Research On Recommendation System Based Weighted Similarity And Network Structure

With the development of information technology and the popularity of Internet, the web site has accumulated a growing number of information. This situation has made it hard for consumers to find the services and products they needed. As an important tool to solve this problem, recommendation systems can help consumers decide which products to purchase. Recommendation systems can improve E-Commerce sales by converting browsers into buyers, at the same time improving user loyalty and increasing cross-sell. Recommendation systems as a bridge between users and producers of information achieve a win-win situation. This article studies and explores the application of similarity calculation model and recommendation algorithm in recommendation systems. The findings and the contributions of this paper are given as follows:1. Rating values represented by fuzzy logic. Numerical scores to characterize the degree of user’s preference is vague, so the fuzzy logic is proposed. By the definition of fuzzy sets and membership functions, a methodology for fuzzifying rating values and rating deviation values is proposed. Fuzzy weightings for the traditional similarity measures of CF is presented. The experimental results show that the new method has better precision than the old one.2. The research on real-time requirement of recommendation system. Due to the magnitudes and scales of users’ interest is changing. This situation make it difficult to satisfy the quality of recommendation systems. The time decay function is added to address and solve this issue effectively. Considering the user data is large, the clustering way is introduced to CF. Finally, the FTKUBCF algorithm is presented. The experiemental results reveal that this algorithm can efficiently improve the real-time requirement.3. Weighted resource allocation algorithm. Traditional resource allocation algorithm in the resource allocation process, the user to all items have the same preference, without considering how much the user prefer to the item. This dissertation improve the traditional resource allocation algorithm, by adding user’s preferences to resource allocation algorithm. Compared with some traditional algorithms, the experimental result show that the proposed algorithm is feasible and effective.