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

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

View at publisher

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.

Impact and interest:

54 citations in Scopus
Search Google Scholar™
34 citations in Web of Science®

Citation counts are sourced monthly from Scopus and Web of Science® citation databases.

These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.

Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.

Full-text downloads:

945 since deposited on 07 Mar 2013
104 in the past twelve months

Full-text downloads displays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.

ID Code: 57833
Item Type: Journal Article
Refereed: Yes
Keywords: Social networking, Recommender systems, Trust, User profiles, User generated content
DOI: 10.1007/s10462-011-9222-1
ISSN: 1573-7462
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Pattern Recognition and Data Mining (080109)
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Funding:
Copyright Owner: Copyright 2012 Springer
Copyright Statement: The original publication is available at SpringerLink
http://www.springerlink.com
Deposited On: 07 Mar 2013 02:02
Last Modified: 29 Jul 2014 06:44

Export: EndNote | Dublin Core | BibTeX

Repository Staff Only: item control page