Research And Application On Collaborative Filtering Algorithm In E-Commerce Personalized Recommendation System

Along with the development and perfection of network technology, E-Commerce has covered the whole marketing network and become a new channel in business communication. For better user experience, Personalized Recommendation Systems gradually rise. It is used to analysis user's historical records, mine user's interest; provide prediction and recommendation to the target user. Collaborative Filtering algorithm is one of the common recommendation technologies, but with the development and application of the Personalized Recommendation System, these problems, such as sparsity, cold start, expansibility etc, have become bottlenecks in recommendation.Through the in-depth research and analysis, this paper presents the improved algorithm based on the traditional Cooperative Filtering. The method is that firstly on basis of Concept Hierarchy, adopt the concept of cluster rated degree, reduce user-item score matrix, get the smaller candidate matrix to improve recommendation scalability; Secondly, these concepts "Item Objective Character" and "Interest Deviation Degree" are introduced to fill the sparse candidate matrix more reasonably, alleviate the impact of data sparsity and cold start; Finally, in prediction, the average of rating to itemset is used to measure user's rating habit and a new recommend formula based on itemset's rating is put forward to improve the prediction accuracy.Through experiments and the test on evaluation rules, the improved algorithm is proved to be better than the traditional collaborative filtering algorithm in recommendation. Finally, realize a book recommendation system as the typical case of the Recommendation Systems for E-Commerce, the improved cooperative filtering algorithm is used in the service of book recommendation, and the practicality of improved algorithm is approved.