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
Indigo Recommendations Project
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Presentation Transcript
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 • What’s the future of recommendations at Indigo?
Project Goals Book recommendation Friend recommendation 1 2 3 1 2 3
Input Data (I) ? ? ? ? ? Book ratings from chapters.indigo.ca’s members
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
Presentation Outline • Project Introduction • Approaches • Results • Recommendation • Visualization • What’s the future of recommendations at Indigo…
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.
? ? ? ? ? Book recommendations - Approach • Goal: Predict ratings for books that have not been rated • Output the highest rated books
? Rating prediction • Method: Collaborative Filtering a Machine learning techniqueUsers with similar tastes agree on new products
Collaborative Filtering - Under the scenes UserDescriptor BookDescriptor I like 19th century Novels, Political Biography
Modeling Ratings Given known ratings, learn: • User descriptors • Book descriptors
Recommending books • Predict the ratings • Output n-highest rated books 1 2 3
Evaluation (Book Recommendation) • Difference between known and predicted ratings • High ratings for purchased / shelf books
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
Recall: Modeling Ratings Given known ratings, learn: • User descriptors • Book descriptors Use these for user comparison
Recommending Friends 1 2 3 • Compare users • Output similar users Similar ?
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
Presentation Outline • Project Introduction • Approaches • Results • Recommendation • Visualization • What’s the future of recommendations at Indigo?
Presentation Outline • Project Introduction • Approaches • Results • Recommendation • Visualization • What’s the future of recommendations at Indigo?
Our recommendation • Run A/B testing • Figure out which members benefit from recommendations. • Get more ratings.
Indigo’s data at a glance • ~300K ratings • ~26K users (with at least one rating) • ~87K products (with at least one rating)
