A Study On Hybrid Recommender Model Of Collaborative Filtering And Association Rules Based On Trust Mechanism

With the development of Internet technology,social network,e-commerce and a variety of applications are flourishing,but massive data information has caused information overload problems while satisfying users' needs.It is increasingly difficult to find effective and reasonable information quickly.As one of the means to solve the problem of information overload,personalized recommendation technology can analyze the users' interest preference according to numerous historical interactions and provide users with information that they may be interested in,so it can help users make decisions.As one of the most successful and widely used recommendation technologies,collaborative filtering technology still faces problems such as data sparsity and cold start.The development of social network brings new direction to the recommendation technology.Introducing the friend relationship in the social network into the traditional collaborative filtering method and measuring through the trust mechanism,can improve the data sparsity effectively and improve the recommendation precision.lt has become one of the important directions in the current research recommendation technology.In order to improve collaborative filtering technology,this paper aims to solve the problems such as data sparsity,cold start and improve the recommendation accuracy of the recommendation system.Firstly,the trust mechanism in social nteworks is introduced into the collaborative filtering technology,which means“A friend trusted by the user can bring a positive recommendation result to the user,which is a favorable result that can be judged after consideration of the interaction history.".The indirect trust is fully calculated to fill the trust matrix on the basis of user-iten rating matrix and trust matrix.Finally,a collaborative filtering recommendation model based on trust mechanism is proposed through the comparison of three prediction methods based on user similarity and trust degree.In order to further optimize this method,the user interest preference and association rule algorithm are merged successively.The former filters the friends of the target users through the similarity of interests between users furtherly.The latter mines the potential correlation between the projects and then mixes with collaborative filtering.lt can solve the new user recommendation problem effectively.In addition,the recommended models proposed in this paper are all experimentally studied on the FilmTrust website public dataset,and the results show that this method can effectively solve the cold start problem and improve the recommendation accuracy.In the end,the shortcomings of the proposed model and the direction of future improvement are described.