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Recommender Systems in 2012

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Talk at Data Science London Meetup on Recommender Systems

Talk at Data Science London Meetup on Recommender Systems


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  • 1. acm recsys 2012:recommender systems, today@neal_lathia
  • 2. warning:daunting task lookout for twitter handles
  • 3. why #recsys? information overloadmailing lists; usenet news (1992) see: @jkonstan, @presnick
  • 4. why #recsys? information overload filter failuremovies; books; music (~1995)
  • 5. why #recsys? information overload filter failure creating valueadvertising; engagement; connection (today)
  • 6. @dtunkelang
  • 7. (1) collaborative “based on the premise that people looking forinformation should be able to make use of what others have already found and evaluated” (maltz & ehrlick)
  • 8. (2) query-less“in September 2010 Schmidt said that one day the combination of cloud computing and mobile phones would allow Google to pass on information to users without them even typing in search queries”
  • 9. (3) discovery engines“we are leaving the age of information and entering the age of recommendation” (anderson)
  • 10. input: ratings, clicks, views users → items process: SVD, kNN, RBM, etc. f(user, item) → prediction ~ ratingoutput: prediction-ranked recommendations measure: |prediction – rating| 2 (prediction – rating)
  • 11. traditional problemsaccuracy, scalability, distributed computation, similarity, cold-start, … (dont reinvent the wheel)
  • 12. acm recsys 2012:5 open problems
  • 13. problem 1: predictionstemporality, multiple co-occurring objectives: diversity, novelty, freshness, serendipity, explainability
  • 14. problem 2: algorithmsmore algorithms vs. more data vs. more rating effort
  • 15. what is your algorithm doing? f(user, item) → R f(user, item1, item2) → R f(user, [item1...itemn]) → R e.g., @alexk_z @abellogin
  • 16. problem 3: users + ratingssignals, context, groups, intents, interfaces
  • 17. @xamat
  • 18. problem 4: itemslifestyle, behaviours, decisions, processes, software development
  • 19. @presnick
  • 20. problem 5: measurementranking metrics vs. usability testing vs. A/B testing
  • 21. Online Controlled Experiments: Introduction, Learnings, and HumblingStatisticshttp://www.exp-platform.com/Pages/2012RecSys.aspx
  • 22. 3 key lessons
  • 23. lesson 1: #recsys is an ensemble ...of disciplines statistics, machine learning, human-computer interaction, social network analysis, psychology
  • 24. lesson 2: return to the domainuser effort, generative models, cost of a freakommendation, value you seek to create
  • 25. @plamere
  • 26. lesson 3: join the #recsys community learn, build, research, deploy: @MyMediaLite, @LensKitRS @zenogantner, @elehack contribute, read: #recsyswiki, @alansaid
  • 27. recommender systems, today@neal_lathia