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Indigo Recommendations Project

Arnold Binas, Laurent Charlin, Alex Levinshtein, Maksims Volkovs Artificial Intelligence Group University of Toronto. Indigo Recommendations Project. Presentation Outline. Project Introduction Approaches Results Recommendation Visualization

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Indigo Recommendations Project

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  1. Arnold Binas, Laurent Charlin, Alex Levinshtein, Maksims Volkovs Artificial Intelligence Group University of Toronto Indigo Recommendations Project

  2. Presentation Outline • Project Introduction • Approaches • Results • Recommendation • Visualization • What’s the future of recommendations at Indigo?

  3. Project Goals Book recommendation Friend recommendation 1 2 3 1 2 3

  4. Input Data (I) ? ? ? ? ? Book ratings from chapters.indigo.ca’s members

  5. Input Data (II) Member’s purchase history Book information (e.g.: category) User community User information Virtual Bookshelves Reviews Recommendations Top-ten lists Existing friends

  6. Presentation Outline • Project Introduction • Approaches • Results • Recommendation • Visualization • What’s the future of recommendations at Indigo…

  7. Challenge #1 – People rate what they enjoy

  8. Challenge #2 – Many books, many users, but not many ratings

  9. Challenge #3 • Which information source should one use to make recommendations? Ratings? Shelves? Purchase History? • Purchase/Shelves history • Ratings are the most useful and expressive feedback from users • Reviews might contain more useful information but having a computer understand them is difficult.

  10. ? ? ? ? ? Book recommendations - Approach • Goal: Predict ratings for books that have not been rated • Output the highest rated books

  11. ? Rating prediction • Method: Collaborative Filtering a Machine learning techniqueUsers with similar tastes agree on new products

  12. Collaborative Filtering - Under the scenes UserDescriptor BookDescriptor I like 19th century Novels, Political Biography

  13. Modeling Ratings Given known ratings, learn: • User descriptors • Book descriptors

  14. Recommending books • Predict the ratings • Output n-highest rated books 1 2 3

  15. Evaluation (Book Recommendation) • Difference between known and predicted ratings • High ratings for purchased / shelf books

  16. Friend recommendations - approach • Recommend people with similar interests • Rate users based on interest similarity • Output the highest rated users • Similar ratings for books Similar interests • Conclusion – friend recommendations rely on rating prediction

  17. Recall: Modeling Ratings Given known ratings, learn: • User descriptors • Book descriptors Use these for user comparison

  18. Recommending Friends 1 2 3 • Compare users • Output similar users Similar ?

  19. Evaluation (Friend Recommendation) • Performance on a manually ranked group of users • High user ranking implies similar tastes in books • Agreement in book rankings • Agreement in purchased books • Agreement in book shelves

  20. Presentation Outline • Project Introduction • Approaches • Results • Recommendation • Visualization • What’s the future of recommendations at Indigo?

  21. Presentation Outline • Project Introduction • Approaches • Results • Recommendation • Visualization • What’s the future of recommendations at Indigo?

  22. Our recommendation  • Run A/B testing • Figure out which members benefit from recommendations. • Get more ratings.

  23. Indigo’s data at a glance • ~300K ratings • ~26K users (with at least one rating) • ~87K products (with at least one rating)

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