavioral analysis: communication channels
[Gonz´alez-Ib´a˜nez et al., 2013, Strijbos et al., 2004]
c
86 / 111
127. 1. Collaboration and Information Retrieval 2. Collaborative IR techniques and models Evaluation 4. Challenges ahead 5. Discussion
METRICS
USER-DRIVEN METRICS
• Search log analysis
Behavioral analysis: collaborators’ actions and communication channels
Search outcomes [Shah, 2014]
c
Evidence sources Description
Visit. doc. Rel. doc. Dwell-time Number of visits
(Unique) Coverage (unique) visited webpages
Likelihood of discovery number of visits-based IDF metric
(Unique) Useful pages (unique) number of useful pages
(visited more than 30 seconds)
Precision number of distinct relevant and vis-
ited pages over the number of dis-
tinct visited pages
Recall number of distinct relevant and vis-
ited pages over the number of dis-
tinct relevant pages
F-measure Combinaison of precision and recall
87 / 111
128. 1. Collaboration and Information Retrieval 2. Collaborative IR techniques and models Evaluation 4. Challenges ahead 5. Discussion
METRICS
USER-DRIVEN METRICS
Exercice
Estimating the search outcome effectiveness of a collaborative search session (Coverage, Relevant
Coverage, Precision, Recall, F-measure).
• Let’s consider:
a collaborative search session involving two users u1 and u2 aiming at solving an information
need I.
During the session, u1 selected the following documents: {d1, d2, d6, d9, d17, d20}
During the session, u2 selected the following documents: {d3, d4, d5, d6, d7}
a collection of 20 documents D = {d ; i = 1, ·, 20},
a ground truth for the information need I: GTI = {d2, d6, d15}
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129. 1. Collaboration and Information Retrieval 2. Collaborative IR techniques and models Evaluation 4. Challenges ahead 5. Discussion
METRICS
USER-DRIVEN METRICS
Exercice
Estimating the search outcome effectiveness of a collaborative search session (Coverage, Relevant
Coverage, Precision, Recall, F-measure).
• Let’s consider:
a collaborative search session involving two users u1 and u2 aiming at solving an information
need I.
During the session, u1 selected the following documents: {d1, d2, d6, d9, d17, d20}
During the session, u2 selected the following documents: {d3, d4, d5, d6, d7}
a collection of 20 documents D = {d ; i = 1, ·, 20},
a ground truth for the information need I: GTI = {d2, d6, d15}
• Evaluation metrics:
UniqueCoverage(g) = {d1, d2, d3, d4, d5, d6, d7, d9, d17, d20}.
RelevantCoverage(g) = {d2, d6}.
Precision(g) = 2
10 = 0.2
Recall(g) = 2
3 = 0.66
F − measure(g) = 2·0.2·0.66
0.2+0.66 = 0.33
88 / 111
130. 1. Collaboration and Information Retrieval 2. Collaborative IR techniques and models Evaluation 4. Challenges ahead 5. Discussion
METRICS
USER-DRIVEN METRICS
• Questionnaires and interviews
The “TLX instrument form”: measuring the cognitive effort
c
89 / 111
131. 1. Collaboration and Information Retrieval 2. Collaborative IR techniques and models Evaluation 4. Challenges ahead 5. Discussion
METRICS
USER-DRIVEN METRICS
• Questionnaires and interviews
The “TLX instrument form”: measuring the cognitive effort
Satisfaction interviews [Shah and Gonz´alez-Ib´a˜nez, 2011a, Tamine and Soulier, 2015]
Question Answer type
Have you already participated in such user
study? If yes, please describe it.
Free-answer
What do you think about this collaborative man-
ner of seeking information?
Free-answer
What was the level of difficulty of the task? a) Easy (Not difficult) b) Moder-
ately difficult c) Difficult
What was task difficulty related to? Free-answer
Could you say that the collaborative system sup-
ports your search?
