Past present and future of Recommender Systems: an Industry Perspective

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Talk presented at the ACM Recommender Systems Conference in Boston, 2016

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Past present and future of Recommender Systems: an Industry Perspective

  1. 1. 11 Past, Present & Future of Recommender Systems: An Industry Perspective Xavier Amatriain (Quora) Justin Basilico (Netflix) RecSys 2016 @xamat @JustinBasilico DeLorean image by JMortonPhoto.com & OtoGodfrey.com
  2. 2. 2 1. Past
  3. 3. 3 Netflix Prize 2006
  4. 4. 4 For more information ...
  5. 5. 5 2. Present
  6. 6. 6 Recommender Systems in Industry Recommender Systems are used pervasively across application domains
  7. 7. 7 Recommender Systems in Industry click upvote downvote expand share
  8. 8. 8 Beyond explicit feedback ▪ Applications typically oriented around an action: click, buy, read, listen, watch, … ▪ Implicit Feedback ▪ More data: Implicit feedback comes as part of normal use ▪ Better data: Matches with actions we want to predict ▪ Augment with contextual information ▪ Content for cold-start ▪ Hybrid: Combine together when you can
  9. 9. 9 Ranking ▪ Ranking items is central to recommending ▪ News feeds ▪ Items in catalogs ▪ … ▪ Most recsys can be assimilated to: ▪ A learning-to-rank approach ▪ A feature engineering problem
  10. 10. 10 Everything is a RecommendationRows Ranking
  11. 11. 11 3. Future
  12. 12. 12 Many interesting future directions 1. Indirect feedback 2. Value-awareness 3. Full-page optimization 4. Personalizing the how ▪ Others ▪ Intent/session awareness ▪ Interactive recommendations ▪ Context awareness ▪ Deep learning for recommendations ▪ Conversational interfaces/bots for recommendations ▪ …
  13. 13. 13 Indirect Feedback Challenges ▪ User can only click on what you show ▪ But, what you show is the result of what your model predicted is good ▪ No counterfactuals ▪ Implicit data has no real “negatives” Potential solutions ▪ Attention models ▪ Context is also indirect/implicit feedback ▪ Explore/exploit approaches and learning across time ▪ ... click upvote downvote expand share
  14. 14. 14 Value-aware recommendations ▪ Recsys optimize for probability of action ▪ Not all clicks/actions have the same “reward” ▪ Different margin in ecommerce ▪ Different “quality” of content ▪ Long-term retention vs. short-term clicks (clickbait) ▪ … ▪ In Quora, the value of showing a story to a user is approximated by weighted sum of actions: v = ∑a va 1{ya = 1} ▪ Extreme application of value-aware recommendations: suggest items to create that have the highest value ▪ Netflix: Which shows to produce or license ▪ Quora: Answers and questions that are not in the service
  15. 15. 15 Full page optimization ▪ Recommendations are rarely displayed in isolation ▪ Rankings are combined with many other elements to make a page ▪ Want to optimize the whole page ▪ Means jointly solving for set of items and their placement ▪ While incorporating ▪ Diversity, freshness, exploration ▪ Depth and coverage of the item set ▪ Non-recommendation elements (navigation, editorial, etc.) ▪ Needs work hand-in-hand with the UX
  16. 16. 16 Personalizing How We Recommend (… not just what we recommend) ▪ Algorithm level: Ideal balance of diversity, novelty, popularity, freshness, etc. may depend on the person ▪ Display level: How you present items or explain recommendations can also be personalized ▪ Select the best information and presentation for a user to quickly decide whether or not they want an item ▪ Interaction level: Balancing the needs of lean-back users and power users
  17. 17. 17 Rows Example: Rows & Beyond Hero Image Predicted rating Evidence Synopsis Horizontal Image Row Title Metadata Ranking
  18. 18. 18 4. Conclusions
  19. 19. 19 Conclusions ▪ Approaches have evolved a lot in the past 10 years ▪ Looking forward to the next 10 ▪ Industry and academia working together has advanced the field since the beginning, we should make sure that continues
  20. 20. 20 Thank You Justin Basilico jbasilico@netflix.com @JustinBasilico Xavier Amatriain xavier@quora.com @xamat

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