Recommendation as classification: using social and content-based information in recommendation

TLDR

Recommendation systems suggest artifacts to users, often using social filtering based on ratings, but these methods typically ignore rich artifact attributes such as cast lists or reviews. This study proposes an inductive learning approach that incorporates both ratings and artifact attributes to predict user preferences. The approach integrates rating data with content-based features through inductive learning to generate personalized recommendations. On a dataset of over 45,000 movie ratings from more than 250 users, the method outperformed a conventional social‑filtering baseline.

Abstract

Recommendation systems make suggestions about artifacts to a user. For instance, they may predict whether a user would be interested in seeing a particular movie. Social recomendation methods collect ratings of artifacts from many individuals, and use nearest-neighbor techniques to make recommendations to a user concerning new artifacts. However, these methods do not use the significant amount of other information that is often available about the nature of each artifact - such as cast lists o r movie reviews, for example. This paper presents an inductive learning approach to recommendation that is able to use both ratings information and other forms of information about each artifact in predicting user preferences. We show that our method outperforms an existing social-filtering method in the domain of movie recommendations on a dataset of more than 45,000 movie ratings collected from a community of over 250 users.