A Trust-Based Collaborative Filtering Approach to Design Recommender Systems

Collaborative Filtering (CF) is one of the most frequently used recommendation techniques to design recommender systems that improve accuracy in terms of recommendation, coverage, and rating prediction. Although CF is a well-established and popular algorithm, it suffers with issues like black-box recommendation, data sparsity, cold-start, and limited content problems that hamper its performance. Moreover, CF is fragile and it is not suitable to find similar users. The existing literatures on CF show that integrating users' social information with a recommender system can handle the above-mentioned issues effectively. Recently, trustworthiness among users is considered as one such social information that has been successfully combined with CF to predict ratings of the unrated items. In this paper, we propose a trust-based recommender system, TrustRER, which integrates users' trusts into an existing user-based CF algorithm for rating prediction. It uses both ratings and textual information of the items to generate a trust network for users and derives the trust scores. For trust score, we have defined three novel trust statements based on user rating values, emotion values, and review helpfulness votes. To generate a trust network, we have used trust propagation metrics to compute trust scores between those users who are not directly connected. The proposed TrustRER is experimentally evaluated over three datasets related to movie, music, and hotel and restaurant domains, and it performs significantly better in comparison to nine standard baselines and one state-of-the-art recommendation method. TrustRER is also able to effectively deal with the cold-start problem because it improves the rating prediction accuracy for cold-start users in comparison to baselines and state-of-the-art method.