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Being Social:
Research in Context-aware and Personalized
            Information Access
                @ Telefonica

        Xavier Amatriain (@xamat)
              J.M. Pujol (@solso)
         Karen Church (@karenchurch)




              #SIGIR2010
             Geneva, July '10
But first...




About Telefonica and Telefonica R&D
Telefonica is a fast-growing Telecom


                            1989                   2000                    2008
  Clients                        About 12          About 68          About 260
                                  million           million           million
                                subscribers       customers          customers
 Services                        Basic        Wireline and mobile    Integrated ICT
                            telephone and       voice, data and     solutions for all
                             data services     Internet services       customers
Geographies
                                                 Operations in      Operations in
                              Spain                                 25 countries
                                                 16 countries

   Staff
                     About 71,000                About 149,000         About 257,000
                     professionals                professionals         professionals

 Finances                   Rev: 4,273 M€       Rev: 28,485 M€      Rev: 57,946 M€
                            EPS(1): 0.45 €      EPS(1): 0.67 €        EPS: 1.63 €
            (1) EPS: Earnings per share
Currently among the largest in the world
         Telco sector worldwide ranking by market cap (US$ bn)




      Source: Bloomberg, 06/12/09
Telefonica R&D (TID)


                                                                      MISSION
                                                               “To contribute to the
                                                                improvement of the
  n   Founded in 1988                                            Telefónica Group’s
  n   Largest private R&D center in Spain                     competitivness through
  n   More than 1100 professionals                           technological innovation”
  n   Five centers in Spain and two in Latin America



 Telefónica was in 2008 the first Spanish company by R&D Investment and the
                                  third in the EU

      Applied             R&D
      research           61 M€                         R&D
                  Products / Services / Processes      594 M€
                  development                                                  4.384 M€
                                                          Technological Innovation
                                                          (1)
Scientific Research
                  Mobile and Ubicomp
Multimedia Core                                    User Modelling &
                                                     Data Mining



                                            HCIR

                                                   DATA MINING



                                                        Wireless Systems
Content Distribution & P2P
                                Social Networks
Enough company talk already...
Information Overload
More is Less
                         W
                          or
                            se
                                 D
                                  ec
                                    is
         ns
                                      io
       io

                                        ns
     is
   ec
  D
   s
 es
L
Search engines don’t always hold the answer
Being Social
You are not alone!
That's what friends are for...
What's her name?
Catalan




          Adriana
Porqpine
              J.M. Pujol and P. Rodriguez
"Towards Distributed Social Search Engines", in WWW '09
So
                                       ci


            e
          ar
                                            al
                                              ly
         w          Personalized                  
       ­a
                                                     aw
     xt

         St                                            ar
   te
 on



           an                                            e
C




             d­
                al
                  on        Lazy collaboration
                    e
          d e




                    Cost effective
         ut




                                                 t
       ib




                                               is
    tr




                                          ex
  is




                                        o­
 D




                                      c
                                  an
  Privacy­preserving             C
                                  
WHAT IS PORQPINE?

●
    Distributed social web search engine
●
    Locally caches the page & records user interactions
    (e.g., bookmarking).
●
    Searches by querying local caches of a user’s
    friends
    ●
        Pages that friends have “interacted with” are ranked
        higher
●
    It uses a proxy masking the identity of the friend
Being Social
Context Overload
≠
Mobile phones are “personal”
Mobile users tend to seek “fresh” content
Where is the nearest florist?
Where is that really cool cocktail bar
I went to last month?
What about discovery?
What about information to help me decide?
Interesting things close to me?
Events near me?
Can we improve the search and
  discovery experience of mobile
    users by providing a readily
available connection to their social
             network?
SSB (Social Search Browser)
    K. Church et al. "SocialSearchBrowser: A novel mobile search and
                  information discovery tool.", in IUI '10
K. Church et al. “The "Map Trap"? An evaluation of map versus text-based
    interfaces for location-based mobile search services”, in WWW '10
SSB
 iPhone optimized web-
 application + Facebook app
 When launched it centers on the
 users current physical location
 Displays all queries/questions
 posted by other users in that
 location
 As users pan/zoom the set of
 queries is updated
 Users can post new queries or
 interact with queries of others
Apr 2009, 16 users, 1 week, ireland



     Live Field Study in-the-wild

Sept 2009, 34 users, 1 month, ireland
A tool for helping and sharing…..
A tool for supporting curiosity
…..extension to my social network
But ...
Crowds are not always wise




●   Predictions based on large datasets that
    are sparse and noisy
User Feedback is Noisy
X. Amatriain et al. "I Like It, I Like It Not, Evaluating User Ratings
 Noise in Recommender Systems", UMAP '09




    ● What is the percentage of inconsistencies given an 
    original rating
Who Can we trust?
“It is really only experts 
who can reliably account 
   for their reactions”
The Wisdom of the Few
    X. Amatriain et al. "The wisdom of the few: a collaborative filtering
     approach based on expert opinions from the web", SIGIR '09
Expert-based CF
●   expert = individual that we can trust to have produced
    thoughtful, consistent and reliable evaluations (ratings) of
    items in a given domain
●   Expert-based Collaborative Filtering
    ●   Find neighbors from a reduced set of experts instead of
        regular users.
         1. Identify domain experts with reliable ratings
         2. For each user, compute “expert neighbors”
         3. Compute recommendations similar to standard kNN CF
Working Prototypes
Music recommendations,
mobile geo-located
recommendations...
Summary
➢   We all knew about information overload
➢   But now there is also context overload
     (especially on mobile)
➢   One way to deal with both is to tap into the
     social network
➢   Many times friends can help but others you
     need to move into the crowds
➢   But crowds are not always wise
➢   Sometimes you are better off with experts
Thank you!
  @xamat

