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Recommender Systems Research: A Connection-centric Survey
Journal of Intelligent Information Systems
  • Saverio Perugini, University of Dayton
  • Marcos André Gonçalves, Virginia Polytechnic Institute and State University
  • Edward A. Fox, Virginia Polytechnic Institute and State University
Document Type
Publication Date

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
Document Version

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

Peer Reviewed
  • Recommendation,
  • recommender systems,
  • small-worlds,
  • social networks,
  • user modeling
Citation Information
Saverio Perugini, Marcos André Gonçalves and Edward A. Fox. "Recommender Systems Research: A Connection-centric Survey" Journal of Intelligent Information Systems Vol. 23 Iss. 2 (2004)
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