Artificial Intelligence Review

, Volume 37, Issue 2, pp 119–132

The state-of-the-art in personalized recommender systems for social networking

Authors

    • Faculty of Science and TechnologyQueensland University of Technology
  • Yue Xu
    • Faculty of Science and TechnologyQueensland University of Technology
  • Yuefeng Li
    • Faculty of Science and TechnologyQueensland University of Technology
  • Audun Josang
    • UNIK Graduate CenterUniversity of Oslo
  • Clive Cox
    • Rummble.com
Article

DOI: 10.1007/s10462-011-9222-1

Cite this article as:
Zhou, X., Xu, Y., Li, Y. et al. Artif Intell Rev (2012) 37: 119. doi:10.1007/s10462-011-9222-1

Abstract

With the explosion of Web 2.0 application such as blogs, social and professional networks, and various other types of social media, the rich online information and various new sources of knowledge flood users and hence pose a great challenge in terms of information overload. It is critical to use intelligent agent software systems to assist users in finding the right information from an abundance of Web data. Recommender systems can help users deal with information overload problem efficiently by suggesting items (e.g., information and products) that match users’ personal interests. The recommender technology has been successfully employed in many applications such as recommending films, music, books, etc. The purpose of this report is to give an overview of existing technologies for building personalized recommender systems in social networking environment, to propose a research direction for addressing user profiling and cold start problems by exploiting user-generated content newly available in Web 2.0.

Keywords

Social networkingRecommender systemsTrustUser profilesUser generated content

Copyright information

© Springer Science+Business Media B.V. 2011