%0 Journal Article
%A CAI Guo-yong
%A LIN Yu-ming
%A WEN Yi-min
%A ZHANG Dong
%T Probabilistic Spreading Models for Improving Recommendation Diversity
%D 2016
%R 10.13190/j.jbupt.2016.03.005
%J Journal of Beijing University of Posts and Telecommunications
%P 34-38
%V 39
%N 3
%X <p>Bipartite-graph based probabilistic spreading (ProbS) algorithms often focus on optimizing the accuracy of recommendation lists while ignoring diversity, another key property to evaluate the quality of recommendation results. In order to deal with this problem, an improved probabilistic spreading (iProbS) algorithm is proposed in the present paper. The iProbS algorithm divides the recommendation process into three steps of resource spreading, and each resource spreading step constrained by spreading probability and spreading cost simultaneously. Users' scores rating on items are applied to compute spreading probability, at the same time, the degree of items, entropy of users, and the neighbors of items are considered for computing spreading costs. Extensive experiments on two widely used data sets (from MovieLens and Netflix) show that iProbS can effectively improve recommending accuracy, aggregate diversity, individual diversity, and sales balance of recommendation lists. Finally, computational complexities of iProbS are studied from its different computing steps.</p>
%U https://journal.bupt.edu.cn/EN/10.13190/j.jbupt.2016.03.005