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

Abstract:

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 user-specific
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.