A Collaborative Filtering Recommendation Algorithm Based on Iterative Bidirectional Clustering
Xiaoyong Xiong · Zhongwen xinxi xuebao · 2008
Collaborative filtering is widely applied in E-Commerce recommendation system.However,data sparcity affects the accuracy of prediction and results in poor recommendation.To address this problem,a novel collaborative filtering algorithm is presented based on the iterative bidirectional clustering method.It works on the initial user clusters and the item clusters,adjusting the two groups of clusters into the stable status by the cross iteration so that the distances within the cluster are much smaller whereas the distances between the clusters are even bigger.The experiments illustrate that the adjusted clusters facilitate a more accurate neighbor search,indicating an efficient solution to the data sparcity and better recommendation quality.