PhD Defense: Dynamic Generation of Personalized Hybrid Recommender Systems
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Slides of the public PhD defense of Simon Dooms as presented on December 19, 2014 in Ghent, Belgium.
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PhD Defense: Dynamic Generation of Personalized Hybrid Recommender Systems
- 1. Dynamic Generation of Personalized Hybrid Recommender Systems Simon Dooms Public PhD Presentation December 19, 2014. Belgium.
- 2. News Movies RestaurantsHotelsBooks TV shows Clothes Apps Laptops CultureBars Recipes Comic books Perfume Conferences Websites GamesColors There’s too much of everything
- 3. Spotify’s music catalog contains 20 million songs Every minute 100 hours of video uploaded to YouTube Every 5 minutes a new book on Amazon We can’t have it all News Movies RestaurantsHotelsBooks TV shows Clothes Apps Laptops CultureBars Recipes Comic books Perfume Conferences Websites GamesColors
- 5. Recommender Systems Content-based FilteringItemAttributeKNN FactorWiseMatrixFactorization Matrixfactorization ItemKNN SigmoidCombinedAsymmetricFactorModel SigmoidItemAsymmetricFactorModel SigmoidUserAsymmetricFactorModel GlobalAverage ItemAverage SVDPlusPlus TimeAwareBaselineWithFrequencies SlopeOne UserKNN BiPolarSlopeOne LatentFeatureLogLinearModel SVD Collaborative Filtering BiasedMatrixFactorization Random Items SigmoidSVDPlusPlus CoClustering UserItemBaseline TimeAwareBaseline Probability-based Extended Profile Filtering
- 6. Recommender Systems Content-based FilteringItemAttributeKNN FactorWiseMatrixFactorization Matrixfactorization ItemKNN LatentFeatureLogLinearModel SVD Collaborative Filtering BiasedMatrixFactorization Random Items SigmoidSVDPlusPlus TimeAwareBaseline Probability-based Extended Profile Filtering
- 7. AanbevelingssystemenWhat’s the challenge? Content-based FilteringItemAttributeKNN FactorWiseMatrixFactorization Matrixfactorization ItemKNN LatentFeatureLogLinearModel SVD Collaborative Filtering BiasedMatrixFactorization Random Items SigmoidSVDPlusPlus TimeAwareBaseline Probability-based Extended Profile Filtering
- 8. How to select the best system for a given context, user or domain?
- 9. Goal Dynamic Generation of Personalized Hybrid Recommender Systems The title
- 10. Goal Dynamic Generation of Personalized Hybrid Recommender Systems
- 11. Goal Dynamic Generation of Personalized Hybrid Recommender Systems
- 12. Goal Dynamic Generation of Personalized Hybrid Recommender Systems
- 13. Goal Dynamic Generation of Personalized Hybrid Recommender Systems
- 14. Goal
- 16. Data MovieLens Netflix (Old) Data is available
- 17. Data MovieLens Netflix 1994 1995 1997 (Old) Data is available
- 18. Data Old Data Old Recommendations (Old) Data is available but…
- 20. Data Searching Recent Data
- 21. Data “I rated #IMDb” Found it!
- 22. Data Collected during 1 year, 9 months 320 000 ratings MovieTweetings Dataset 30 000 users 20 000 movies Found it!
- 23. Goal
- 24. Goal
- 27. + + + +
- 28. Personalized
- 34. Repeat Data Optimization Training Evaluate + = 25 = 50 = 75 Adapt Combine
- 36. Optimize Folddatasets Slow (hours) Alldata Fast (seconds) New Ratings: no re-training required
- 37. Optimization Results It works … in theory
- 39. User Evaluation Google Chrome Extension
- 44. Interaction Recent Movies Evaluation Explicit ImplicitClick Tracking & Logging Movie Recommendations
- 45. Movie Recommendations: Results Interactive users use the system more often All users are different Explicit & Implicit evaluation was positive
- 46. Goal Achieved
- 47. User Interface Design Experiments But wait … there’s more!
- 48. High-Performance Calculation Research User Interface Design Experiments But wait … there’s more!
- 49. High-Performance Calculation Research User Interface Design Experiments But wait … there’s more!
- 50. High-Performance Calculation Research User Interface Design Experiments But wait … there’s more!
- 51. Rating systems, online experiments High-Performance Calculation Research User Interface Design Experiments But wait … there’s more!
- 52. Conclusion
- 53. A dynamic, personalized hybrid recommender system works All users are unique Interactive systems are the future Conclusion
- 54. You might also like… My PhD dissertation for more details: Google Scholar for my publications Slideshare for my presentations and posters Github for my public code and data http://bit.ly/simonphd https://github.com/sidooms
- 55. That’s all folks! More questions? Contact me! https://twitter.com/sidooms simon [dot] dooms [at gmail dot] com
- 56. Dynamic Generation of Personalized Hybrid Recommender Systems Simon Dooms Public PhD Presentation December 19, 2014. Belgium.