In view of the traditional collaborative filtering algorithm in E-Commerce system data sparseness and scalability issues

a hybrid user and item based personalized collaborative filtering recommender algorithm in E-Commerce was proposed. Combined with user based collaborative filtering and item based collaborative filtering

the algorithm uses the clustering technology to cluster twice

and can get better performance. Experiments results show that the algorithm is superior to other recommendation algorithms obviously in the aspect of recommender quality

precision and recall rate.