An Improved Item-based Collaborative Filtering Recommendation System
Lan-jun YAO, Lihong Shang, Mi Zhou · DEStech Transactions on Computer Science and Engineering · 2017
Today E-commerce is very popular, recommendation systems are very widely applied to various sites [1]. However, there still remains many problems in current recommendation system to be resolved. Given the data sparse problem in the traditional collaborative filter algorithm, we will introduce the relationship of the trust between the items, and transmit the similarity throughout it. In this paper, Experiment shows that the accuracy and coverage rate of the algorithm have been improved.