Improving Neighborhood-Based Collaborative Filtering by Reducing Hubness
Peter Knees, Dominik Schnitzer, Arthur Flexer · 2014
For recommending multimedia items, collaborative filtering (CF) denotes the technique of automatically predicting a user's rating or preference for an item by exploiting item preferences of a (large) group of other users. In traditional memory-based (or neighborhood-based) recommenders, this is accomplished by, first, selecting a number of similar users (or items) and, second, combining their ratings into a single user's predicted rating for an item. Strategies for both defining similarity (i.e., to identify nearest neighbors) and for combining ratings (i.e., to weight their impact) have been extensively studied and even resulted in inconsistent findings.