A Novel Trust Evaluation Mechanism for Collaborative Filtering Recommender Systems

In online social networks (OSNs), high trust value entities play an important role in service recommendation when users inquire certain service. Generally, users in OSNs are more willing to choose those services recommended by high trust value entities. In fact, users may suffer from great loss of property once they accept some bad services provided by high trust value entities. However, current schemes do not consider this problem. Hence, we propose a scheme called RHT (recommendation from high trust value entities) to evaluate the trust degree of service recommended by high trust value entities. To be specific, there exist other users who provide their ratings to the service recommended by a high trust value entity, and RHT first selects the trusted ones from those users by computing the similarity between target user and them. Simultaneously, RHT also withstands malicious attacks during the trusted nodes selection. In addition, we also design an adaptive trust computation method to calculate trust value according to the ratings of trusted users. The experimental results show that RHT has higher accuracy in trust evaluation compared with current representative schemes and do effectively resistant four common attacks when choosing trusted nodes.