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

Zhou, Xujuan and Xu, Yue and Li, Yuefeng and Audun, Josang and Cox, Clive (2012) The state-of-the-art in personalized recommender systems for social networking. Artificial Intelligence Review, 37 (2). pp. 119-132. ISSN 0269-2821

Abstract

With the explosion ofWeb 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.


Statistics for USQ ePrint 29634
Statistics for this ePrint Item
Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Files associated with this item cannot be displayed due to copyright restrictions.
Faculty / Department / School: Historic - Faculty of Business and Law - School of Management and Marketing
Date Deposited: 05 Sep 2016 05:19
Last Modified: 28 Sep 2016 02:41
Uncontrolled Keywords: Social networking, Recommender systems, Trust, User profiles, User generated content
Fields of Research : 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080199 Artificial Intelligence and Image Processing not elsewhere classified
Socio-Economic Objective: E Expanding Knowledge > 97 Expanding Knowledge > 970108 Expanding Knowledge in the Information and Computing Sciences
Identification Number or DOI: 10.1007/s10462-011-9222-1
URI: http://eprints.usq.edu.au/id/eprint/29634

Actions (login required)

View Item Archive Repository Staff Only