Journal of Intelligent Information Systems

, Volume 23, Issue 2, pp 107–143 | Cite as

Recommender Systems Research: A Connection-Centric Survey

  • Saverio Perugini
  • Marcos André Gonçalves
  • Edward A. Fox
Article

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.

recommendation recommender systems small-worlds social networks user modeling 

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Copyright information

© Kluwer Academic Publishers 2004

Authors and Affiliations

  • Saverio Perugini
    • 1
  • Marcos André Gonçalves
    • 1
  • Edward A. Fox
    • 1
  1. 1.Department of Computer ScienceVirginia TechBlacksburg

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