Research On Collaborative Filtering Recommendation Algorithm Based On Quotient Space Model

With the rapid development of the information and network technology, the resources become more and more abundant. The information sources of multi channels make the information more convenient. The massive information is available to provide rich information, while the overload information have made people too difficult to choose the useful information.As one of the effective tool that can solve the information overload problem and provide the personalized service, recommender systems have received extensive attention in recent years. Collaborative filtering is one of the most widely used and successful methods for recommendation. Based on the collaborative filtering, the new algorithms are emerging in an endless stream. With the expansion of network scale, increase sharply in number of users/items and use of social media, the collaborative filtering faces several opportunities and challenges.Utilizing the though of human deal with complex problems, Quotient Space Theory highly abstract the problems into a triple (X,f, T). This theory utilizes the analysis method which is from coarse to fine to solve the problems step by step and has the great significance to the solution of complex problems.In this dissertation, the recommendation is expressed by quotient space triple.And it can improve the recommendation accuracy through granulation. Firstly, we give the general description and explanation of recommendation problem according to the quotient space theory. Then we make a research into the collaborative filtering partly according to the attribution, structure etc., and mainly researched the problems such as Data sparsity, real-time, robustness and the effect of social relation in recommender system. Given the corresponding description of quotient space model, we proposed the solving methods against the problems mentioned above from the granular computing perspective.The dissertation includes:1. Give the unitary definition of recommender systems based on quotient space theory.Utilizing the a triple (X,f,T) in quotient space theory, the recommender system can be considered as the system that the user ratings, the user relevant characters and the relations between users are respectively viewed as the domain: X, the attribute function: f, andthe structure of the elements in the domain:T. Thus the new model of recommender system based on the quotient space model with a core of usercan be built. Furthermore the model description of the basic collaborative filtering algorithms in the view of granular aregiven in the dissertation. Based on this model, we research and solve some concrete problems taking user reputation, the rating relations between users, and the social relations between users as attribution and structure.2. Research the effect of user reputation on various recommender systemsLatent factor model can be considered as the produce of generating two relatively smaller granules under a certain optimal rules and further synthesizing larger granule from the perspective of quotient space. We introduce the user reputation into the optimal rules which be used in different latent factor model, and investigate the effect of user reputation on various recommender systems.We proposed LFMrep that is the LFM model combined user reputation and SoRS that is PMF model based user reputation. And the user reputation can be obtained by iteratively learning from historical ratings, which has been introduced into normal recommender systems and social recommender systems as mentioned above in the dissertation. Experimental results show that our methods have improved in terms of prediction accuracy criteria by using the user reputation can effectively remove noise because of user less rigorous ratings in recommender systems. In the normal recommender system, it can improve the robustness and vulnerability to attacks of the system by weakening the attribution of high reputation user. And in social recommender system, under the insufficiency of training set, the recommendation accuracy can be maintained by introducing the user reputation.. In addition, we also proposed SrBug (Social Recommendation Based on User Reputation Granulation) which has advantage in online response with short recommendation list under no obvious change of accuracy.3. Give the method of user domain structure granulation in recommendation using the granularity idea.The similarity between users is viewed as the network structure relation with no weight character in recommendation. Using the granulation idea in quotient space theory, the user domain can be structured where the recommendation generated in. We proposed the CUCRA (Community-Based User Domain Model Collaborative Recommendation Algorithm), through modeling by ratings with no structure, mapping the rating relation network between users, and granulating the user domain by using community detection algorithm. Empirical results demonstrate that it can find user domain neighbor more efficiently by using the method of structural granulation. And it also has quick online response without losing accuracy. Furthermore we proposed the HGUCRA (Hierarchical Granulation-based User Domain Model Collaborative Recommendation) in order to optimize the user domain by hierarchical clustering methods. Related experiments show that the proposed model can obtain a better recommendation accuracy, on the meantime it also has the better online response.4. Define the social relation within "three degrees" and hierarchical structure by fuzzy equivalency relations, deeply mine the effect of social relation within "three degrees" on the recommendation accuracy.Selecting the social relation and similarity between users as the network structure with weight character in recommendation, we proposed the IRSubNet which incorporate the implicit relations based on context-aware subnet-work propagation for SoReg. According to the definition of fuzzy equivalency relations, it can be obtained hierarchical structure with step in network as cut set. The proposed method integrated implicit relations and explicit relations within "three degrees" into unified framework. In comparison with other algorithms, IRSubNet deeply mine the effect of social relation within "three degrees", define the social similarity between any two nodes in social network based on context-aware subnet-work, and emphasizes the effect of implicit relation on the recommendation accuracy. The experimental results shows that IRSubNet can obviously improve the recommendation accuracy on the two real datasets.