The state-of-the-art in personalized recommender systems for social networking
Article
Article Title | The state-of-the-art in personalized recommender systems for social networking |
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ERA Journal ID | 17763 |
Article Category | Article |
Authors | Zhou, Xujuan (Author), Xu, Yue (Author), Li, Yuefeng (Author), Audun, Josang (Author) and Cox, Clive (Author) |
Journal Title | Artificial Intelligence Review |
Artificial Intelligence Review: an international survey and tutorial journal | |
Journal Citation | 37 (2), pp. 119-132 |
Number of Pages | 14 |
Year | 2012 |
Place of Publication | Netherlands |
ISSN | 0269-2821 |
1573-7462 | |
Digital Object Identifier (DOI) | https://doi.org/10.1007/s10462-011-9222-1 |
Web Address (URL) | http://link.springer.com/article/10.1007%2Fs10462-011-9222-1 |
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. |
Keywords | Social networking, Recommender systems, Trust, User profiles, User generated content |
ANZSRC Field of Research 2020 | 469999. Other information and computing sciences not elsewhere classified |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | Queensland University of Technology |
University of Oslo, Norway | |
Rummble.com, United Kingdom | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q390z/the-state-of-the-art-in-personalized-recommender-systems-for-social-networking
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