A reliability-based recommendation method to improve trust-aware recommender systems. Issue 21 (30th November 2015) - Moradi, Parham; Ahmadian, Sajad
Highlights: We proposed a trust-based collaborative filtering method called RTCF. A trust based reliability measure is proposed to evaluate quality of predicted rates. A new mechanism is proposed to reconstruct the trust network. Experiments were performed on two real-world datasets. The results show that our method outperformed several state-of-the-art methods. Abstract: Recommender systems (RSs) are programs that apply knowledge discovery techniques to make personalized recommendations for user's information on the web. In online sharing communities or e-commerce sites, trust is an important mechanism to improve relationship among users. Trust-aware recommender systems are techniques to make use of trust statements and user personal data in social networks. The accuracy of ratings prediction in RSs is one of the most important problems. In this paper, a Reliability-based Trust-aware Collaborative Filtering (RTCF) method is proposed to improve the accuracy of the trust-aware recommender systems. In the proposed method first of all, the initial trust network of the active user is constructed by using combination of the similarity values and the trust statements. Then, an initial rate is predicted for an unrated item of the user. In the next step, a novel trust based reliability measure is proposed to evaluate the quality of the predicted rate. Then, a new mechanism is performed to reconstruct the trust network for those of the users with lower reliability value than a predefined threshold. Finally, the final rate of the unrated item is predicted based on the new trust network of the user. In other words, the proposed method provides a dynamic mechanism to construct trust network of the users based on the proposed reliability measure. Therefore, the proposed method leads to improve the reliability and also the accuracy of the predictions. Experimental results performed on two real-world datasets including; Epinions and Flixster, demonstrated that the proposed method achieved higher accuracy and also obtained reasonable user and rate coverage compared to several state-of-the-art recommender system methods.