Abstract：The personalized recommendation technology provide people an effective method to solve the problem of information overload.Collaborative filtering is one of the key algorithm of recommendation technology，In this paper，the Slope One algorithm is a kind of itembased collaborative filtering recommendation algorithm，However，it doesn′t take user similarity and item similarity into consideration when it works.Therefore，this article puts forward five new method to integrate user similarity and item similarity into weighted Slope One algorithm.Using trust and Jaccard to find the influential users，Pearson to find the similar items of current item，respectively.Experiments on the wellknown datasets Epinions and Movielens show that the algorithm weighted by Jaccard and Pearson in the case of sparse datasets and less neighbor achieves great improvement of prediction accuracy.
张玉连,郇思思,梁顺攀,. 融合用户相似度与项目相似度的加权Slope One算法[J]. 小型微型计算机系统, 2016, 37(6): 1174-1178.
ZHANG Yu-lian,HUAN Si-si,LIANG Shun-pan,. Integrating User Similarity and Item Similarity into Weighted Slope One Algorithm. Journal of Chinese Computer Systems, 2016, 37(6): 1174-1178.
2016, Vol. 37 
