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The continuous cold-start problem in e-commerce recommender systems

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Talk at 2nd Workshop on New Trends in Content-Based Recommender Systems, RecSys 2015 Vienna #recsys2015 #CoCoS

Authors: Lucas Bernardi, Jaap Kamps, Julia Kiseleva and Melanie Mueller. Booking.com, Amsterdam University, Eindhoven University.

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The continuous cold-start problem in e-commerce recommender systems

  1. 1. Booking.com The Continuous Cold Start Problem in e-Commerce Recommender Systems RecSys Vienna 2015-09-20 Lucas Bernardi, Melanie Mueller , Amsterdam Jaap Kamps, Julia Kiseleva Amsterdam & Eindhoven University
  2. 2. Booking.com Cold Start in Travel
  3. 3. Booking.com Cold Start in Travel
  4. 4. Booking.com Travel recommendations Destinations related to Vienna: • User-to-user collaborative filtering: • Content-based item-to-item recommendation: Customers who viewed Hotel Sacher Wien also viewed:
  5. 5. Booking.com • Characteristics of the Continuous Cold Start Problem Outline • Addressing the Continuous Cold Start Problem
  6. 6. Booking.com Recommender Systems 5 1 ? ? ? 2 5 4 3 • Task: predict rating of new item • Classical cold start: too many ? ??? new user ? ? ? ? new item
  7. 7. Booking.com • Cold start = not enough information (yet) Classical Cold Start → Bridge period until warmed up Other information: popularity, content... Standard recommender time → performance
  8. 8. Booking.com • Classical cold-start / sparsity new / rare users User Continuous Cold Start • Volatility • Personas • Identity
  9. 9. Booking.com • New users: - Millions unique users/day, growing - Most not logged-in, no cookie Cold Users 1 51 101 151 201 251 301 351 Day ActivityLevel Continuously cold users at Booking.com. Activity levels of two randomly chosen users ver time. The top user exhibits only rare activity throughout a year, and the bottom use • Sparse users: holidays are rare events!
  10. 10. Booking.com • Classical cold-start / sparsity new / rare users User Continuous Cold Start • Volatility user interest changes over time • Personas • Identity
  11. 11. Booking.com Backpacker hostel User Volatility Family hotel Wellness hotel
  12. 12. Booking.com • Classical cold-start / sparsity new / rare users User Continuous Cold Start • Volatility user interest changes over time • Personas different interest at close-by points in time • Identity
  13. 13. Booking.com • Leisure versus business: User Personas 1 11 21 31 41 51 61 71 81 91 Day ActivtyLevel Leisure booking Business booking inuously cold users at Booking.com. Activity levels of two randomly chosen me. The top user exhibits only rare activity throughout a year, and the botto nas, making a leisure and a business booking, without much activity inbetween • Browsing on a sunny versus rainy day • Weekend city trip versus long holiday
  14. 14. Booking.com • Classical cold-start / sparsity new / rare users User Continuous Cold Start • Volatility user interest changes over time • Personas different interest at close-by points in time • Identity failure to match data from same user
  15. 15. Booking.com User Identity Problem • Different devices • Not logged in
  16. 16. Booking.com • Classical cold-start / sparsity new / rare items Item Continuous Cold Start • Volatility item properties/values change over time • Personas item appeals to different types of users • Identity failure to match data from same item
  17. 17. Booking.com • Characteristics of the Continuous Cold Start Problem Outline • Addressing the Continuous Cold Start Problem
  18. 18. Booking.com Addressing Continuous Cold Start - Ask user • Continuous cold start = information continuously missing → Need more/other information:
  19. 19. Booking.com Ask the User
  20. 20. Booking.com - Implicit ratings: buys, clicks, views... Addressing Continuous Cold Start - Ask user • Continuous cold start = information continuously missing → Need more/other information: → can add friction
  21. 21. Booking.com Implicit Ratings Customers who viewed Hotel Sacher Wien also viewed:
  22. 22. Booking.com - Implicit ratings: buys, clicks, views... Addressing Continuous Cold Start - Ask user • Continuous cold start = information continuously missing → Need more/other information: - Popularity → can add friction → weak signal
  23. 23. Booking.com Destination Finder
  24. 24. Booking.com - Implicit ratings: buys, clicks, views... Addressing Continuous Cold Start - Content - Ask user → surprisingly hard to beat! → not personalized - item descriptions - user profiles - context • Continuous cold start = information continuously missing → Need more/other information: - Popularity → can add friction → weak signal
  25. 25. Booking.com Destination Finder
  26. 26. Booking.com Destination Finder
  27. 27. Booking.com Endorsements • From users who stayed at hotel in destination • Free text endorsements since 2013 • 2014: used NLP techniques to extract 256 tags • 13 000 unique endorsements • 60 000 destinations
  28. 28. Booking.com Endorsements
  29. 29. Booking.com Endorsements
  30. 30. Booking.com Destination Finder Cold Start • Upper funnel: almost no information about user (very cold) • Construct MIXED profile: Features: - user: operating system, browser - context: weekday - item rating: endorsement <Ubuntu, Firefox, Tuesday, opera> <0,1,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,0...> • Train recommender for each profile • New user + query: find closest profile • Clustering → mixed content profiles
  31. 31. Booking.com A/B testing Recommender Users Click-Through-Rate No mixed profiles 13,306 18.5 ± 0.4% With mixed profiles 13,562 22.2 ± 0.4% Improved by 20%
  32. 32. Booking.com - Implicit ratings: buys, clicks, views... Addressing Continuous Cold Start - Content - Ask user → surprisingly hard to beat! → not personalized - item descriptions - user profiles - context • Continuous cold start = information continuously missing → Need more/other information: - Popularity → can add friction → weak signal → mix ‘em up! Not only bridge warm-up period, but deal with continuous cold start
  33. 33. Booking.com • Classical cold-start new & rare users / items Summary: Continuous Cold Start • Volatility users/items change over time • Personas different interest at close-by times • Identity failure to match data from same user/item
  34. 34. Booking.com Timely Personalized Recommendation: Don’t do lunch cold start

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