چکیده (انگلیسی):

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 attack
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 derived
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.