Mixed Collaborative and Content-Based Filtering with User-Contributed Semantic Features

We describe a recommender system which uses a unique combination of content-based and collaborative methods to suggest items of interest to users, and also to learn and exploit item semantics. Recommender systems typically use techniques from collaborative filtering, in which proximity measures between users are formulated to generate recommendations, or content-based filtering, in which users are compared directly to items. Our approach uses similarity measures between users, but also directly measures the attributes of items that make them appealing to specific users. This can be used to directly make recommendations to users, but equally importantly it allows these recommendations to be justified. We introduce a method for predicting the preference of a user for a movie by estimating the user’s attitude toward features with which other users have described that movie. We show that this method allows for accurate recommendations for a sub-population of users, but not for the entire user population. We describe a hybrid approach in which a userspecific recommendation mechanism is learned and experimentally evaluated. It appears that such a recommender system can achieve significant improvements in accuracy over alternative methods, while also retaining other advantages.