Recommendation as Classification: Using Social and Content-Based Information in Recommendation

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 or 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. Introduction Recommendations are a part of everyday life. We usually rely on some external knowledge to make informed decisions about a particular artifact or action, for instance when we are going to see a movie or going to see a doctor. This knowledge can be derived from social processes. At other times, our judgments may be based on available information about an artifact and our known preferences. There are many factors which may influence a person in making choices, and ideally one would like to model as many of these factors as possible in a recommendation system. There are some general approaches to this problem. In one approach, the user of the system provides ratings of some artifacts or items. The system makes informed guesses about other items the user may like based on ratings other users have provided. This is the framework for social-filtering methods (Hill, Stead, Rosenstein Furnas 1995; Shardanand & Maes 1995). In a second approach, the system accepts information describing the nature of an item, and based on a sample of the user’s preferences, learns to predict which items the user will like (Lang 1995; Pazzani, Muramatsu, & Billsus 1996). We will call this approach content-based filtering, as it does not rely on social information (in the form of other users’ ratings). Both social and content-based filtering can be cast as learning problems: the objective is to *Department of Computer Science, Rutgers University, Piscataway, NJ 08855 We would like to thank Susan Dumais for useful discussions during the early stages of this work. Copyright ~)1998, American Association for Artificial Intelligence (www.aaai.org). All rights reserved. learn a function that can take a description of a user and an artifact and predict the user’s preferences concerning the artifact. Well-known recommendation systems like Recommender (Hill, Stead, Rosenstein & Furnas 1995) and Firefly (http: //www.firefly.net) (Shardanand & Maes 1995) are based on social-filtering principles. Recommender, the baseline system used in the work reported here, recommends as yet unseen movies to a user based on his prior ratings of movies and their similarity to the ratings of other users. Social-filtering systems perform well using only numeric assessments of worth, i.e., ratings. However, social-filtering methods leave open the question of what role content can play in the recommen-