Research On Approaches Based On Users’ Weighted Trust Relations And The Rating Similarities

Recently, the rapid popularization of Internet and e-commerce not only provide new development chance for the enterprises, but also put forward some challenges, such as "Information overload". Facing exponentially increasing web information, it becomes more and more difficult for people to find out the information they need. As a kind of personalized service, recommender systems can provide different recommendation based on users’ different characteristics to avoid users inundated with choices. Due to their great commercial value, recommendation techniques have been widely adopted in e-commerce.Many techniques have been proposed to make recommendations for users, among which Collaborative Filtering (CF) is one of the most widely used approaches. CF is a technique that can automatically provide recommendations for the target user by collecting information in the form of ratings from other similar users or items. Although it has gained widespread attention, CF has some inherent disadvantages, such as sparsity problem, cold start problem and so on. With the growing popularity of open social networks, approaches incorporating social relationship into recommender systems are gaining momentum. This kind of methods assumes that users are more likely to be influenced by the users they trust in the social network, incorporating the social relation into traditional recommender systems. They can overcome these problems effectively and has become an important research project.Most existing successful social recommendation methods only use the explicit trust relation, which is far from enough. Some other kind of relationships can be exploited, such as the similarity relationship hidden behind users’ behavior. Besides, only few online recommender systems, like Epinions, have the implementation of trust mechanism and the trust values between users are binary. Based on existing binary trust network, these methods assume that different friends trusted by the same user affect the user in the same degree. It is not consistent with the reality.Aiming at solving the problems mentioned above, on the basis of the previous work, we propose a novel recommendation approach based on users’ weighted trust relations and the rating similarities. First, we assign different weights on the social trust relationships among users based on the trustee’s competence and trustworthiness; Secondly, we incorporates the similarity relationships among users as a complement into the social trust relationships to enhance the computation of user’s neighborhood; When generating recommendations, we balance the influence of these two kinds of relationships based on user’s individuality adaptively.The main works in this thesis are as follows:First of all, we analysis the research background and status of personalized recommender systems. We also conclude and analysis the problems of personalized recommender systems.Besides, we conclude and analysis the related technologies of personalized service and recommender systems, especially the Collaborative Filtering. We also analysis the advantages and disadvantages of social recommendation methods, which have been proven very effective.Finally, by modeling the binary trust network and incorporating the similarity relationship, we propose our method and evaluate it on the real datasets Epinions and Ciao for recommendation accuracy. The experiment results show that our proposed method outperforms the state-of-the-art algorithms in improving the prediction accuracy and can alleviate the impact of the sparsity and cold start problems.