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Recommender Systems
• Systems for recommending items (e.g. books, movies, CD’s, web pages, newsgroup messages) to users based on examples of their preferences.
• Many on-line stores provide recommendations (e.g. Amazon, CDNow).
• Recommenders have been shown to substantially increase sales at on-line stores.
• There are two basic approaches to recommending:
– Collaborative Filtering (a.k.a. social filtering)
– Content-based
Personalization
• Recommenders are instances of personalization software.
• Personalization concerns adapting to the individual needs, interests, and preferences of each user.
• Includes:
– Recommending
– Filtering
– Predicting (e.g. form or calendar appt. completion)
• From a business perspective, it is viewed as part of Customer Relationship Management (CRM).
Machine Learning and Personalization
• Machine Learning can allow learning a user model or profile of a particular user based on:
– Sample interaction
– Rated examples
• This model or profile can then be used to:
– Recommend items
– Filter information
Predict behavior
Collaborative Filtering
• Maintain a database of many users’ ratings of a variety of items.
• For a given user, find other similar users whose ratings strongly correlate with the current user.
• Recommend items rated highly by these similar users, but not rated by the current user.
• Almost all existing commercial recommenders use this approach (e.g. Amazon).
Collaborative Filtering Method
• Weight all users with respect to similarity with the active user.
• Select a subset of the users (neighbors) to use as predictors.
• Normalize ratings and compute a prediction from a weighted combination of the selected neighbors’ ratings.
• Present items with highest predicted ratings as recommendations.
Similarity Weighting
Typically use Pearson correlation coefficient between ratings for active user, a, and another user, u.
Neighbor Selection
• For a given active user, a, select correlated users to serve as source of predictions.
• Standard approach is to use the most similar n users, u, based on similarity weights, wa,u
• Alternate approach is to include all users whose similarity weight is above a given threshold.
Rating Prediction
• Predict a rating, pa,i, for each item i, for active user, a, by using the n selected neighbor users, u Î {1,2,…n}.
• To account for users different ratings levels, base predictions on differences from a user’s average rating.
• Weight users’ ratings contribution by their similarity to the active user.
Problems with Collaborative Filtering
• Cold Start: There needs to be enough other users already in the system to find a match.
• Sparsity: If there are many items to be recommended, even if there are many users, the user/ratings matrix is sparse, and it is hard to find users that have rated the same items.
• First Rater: Cannot recommend an item that has not been previously rated.
– New items
– Esoteric items
• Popularity Bias: Cannot recommend items to someone with unique tastes.
Tends to recommend popular items
Content-Based Recommending
• Recommendations are based on information on the content of items rather than on other users’ opinions.
• Uses a machine learning algorithm to induce a profile of the users preferences from examples based on a featural description of content.
• Some previous applications:
– Newsweeder (Lang, 1995)
– Syskill and Webert (Pazzani et al., 1996)
Advantages of Content-Based Approach
• No need for data on other users.
– No cold-start or sparsity problems.
• Able to recommend to users with unique tastes.
• Able to recommend new and unpopular items
– No first-rater problem.
• Can provide explanations of recommended items by listing content-features that caused an item to be recommended.
Disadvantages of Content-Based Method
• Requires content that can be encoded as meaningful features.
• Users’ tastes must be represented as a learnable function of these content features.
• Unable to exploit quality judgments of other users.
– Unless these are somehow included in the content features.
LIBRA
Learning Intelligent Book Recommending Agent
• Content-based recommender for books using information about titles extracted from Amazon.
• Uses information extraction from the web to organize text into fields:
– Author
– Title
– Editorial Reviews
– Customer Comments
– Subject terms
– Related authors
– Related titles