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

Xujuan Zhou, Yue Xu, Yuefeng Li, Audun Josang, Clive Cox

Research output: Contribution to journalArticlepeer-review

135 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)119-132
Number of pages14
JournalArtificial Intelligence Review
Issue number2
Publication statusPublished - 2012
Externally publishedYes


  • Social networking
  • Recommender systems
  • Trust
  • User profiles
  • User generated content


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