Journal
JOURNAL OF INTELLIGENT INFORMATION SYSTEMS
Volume 23, Issue 2, Pages 107-143Publisher
SPRINGER
DOI: 10.1023/B:JIIS.0000039532.05533.99
Keywords
recommendation; recommender systems; small-worlds; social networks; user modeling
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available