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Research Of Matrix Factorization Recommendation Model Based On Trust Relationship

Posted on:2018-05-06Degree:MasterType:Thesis
Country:ChinaCandidate:Y ChenFull Text:PDF
GTID:2348330518499186Subject:Computer Science and Technology
Abstract/Summary:PDF Full Text Request
The rapid development of information technology and the Internet has brought people into the information age, the rapid growth of information is a significant feature of this era. In the face of a massive amount of data, users frequently can not obtain information that is useful to them quickly and efficiently, which is the "information overload" problem in the era of big data. How to filter out valuable information from a large amount of information has become a research hotspot in related fields and an urgent problem which needs to be solved. The emergence of search engines and recommendation systems has solved the problem of"information overload" to a certain extent. In the face of growing information on the Internet,search engines that passively answer user queries can no longer fully meet the needs of the vast numbers of users. Unlike the passive response mode of the search engine, recommendation system can actively recommend the items that users may most interest in to the users according to the user's interest, behavior, scenario and other relevant information through some relevant recommendation algorithm, so it has been widely studied and achieved great development.Collaborative filtering recommendation algorithm plays an important role in the developing process of the recommended system, and it has been intensively studied and widely used in the field of recommendation system. But traditional collaborative filtering recommendation algorithms are faced with such inevitable problems as data sparseness and cold-start, these problems seriously affect the quality of recommendation, how to solve the problems of traditional collaborative filtering algorithm effectively has become the focus of research in the correlation domain. In recent years, with the rapid development of social networks, a large number of social information data also has been generating, therefore,some researchers suggest that we should fully exploit the potential value of social information,so that we can use it to improve recommendation algorithm and make it better. As an important form of social information, trust information has been introduced into recommendation algorithms, and many trust-based recommendation algorithms have been proposed. These algorithms alleviate the data sparseness and cold-start problems at a different angle and improve the recommendation effect. However, there are some problems in these algorithms. In this paper, the following two aspects of work are done to further improve the effect of the recommendation algorithm.Firstly, for the rating prediction task of recommender systems, this article researches a novel rating prediction model, termed TMFSVD, this model is based on the the SVD++ model and TrustMF model, TMFSVD model makes the best of the advantages of SVD++ model and the TrustMF model,TMFSVD model bridges ratings information and user trust relationships together through matrix factorization technique and incorporates both the explicit and implicit influence of user-item ratings and user trust relationship on the prediction of items for an active user. Experimental results on the three widely used real data sets demonstrate that TMFSVD model can reduce the data sparsity and cold start problems effectively, and it can improve the accuracy of rating prediction.Secondly, for the Top-N recommendation task of recommender systems, this article researches a novel Top-N recommendation model, termed TrecRank, which based on the user trust relationships. TrecRank model bridges ratings information and user trust relationships together through matrix factorization technique and incorporates both the explicit and implicit influence of user-item ratings and user trust relationship on the Top-N recommendation of items for an active user. TrecRank model ranks the items in the predicted list through Plackett-Luce model, and adjusts the trust values between two users according to the number of outdegrees and indergrees of nodes in trust network. Experimental results on the three widely used real data sets demonstrate that TrecRank model can improve the accuracy of Top-N recommendation.
Keywords/Search Tags:Recommender systems, Collaborative filtering, Matrix factorization, Explicit and implicit feedback, Trust relationship, Top-N recommendation
PDF Full Text Request
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