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.
Highlights
With similar interest within a social tagging system that have hundreds of thousands of user with users racking up Collaborative Tagging Systems
The hybrid User Trust (UserTrust) method is compared with the previous methods; the user-based collaborative filtering with Pearson Correlation Coefficient (PCC) (Resnick et al, 1994), Tidal Trust (TT) (Golbeck, 2006), UserRec (Zhou et al, 2010), tag-based Similarity Trust approach (ST) (Bhuiyan et al, 2010) and incorporation of social network information in Collaborative Filtering (CF) (PCC-SN) (Liu and Lee, 2010)
We have presented a hybrid User Trust method for user recommendation approach which allows users to find other users with similar interest in social tagging system
Summary
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). 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. Users would prefer to receive recommendations from people that they trust
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