Research On Trust-Based Collaborative Filtering Algorithm Of Recommendation System For E-Commerce

With the rapid development of Internet and e-commerce, information overloading becomes a serious problem for the e-commerce user. More and more product information generated in e-commerce makes it difficult for users to quickly find their favorite products. At the same time, e-commerce companies are faced with the problem that how to provide users with products in time to improve sales. Recommendation system is an effective way to solve such problem.Collaborative Filtering is one of the best and widely used technologies in recommendation system, it is also facing data sparsity, cold start, scalability and other issues.This article proposes a Trust-based Collaborative Filtering algorithm to solve the data sparsity problem of the traditional Collaborative Filtering algorithm and improve the recommendation accuracy. Trust can describe the relationship between users more accurately; trust propagation can connect users that originally without interaction, use trust as the supplement of similarity can alleviate the data sparse problem of traditional collaborative filtering recommendation algorithm and improve the prediction accuracy of recommendation system. This article designs3experiments using the classic data sets to verify the validity of the algorithm. Experiments show that Trust-based Collaborative Filtering algorithm can alleviate the data sparsity problem and improve the recommendation accuracy. At last, the article uses this algorithm in the design of recommendation module of an online bookstore for book recommendation.