Research On Collaborative Filtering Based On Rating Prediction And Probabilistic Fusion

With the widespread of network and the development of network technologies, e-commerce has been popularized and development in a global context because of its advantages such as low cost, fast, free from constraints of time and space. But in this virtual enviorment, more and more types and amounts of products are provided by businesses, and from a practical experience, the requirements of users are often unclear, vague. They may have potential demand for certain products, but it is not clear what products would meet their fuzzy needs, so how to give the customers personalized recommendation about the products, how to turn the e-commerce website browsers to buyers ,how to enhance the ability of cross-sell of e-commerce website and the customers loyalty on e-commerce websites, making enterprises maximize their profits have become an important isuue on e-commerce. Recommerder system is combined with e-commerce in such enviroment. Collaborative filtering is the earlist and most successive personalized recommendation technology in the e-commerce recommender systems. Yet, with the continues increment of the structure of website,the complexity of contents, the amounts of products,the types of products and the amount of customers, the development of collaborative filtering technology of recommerder systems faces serious challenges such as poor recommendation quality and scalability. Facing these challenges, on the issues of how to improve the quality of users recommendation information and how to enhance the scalability of collaborative filtering algorithms, some researches have been done in domestic and foreign, and the research results have been successfully applied in practice.This paper briefly introduced background of the e-commerce recommender system at first. It illustrates the recommender system concept, effect, and popular methods in detail. In the recommendation technology, collaborative filetering not only provides new ideas for improving serve quality of recommender systems, but also is applied widly and successfully in many commencial website. It introduces colloaborative filtering basic ideas, theory start, implementation and its two directions. Also it gives emphasis to analyzing the probelmes which collaborative filtering is facing when its applied in recommender systems and existing improved methods. The paper proposes new methods to solve those problems. Based on the user-based and item-based collaborative filering algorithms, the new method introduces probabilistic fusion framework to fuse these two algorithms. Thus, on one hand, it increases the available data during recommending, reduces the effectness of data sparsity, and enhance the recommendation quality to a certain extent. On the other hand, for data extrem sparsity problem, BP neural network is introduced to predict the values of the null ratings, alleviate this issue, and also improves the recommender systems precision to some extent. Furthermore, because of not concerning and use of items classification information in old algorithms, it makes the recommendation lack of personality so that it is difficult to adapt to the trend of e-commerce growing diversity and individuality of the current systems. For this issue, a scene-oriented approach is introduced. Thus, it not only solves the former problems, but also reduces the amount of computation when prediction using BP neural network and similar user computing complexity. It has some positive impact on recommendation quality. It also gives experiment results for proposed algorithnms in standard data sets, and the performance between the new method and the old one is compared and analyzed. Finally, we summarize on the paper, point out defects and the directions that will be further studied in the future.