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Being Social

  • 1. Being Social: Research in Context-aware and Personalized Information Access @ Telefonica Xavier Amatriain (@xamat) J.M. Pujol (@solso) Karen Church (@karenchurch) #SIGIR2010 Geneva, July '10
  • 2. But first... About Telefonica and Telefonica R&D
  • 3. Telefonica is a fast-growing Telecom 1989 2000 2008 Clients About 12 About 68 About 260 million million million subscribers customers customers Services Basic Wireline and mobile Integrated ICT telephone and voice, data and solutions for all data services Internet services customers Geographies Operations in Operations in Spain 25 countries 16 countries Staff About 71,000 About 149,000 About 257,000 professionals professionals professionals Finances Rev: 4,273 M€ Rev: 28,485 M€ Rev: 57,946 M€ EPS(1): 0.45 € EPS(1): 0.67 € EPS: 1.63 € (1) EPS: Earnings per share
  • 4. Currently among the largest in the world Telco sector worldwide ranking by market cap (US$ bn) Source: Bloomberg, 06/12/09
  • 5. Telefonica R&D (TID) MISSION “To contribute to the improvement of the n Founded in 1988 Telefónica Group’s n Largest private R&D center in Spain competitivness through n More than 1100 professionals technological innovation” n Five centers in Spain and two in Latin America Telefónica was in 2008 the first Spanish company by R&D Investment and the third in the EU Applied R&D research 61 M€ R&D Products / Services / Processes 594 M€ development 4.384 M€ Technological Innovation (1)
  • 6. Scientific Research Mobile and Ubicomp Multimedia Core User Modelling & Data Mining HCIR DATA MINING Wireless Systems Content Distribution & P2P Social Networks
  • 7. Enough company talk already...
  • 9. More is Less W or se D ec is ns io io ns is ec D s es L
  • 10. Search engines don’t always hold the answer
  • 12. You are not alone!
  • 15. Catalan Adriana
  • 16. Porqpine J.M. Pujol and P. Rodriguez "Towards Distributed Social Search Engines", in WWW '09
  • 17. So ci e ar al ly w Personalized   ­a aw xt St ar te on an e C d­ al on Lazy collaboration e d e Cost effective ut t ib is tr ex is o­ D  c an Privacy­preserving  C    
  • 18. WHAT IS PORQPINE? ● Distributed social web search engine ● Locally caches the page & records user interactions (e.g., bookmarking). ● Searches by querying local caches of a user’s friends ● Pages that friends have “interacted with” are ranked higher ● It uses a proxy masking the identity of the friend
  • 21.
  • 22. Mobile phones are “personal”
  • 23. Mobile users tend to seek “fresh” content
  • 24. Where is the nearest florist?
  • 25. Where is that really cool cocktail bar I went to last month?
  • 27. What about information to help me decide?
  • 30. Can we improve the search and discovery experience of mobile users by providing a readily available connection to their social network?
  • 31. SSB (Social Search Browser) K. Church et al. "SocialSearchBrowser: A novel mobile search and information discovery tool.", in IUI '10 K. Church et al. “The "Map Trap"? An evaluation of map versus text-based interfaces for location-based mobile search services”, in WWW '10
  • 32. SSB iPhone optimized web- application + Facebook app When launched it centers on the users current physical location Displays all queries/questions posted by other users in that location As users pan/zoom the set of queries is updated Users can post new queries or interact with queries of others
  • 33. Apr 2009, 16 users, 1 week, ireland Live Field Study in-the-wild Sept 2009, 34 users, 1 month, ireland
  • 34. A tool for helping and sharing…..
  • 35. A tool for supporting curiosity
  • 36. …..extension to my social network
  • 38. Crowds are not always wise ● Predictions based on large datasets that are sparse and noisy
  • 39. User Feedback is Noisy X. Amatriain et al. "I Like It, I Like It Not, Evaluating User Ratings Noise in Recommender Systems", UMAP '09 ● What is the percentage of inconsistencies given an  original rating
  • 40. Who Can we trust?
  • 42. The Wisdom of the Few X. Amatriain et al. "The wisdom of the few: a collaborative filtering approach based on expert opinions from the web", SIGIR '09
  • 43. Expert-based CF ● expert = individual that we can trust to have produced thoughtful, consistent and reliable evaluations (ratings) of items in a given domain ● Expert-based Collaborative Filtering ● Find neighbors from a reduced set of experts instead of regular users. 1. Identify domain experts with reliable ratings 2. For each user, compute “expert neighbors” 3. Compute recommendations similar to standard kNN CF
  • 44. Working Prototypes Music recommendations, mobile geo-located recommendations...
  • 45. Summary ➢ We all knew about information overload ➢ But now there is also context overload (especially on mobile) ➢ One way to deal with both is to tap into the social network ➢ Many times friends can help but others you need to move into the crowds ➢ But crowds are not always wise ➢ Sometimes you are better off with experts
  • 46. Thank you! @xamat