Recommendation in the Social Web

作者：Burke Robin^*; Gemmell Jonathan; Hotho Andreas; Jaeschke Robert

来源：AI Magazine, 2011, 32(3): 46-56.

DOI：10.1609/aimag.v32i3.2373

Recommender systems are a means of personalizing the presentation of information to ensure that users see the items most relevant to them. The social web has added new dimensions to the way people interact on the Internet, placing the emphasis on user-generated content. Users in social networks create photos, videos, and other artifacts, collaborate with other users, socialize with their friends, and share their opinions online. This outpouring of material has brought increased attention to recommender systems as a means of managing this vast universe of content. At the same time, the diversity and complexity of the data has meant new challenges for researchers in recommendation. This article describes the nature of recommendation research in social web applications and provides some illustrative examples of current research directions and techniques.

• 出版日期2011