A Graph Model for Recommender Systems
With the constant enlargement of the scope and coverage of the Internet,the traditional search algorithms just help to filter data,without considering the needs of individuals.Therefore,various recommender systems employing different data representations and recommendation methods are currently used to cope with these challenges.In this paper,inspired by the network-based user-item rating matrix,we introduce an improved algorithm which combines the similarity of items with a dynamic resource allocation process.To demonstrate its accuracy and usefulness,this paper compares the proposed algorithm with collaborative filtering algorithm using data from MovieLens,and finally verifies the results.The evaluation shows that,the improved recommendation algorithm based on graph model achieves more accurate predictions and more reasonable recommendation than collaborative filtering algorithm or the basic graph model algorithm does.Meanwhile,the algorithm can effectively mitigate the sparse of the rating matrix.
recommender systems graph model collaborative filtering resource allocation matrix
Hong Chen Mingxin Gan Mengzhao Song
Dongling School of Economics and Management University of Science and Technology Beijing,China
国际会议
杭州
英文
878-881
2013-03-22（万方平台首次上网日期，不代表论文的发表时间）