Recommendation as classification: using social and content-based information in recommendation
Chumki Basu, Haym Hirsh, William W. Cohen · 1998
Recommendation systems make suggestions about arti-facts 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 arti-facts from many individuals and use nearest-neighbor techniques to make recommendations to a user concern-ing new artifacts. However, these methods do not use the significant amount of other information that is of-ten available about the nature of each artifact-- such as cast lists or movie reviews, for example. This paper presents an inductive learning approach to recommen-dation that is able to use both ratings information and other forms of information about each artifact in pre-dicting 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.