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 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...
Keywords
Computer scienceArtifact (error)Recommender systemCollaborative filteringInformation retrievalSocial mediaDomain (mathematical analysis)Artificial intelligenceWorld Wide Web
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Publication Info
- Year
- 1998
- Type
- article
- Pages
- 714-720
- Citations
- 955
- Access
- Closed
External Links
Citation Metrics
955
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Cite This
Chumki Basu,
Haym Hirsh,
William W. Cohen
(1998).
Recommendation as classification: using social and content-based information in recommendation.
, 714-720.