Document Type

Article

Publication Date

9-2004

Publication Source

Journal of Intelligent Information Systems

Abstract

Recommender systems attempt to reduce information overload and retain customers by selecting a subset of items from a universal set based on user preferences. While research in recommender systems grew out of information retrieval and filtering, the topic has steadily advanced into a legitimate and challenging research area of its own. Recommender systems have traditionally been studied from a content-based filtering vs. collaborative design perspective. Recommendations, however, are not delivered within a vacuum, but rather cast within an informal community of users and social context. Therefore, ultimately all recommender systems make connections among people and thus should be surveyed from such a perspective. This viewpoint is under-emphasized in the recommender systems literature. We therefore take a connection-oriented perspective toward recommender systems research. We posit that recommendation has an inherently social element and is ultimately intended to connect people either directly as a result of explicit user modeling or indirectly through the discovery of relationships implicit in extant data. Thus, recommender systems are characterized by how they model users to bring people together: explicitly or implicitly. Finally, user modeling and the connection-centric viewpoint raise broadening and social issues—such as evaluation, targeting, and privacy and trust—which we also briefly address.

Inclusive pages

107-143

ISBN/ISSN

0925-9902

Document Version

Postprint

Comments

Paper in repository is the version accepted for publication in the Journal of Intelligent Information Systems. The final publication is available online.

Publisher

Springer

Volume

23

Issue

2

Peer Reviewed

yes

Keywords

Recommendation, recommender systems, small-worlds, social networks, user modeling