a) Yes b) Not totally c) Not at all
How could we improve this system? Free-answer
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132. 1. Collaboration and Information Retrieval 2. Collaborative IR techniques and models Evaluation 4. Challenges ahead 5. Discussion
METRICS
SYSTEM-ORIENTED METRICS [SOULIER ET AL., 2014A]
• The precision Prec@R(g) at rank R of a collaborative group g:
Prec@R(g) = 1
T(g)
|T(g)|
t=1 Prec@R(g)(t) = 1
T(g)
|T(g)|
t=1
RelCov@R(g)(t)
Cov@R(g)(t) (28)
• The recall Recall@R(g) at rank R of group g:
Recall@R(g) = 1
T(g)
|T(g)|
t=1 Recall@R(g)(t) = 1
T(g)
|T(g)|
t=1
RelCov@R(g)(t)
|RelDoc|
(29)
• The F-measure Fsyn@R(g) at rank R of a collaborative group g:
F@R(g) =
1
T(g)
|T(g)|
t=1
2 ∗ Prec@R(g)(t) ∗ Recall@R(g)(t)
Prec@R(g)(t) + Recall@R(g)(t)
(30)
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133. 1. Collaboration and Information Retrieval 2. Collaborative IR techniques and models Evaluation 4. Challenges ahead 5. Discussion
METRICS
SYSTEM-ORIENTED METRICS AND GROUND TRUTH
Example
Estimating the retrieval effectiveness of the rankings of CIR models (Coverage, Relevant Coverage,
Precision, Recall, F-measure).
Ground truth GTI = {d2, d6, d15}
Query Document ranking
q1 d1, d2, d3
q2 d2, d8, d14
q3 d17, d3, d8
q4 d9, d15, d2
q5 d1, d5, d3
q6 d20, d3, d1
q7 d5, d2, d4
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METRICS
GROUND TRUTH
• Evidence sources:
From relevance assessments [Morris et al., 2008]
From individual search logs [Foley and Smeaton, 2009b, Soulier et al., 2014b]
From collaborative search logs [Shah and Gonz´alez-Ib´a˜nez, 2011b, Soulier et al., 2014a]
• Importance of considering an agreement level of at least two users (belonging to
different groups?) [Shah and Gonz´alez-Ib´a˜nez, 2011b, Soulier et al., 2014a]
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BASELINES
• Benefit of the collaboration
Individual models: BM25, LM, ...
Search logs of individual search
• Collaboration optimization through algorithmic mediation
User-driven approach with collaborative interfaces
• Benefit of roles
Role-based vs. No-role CIR models [Foley and Smeaton, 2009b, Morris et al., 2008]
Dynamic vs. predefined CIR models [Pickens et al., 2008, Shah et al., 2010]
• ...
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TOOLS AND DATASETS
• Simulation-based evaluation
TREC Interractive dataset [Over, 2001]
Other available search logs (TREC, CLEF, propritary, ...)
• Log-studies
Collaborative dataset [Tamine and Soulier, 2015]
• User-studies
open-source Coagmento plugin [Shah and Gonz´alez-Ib´a˜nez, 2011a]:
http://www.coagmento.org/collaboraty.php
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PLAN
1. Collaboration and Information Retrieval
2. Collaborative IR techniques and models
3. Evaluation
4. Challenges ahead
Theoretical foundations of CIR
Empirical evaluation of CIR
Open ideas
5. Discussion
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THEORETICAL FOUNDATIONS OF CIR
• Towards a novel probabilistic framework of relevance for CIR
What is a ”good ranking” with regard to the expected synergic effect of collaboration?
• Dynamic IR models for CIR
How to optimize long-term gains over multiple users, user-user interactions, user-system
interactions and multi-search sessions?
How to formalize the division of labor through the evolving of users’ information needs over
time?
• Towards an axiomatic approach of relevance for CIR
Are IR heuristics similar to CIR heuristics?
Can relevance towards a group be modeled by a set of formally defined constraints on a
retrieval function?
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EVALUATION OF CIR
• Multiple facets of system performance
Should we measure the performance in terms of gain per time, effort gain per user,
effectiveness of outcomes or all in a whole?
How do we delineate the performance of the system from the performance and interaction of
the users?
• Robust experiments for CIR
Should experimental evaluation protocol be task-dependent?
Are simulated work tasks used in IIR reasonable scenario for evaluating CIR scenario?
How to build data collections allowing reproducible experiments and handling robust
statistical tests?
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OPEN IDEAS
• Multi-level CIR [Htun et al., 2015]
Non-uniform information access within the group
Application domains: legacy, military, ...
• Collaborative group building
Task-based group building (information search, synthesis, sense-making,
question-answering...)
Leveraging users’ knowledge, collaboration abilities, information need perception
• Socio-collaborative IR [Morris, 2013]
Web search vs. social networking [Oeldorf-Hirsch et al., 2014]
Leveraging from the crowd to solve a user’s information need
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PLAN
1. Collaboration and Information Retrieval
2. Collaborative IR techniques and models
3. Evaluation
4. Challenges ahead
5. Discussion
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DISCUSSION
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