Journal of Computer Science 9 (8): 1008-1018, 2013 ISSN: 1549-3636 © 2013 Science Publications doi:10.3844/jcssp.2013.1008.1018 Published Online 9 (8) 2013 (http://www.thescipub.com/jcs.toc) Corresponding Author: Norwati Mustapha, Department of Computer Science, Faculty of Computer Science and Information Technology, University Putra Malaysia, Serdang, Malaysia 1008 Science Publications JCS USER RECOMMENDATION ALGORITHM IN SOCIAL TAGGING SYSTEM BASED ON HYBRID USER TRUST Norwati Mustapha, Wong Pei Voon and Nasir Sulaiman Department of Computer Science, Faculty of Computer Science and Information Technology, University Putra Malaysia, Serdang, Malaysia Received 2013-02-21, Revised 2013-06-13; Accepted 2013-07-05 ABSTRACT With the rapid growth of web 2.0 technologies, tagging become much more important today to facilitate personal organization and also provide a possibility for users to search information or discover new things with Collaborative Tagging Systems. However, the simplistic and user-centered design of this kind of systems cause the task of finding personally interesting users is becoming quite out of reach for the common user. Collaborative Filtering (CF) seems to be the most popular technique in recommender systems to deal with information overload issue but CF suffers from accuracy limitation. This is because CF always been at- tack by malicious users that will make it suffers in finding the truly interesting users. With this problem in mind, this study proposes a hybrid User Trust method to enhance CF in order to increase accuracy of user recommendation in social tagging system. This method is a combination of developing trust network based on user interest similarity and trust network from social network analysis. The user interest similarity is de- rived from personalized user tagging information. The hybrid User Trust method is able to find the most trusted users and selected as neighbours to generate recommendations. Experimental results show that the hybrid method outperforms the traditional CF algorithm. In addition, it indicated that the hybrid method give more accurate recommendation than the existing CF based on user trust. Keywords: User Trust, Tag, Collaborative Filtering 1. INTRODUCTION Collaborative Tagging Systems (Begelman et al., 2006; Gemmell et al., 2009a; 2009b; Hotho et al., 2006; Shepitsen et al., 2008) allow users to label digital resources using free-form of keywords (tags). The simplistic and the user-centered design of this kind of systems have encouraged many Web users to annotate their data using tags (Begelman et al., 2006; Gemmell et al., 2009a; 2009b; Hotho et al., 2006; Shepitsen et al., 2008). Collaborative Tagging Systems allow users to explore other users’ bookmarks via the keywords and tracking users who bookmarked pages that you considered interesting (Begelman et al., 2006; Gemmell et al., 2009a; 2009b; Hotho et al., 2006; Shepitsen et al., 2008). Nonetheless, users will find it hard to search other users with similar interest within a social tagging system that have hundreds of thousands of user with users racking up tens of thousands of bookmarks. CF algorithm seems to be the most popular technique in recommender systems (Konstan et al., 1997; Resnick et al., 1994; Sarwar et al., 2000; Tso-Sutter et al., 2008) to deal with information overload issue. However, traditional CF algorithm focuses only on similar users’ opinions which express in ratings and do not consider the actual content of the items, which affected the quality of the recommendation. To improve recommendation quality, metadata such as content information in items and tags have been typically used as additional knowledge. In the past few years, the dramatic expanding of Web 2.0 Web sites and applications poses new challenges for traditional CF recommender systems. Traditional CF