Evaluating Collaborative Filtering Over Time Neal Lathia Stephen Hailes, Licia Capra UCL - June 14, 2010
Broad Scenario <ul><li>Information Filtering </li><ul><li>Data Relevance: learning from data, finding 'important' data
Personalisation: using data to meet different individual's goals (decision support) </li></ul><li>Web Recommender Systems ...
Studied in various contexts: data mining, HCI, stats, psychology, social nets, ... </li></ul></ul>
Specific Context <ul><li>Rift between dynamics of deployed systems and methodology used to guide research </li><ul><li>Sta...
System: updating itself </li></ul><li>Thesis adopts a system-oriented perspective </li></ul>
Key Contributions <ul><li>Temporal Analysis </li><ul><li>How do datasets change?
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PhD Viva - Short Intro

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PhD Viva - Short Intro

  1. 1. Evaluating Collaborative Filtering Over Time Neal Lathia Stephen Hailes, Licia Capra UCL - June 14, 2010
  2. 2. Broad Scenario <ul><li>Information Filtering </li><ul><li>Data Relevance: learning from data, finding 'important' data
  3. 3. Personalisation: using data to meet different individual's goals (decision support) </li></ul><li>Web Recommender Systems </li><ul><li>Many examples (Amazon, Last.fm, Netflix,...)
  4. 4. Studied in various contexts: data mining, HCI, stats, psychology, social nets, ... </li></ul></ul>
  5. 5. Specific Context <ul><li>Rift between dynamics of deployed systems and methodology used to guide research </li><ul><li>Static algorithms are then deployed in dynamic settings, with no insight into differences </li></ul><li>There are two (complimentary) aspects to temporal changes in recommender systems </li><ul><li>Users: shifting preferences
  6. 6. System: updating itself </li></ul><li>Thesis adopts a system-oriented perspective </li></ul>
  7. 7. Key Contributions <ul><li>Temporal Analysis </li><ul><li>How do datasets change?
  8. 8. How does similarity evolve? </li></ul><li>Novel Methodology </li><ul><li>How to run experiments that mimic a deployed recommender system? </li></ul><li>Hybrid Algorithms </li><ul><li>How to improve accuracy and/or diversity?
  9. 9. How to secure the system from attackers? </li></ul></ul>
  10. 10. Key Results <ul><li>Temporal Analysis </li><ul><li>Unreliability of measured similarity </li></ul><li>Novel Methodology </li><ul><li>Comparison to claims from Netflix prize </li></ul><li>Hybrid Algorithms </li><ul><li>Trade-offs between various metrics </li></ul></ul>
  11. 11. Publications <ul><li>N. Lathia, S. Hailes, L. Capra, and X. Amatriain. Temporal Diversity in Recommender Systems. To appear, ACM SIGIR 2010, July 2010. Geneva, Switzerland.
  12. 12. N. Lathia, S. Hailes, and L. Capra. Evaluating Collaborative Filtering Over Time. ACM SIGIR Workshop on the Future of IR Evaluation., July 2009. Boston, Massachusetts.
  13. 13. N. Lathia, S. Hailes, and L. Capra. Temporal Collaborative Filtering With Adaptive Neighbourhoods. ACM SIGIR 2009., July 2009. Boston, Massachusetts.
  14. 14. N. Lathia, S. Hailes, and L. Capra. kNN CF: A Temporal Social Network. ACM Recommender Systems (RecSys '08), October 2008. Lausanne, Switzerland.
  15. 15. Computing Recommendations With Collaborative Filtering. Chapter 2 in &quot;Collaborative and Social Information Retrieval and Access: Techniques for Improved User Modeling., July 2008.
  16. 16. N. Lathia, S. Hailes, and L. Capra. The Effect of Correlation Coefficients on Communities of Recommenders. 23rd Annual ACM Symposium on Applied Computing: Trust, Recommendations, Evidence and other Collaboration Know-how (TRECK Track)., March 2008. Fortaleza, Brazil.
  17. 17. N. Lathia, S. Hailes, and L. Capra. Trust-Based Collaborative Filtering. Joint iTrust and PST Conferences on Privacy, Trust Management and Security (IFIPTM)., June 2008. Trondheim, Norway.
  18. 18. N. Lathia. Learning to Trust on the Move. International Workshop on Trust in Mobile Environments (TIME 08)., June 2008. Trondheim, Norway. </li></ul>
  19. 19. Future Directions <ul><li>Temporal aspects of collaborative filtering are a clear theme of recent work </li><ul><li>Although the focus is, for the most part, on the user side (preference shift) rather than system view </li></ul><li>Many unexplored facets of temporal recommendation </li><ul><li>E.g., novelty, serendipity, delay </li></ul><li>Research focus continues to rely on available web-centric (movie, music) data </li><ul><li>Mobile/pervasive recommender systems? </li></ul></ul>

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