Interactivity and feedback

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G

Gene Golovchinsky

Slides given at the NSF-sponsored workshop on task-based search at UNC

Interactivity and feedback
Gene Golovchinsky
FX Palo Alto Laboratory
@HCIR_GeneG

Thanks to Tony Dunnigan for the drawings

A half-assed analogy

A trivial taxonomy

Some self-serving examples

What I mean by “task”
Information Multiple Evolving
seeking occurs interactions information
over time with the system needs

Human Computer

Two kinds of feedback
Person System
Person trains system to find documents

System  Person
System indicates possibilities to guide
person

Person  System
People don’t use relevance feedback, right?

They do when suitably motivated.

Two examples:

Ancestry.com
Predictive coding

Relevance feedback at Ancestry.com
Search
People find historical records about specific individuals
Facts from records are saved to individuals in family trees

Relevance feedback
Saved facts are automatically incorporated into subsequent
queries
Relevance feedback is inferred from saved records

Many motivated users
Hundreds of hours of system use
Lots of interaction

Person  System

Relevance Feedback in
Predictive Coding

Predictive coding is a technique for training a classifier to find
relevant documents

Used in e-discovery to increase accuracy and reduce costs

Machine learning algorithm is trained through hundreds of
relevance judgments; applied to millions of documents

Big (and getting bigger) business

Person  System

System  Person
System provides hints about potential actions
Information scent
Which documents are new
Which terms are effective
Ways to expand/reformulate the query

Examples
Facets indicating numbers of matching documents
Previously-saved or viewed documents
History of queries, related queries
Previews, hints, etc.

Interacting with the past

Previously
saved
records for
this person

Ancestry.com
System  Person

Interacting with the past

Newly-
retrieved

Re-retrieved

Querium
System  Person

Interacting with the present
mspace

Commerical faceted browsing UI
RelationBrowser

Interacting with the future

System  Person

Interacting with the future
Query preview Query nudges
As searcher types, shows As searcher types, changes
distribution of new vs. re- halo color to encourage longer
retrieved documents in a queries
search session

7
6
5
Number of Query Terms

4
3
2
No instr.
1
0 Instruction

No halo System  Person
Halo

Design Challenges

How do we get people to use
relevance feedback?
How do we help people discover
which queries will be effective?
How do we help people plan?

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Interactivity and feedback

1. Interactivity and feedback Gene Golovchinsky FX Palo Alto Laboratory @HCIR_GeneG Thanks to Tony Dunnigan for the drawings

2. A half-assed analogy A trivial taxonomy Some self-serving examples

3.

4.

5.

6.

7.

8.

9. What I mean by “task” Information Multiple Evolving seeking occurs interactions information over time with the system needs Human Computer

10. Two kinds of feedback Person System Person trains system to find documents System  Person System indicates possibilities to guide person

11. Person  System People don’t use relevance feedback, right? They do when suitably motivated. Two examples: Ancestry.com Predictive coding

12. Relevance feedback at Ancestry.com Search People find historical records about specific individuals Facts from records are saved to individuals in family trees Relevance feedback Saved facts are automatically incorporated into subsequent queries Relevance feedback is inferred from saved records Many motivated users Hundreds of hours of system use Lots of interaction Person  System

13. Relevance Feedback in Predictive Coding Predictive coding is a technique for training a classifier to find relevant documents Used in e-discovery to increase accuracy and reduce costs Machine learning algorithm is trained through hundreds of relevance judgments; applied to millions of documents Big (and getting bigger) business Person  System

14. System  Person System provides hints about potential actions Information scent Which documents are new Which terms are effective Ways to expand/reformulate the query Examples Facets indicating numbers of matching documents Previously-saved or viewed documents History of queries, related queries Previews, hints, etc.

15. Interacting with the past Previously saved records for this person Ancestry.com System  Person

16. Interacting with the past Newly- retrieved Re-retrieved Querium System  Person

17. Interacting with the present mspace Commerical faceted browsing UI RelationBrowser

18. Interacting with the future System  Person

19. Interacting with the future Query preview Query nudges As searcher types, shows As searcher types, changes distribution of new vs. re- halo color to encourage longer retrieved documents in a queries search session 7 6 5 Number of Query Terms 4 3 2 No instr. 1 0 Instruction No halo System  Person Halo

20. Design Challenges How do we get people to use relevance feedback? How do we help people discover which queries will be effective? How do we help people plan?