A Connection-Centric Survey of Recommender Systems Research

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 underemphasized in the recommender systems literature. We therefore take a connection-oriented viewpoint toward recommender systems research. We posit that recommendation has an inherently social element and is ultimately intended to connect people either dir...

• Type: Journal
• Year: 2002
• Where: CORR
• Authors: Saverio Perugini, Marcos André Gonçalves, Edward A. Fox
• Keywords: Content-based Filtering Vs, CORR 2002, Education, Recommender Systems, Recommender Systems Research