协同过滤与基于内容的混合推荐算法研究
[Abstract]:The rapid development of network technology influences and changes the human life. The advancement of network technology is no longer what the network can give people, but how people experience life better on the network. This is the technology from simple to cumbersome, from extensive to fine transformation. The problem of information overload has affected the comfort of people's online life, and the birth of recommendation system has brought good news to meet the personal needs of people. Collaborative filtering and content-based recommendation algorithms are two major recommendation algorithms. Although they have been applied in different fields, there are still some problems such as weak adaptive ability and insufficient personalized recommendation ability. In addition, the two algorithms complement each other in their advantages and disadvantages. However, due to the difficulty of feature extraction of non-text items, the content-based recommendation algorithm is generally only used in the recommendation system of text items. In order to improve the recommendation quality of the recommendation algorithm, a hybrid recommendation algorithm is proposed to give full play to the advantages of both collaborative filtering and content-based recommendation algorithms. The main algorithm of this algorithm is the collaborative filtering algorithm. When the main algorithm finds trusted neighbors, it integrates the idea of content-based recommendation. Finally, the trusted neighbor collaborative recommendation is used. The innovation of the strategy includes: first, introducing project heat to optimize Pearson correlation coefficient. Secondly, the item label is taken as the attribute feature of non-text items, and the method of building two-dimensional interest model for users to measure the similarity of interest model is given. Thirdly, based on the structural features of the similarity formula of interest model, a method is proposed to solve the similarity weight coefficient by using variance. The final experiments show that the hybrid recommendation method improves the quality of the recommendation. It is an effective method and has the advantages of recommendation compared with the two existing hybrid strategies. Moreover, the effectiveness of each step optimization and calculation method in the hybrid algorithm model is verified by experiments. Because the project feature is extracted by the item label, the mixed recommendation of non-text item has a certain universality.
【学位授予单位】:天津财经大学
【学位级别】:硕士
【学位授予年份】:2016
【分类号】:TP391.3
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