Collaborative Filtering for Recommender Systems
Collaborative filtering(CF)predicts user preferences in item selection based on the known user ratings of items.As one of the most common approach to recommender systems,CF has been proved to be effective for solving the information overload problem.CF can be divided into two main branches: memory-based and model-based.Most of the present researches improve the accuracy of Memory-based algorithms only by improving the similarity measures.But few researches focused on the prediction score models which we believe are more important than the similarity measures.The most well-known algorithm to modelbased is the matrix factorization.Compared to the memorybased algorithms,matrix factorization algorithm generally has higher accuracy.However,the matrix factorization may fall into local optimum in the learning process which leads to inadequate learning.CF approaches are usually designed to provide products to potential customers.Therefore the accuracy of the methods is crucial.In this paper,we propose various solutions to make a quality recommendation.First,we proposed a new prediction score model for the Memory-based method.Second,we proposed a differential model that considers the adjustment process after the training process in the existing matrix factorization methods.Third,a novel hybrid collaborative filtering is outlined to avoid or compensate for the shortcomings of matrix factorization and neighbor-based methods.In the end,we performed the experiments on MovieLens datasets and the results confirmed the effectiveness of our methods.
collaborative filtering recommender system matrix factorization neighbor-based hybrid
Rui-sheng Zhang Qi-dong Liu Chun-Gui Jia-Xuan Wei Huiyi-Ma
School of Information Science & Engineering Lanzhou University Lanzhou,China
国际会议
2014 2nd International Conference on Advanced Cloud and Big Data (CBD 2014)(2014年先进云计算和大数据国际会议)
安徽黄山
英文
301-308
2014-11-20（万方平台首次上网日期，不代表论文的发表时间）