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
Copyright
Copyright © 2004, Springer
Publisher
Springer
Volume
23
Peer Reviewed
yes
Issue
2
Keywords
Recommendation, recommender systems, small-worlds, social networks, user modeling
eCommons Citation
Perugini, Saverio; Gonçalves, Marcos André; and Fox, Edward A., "Recommender Systems Research: A Connection-centric Survey" (2004). Computer Science Faculty Publications. 34.
https://ecommons.udayton.edu/cps_fac_pub/34
Included in
Databases and Information Systems Commons, Graphics and Human Computer Interfaces Commons, Information Security Commons, Numerical Analysis and Scientific Computing Commons, OS and Networks Commons, Other Computer Sciences Commons, Theory and Algorithms Commons
Comments
Paper in repository is the version accepted for publication in the Journal of Intelligent Information Systems. The final publication is available online.