Being Social


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Presentation about research projects in Telefonica related to context-aware and personalized information access. It was given in the Industrial Track for SIGIR2010

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Being Social

  1. 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. 2. But first... About Telefonica and Telefonica R&D
  3. 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. 4. Currently among the largest in the world Telco sector worldwide ranking by market cap (US$ bn) Source: Bloomberg, 06/12/09
  5. 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. 6. Scientific Research Mobile and Ubicomp Multimedia Core User Modelling & Data Mining HCIR DATA MINING Wireless Systems Content Distribution & P2P Social Networks
  7. 7. Enough company talk already...
  8. 8. Information Overload
  9. 9. More is Less W or se D ec is ns io io ns is ec D s es L
  10. 10. Search engines don’t always hold the answer
  11. 11. You are not alone!
  12. 12. That's what friends are for...
  13. 13. What's her name?
  14. 14. Catalan Adriana
  15. 15. Porqpine J.M. Pujol and P. Rodriguez "Towards Distributed Social Search Engines", in WWW '09
  16. 16. 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    
  17. 17. 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
  18. 18. Context Overload
  19. 19.
  20. 20. Mobile phones are “personal”
  21. 21. Mobile users tend to seek “fresh” content
  22. 22. Where is the nearest florist?
  23. 23. Where is that really cool cocktail bar I went to last month?
  24. 24. What about discovery?
  25. 25. What about information to help me decide?
  26. 26. Interesting things close to me?
  27. 27. Events near me?
  28. 28. Can we improve the search and discovery experience of mobile users by providing a readily available connection to their social network?
  29. 29. 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
  30. 30. 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
  31. 31. Apr 2009, 16 users, 1 week, ireland Live Field Study in-the-wild Sept 2009, 34 users, 1 month, ireland
  32. 32. A tool for helping and sharing…..
  33. 33. A tool for supporting curiosity
  34. 34. …..extension to my social network
  35. 35. But ...
  36. 36. Crowds are not always wise ● Predictions based on large datasets that are sparse and noisy
  37. 37. 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
  38. 38. Who Can we trust?
  39. 39. “It is really only experts  who can reliably account  for their reactions”
  40. 40. 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
  41. 41. 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
  42. 42. Working Prototypes Music recommendations, mobile geo-located recommendations...
  43. 43. 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
  44. 44. Thank you! @xamat