Lecture-E7

Lecture-E7 - Lecture E Collaborative Filtering via...

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Lecture E Collaborative Filtering via Euclidean Embedding M. Khoshneshin and W. Street Proc. of ACM RecSys, pp. 87-94, 2010
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Introduction Recommendation Systems Suggest items based on user preferences Recommendation Approaches: Content-based Items are recommended based on a user profile and product information Collaborative Filtering Use similarity to recommend items that were liked by similar users, i.e., recommendation is based on the rating history of the system Predict unknown ratings so that users can be given suggestions based on items with a high expected rating 2
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Challenges Existing approaches are more adequate for static settings Incorporating new data to this models is not a trivial task Recommendations are based on the best predicted ratings However, predicting ratings is very computationally expensive in large databases Euclidean embedded (EE) method for collaborative filtering Users and items are embedded in a unified Euclidean space The distance between a user and an item is inversely proportional to the rating 3 Solution
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Euclidean Embedding Advantages of EE Is more intuitively understandable for human allowing useful visualizations Allows very efficient recommendation queries Facilitates online implementation requirements 4
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Lecture-E7 - Lecture E Collaborative Filtering via...

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