Open AccessRecommendation in the Social Web
Author(s) -
Burke Robin,
Gemmell Jonathan,
Hotho Andreas,
Jäschke Robert
Publication year - 2011
Publication title -
ai magazine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.597
H-Index - 79
eISSN - 2371-9621
pISSN - 0738-4602
DOI - 10.1609/aimag.v32i3.2373
Subject(s) - recommender system , world wide web , computer science , presentation (obstetrics) , social web , the internet , diversity (politics) , social media , multimedia , data science , medicine , sociology , anthropology , radiology
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.