Personalized recommendation algorithm based on weighted bipartite network
Shengyi Jiang · Journal of Computer Applications · 2012
In Network-Based Inference(NBI) algorithm,the weight of edge between user and item is ignored;therefore,the items with high rating have not got the priority to be recommended.In order to solve the problem,a Weigted Network-Based Inference(WNBI) algorithm was proposed.The edge between user and item was weighted with item's rating by proposed algorithm,the resources were allocated according to the ratio of the edge's weight to total edges' weight of the node,so that high rating items could be recommended with priority.The experimental results on data set MovieLens demonstrate that the number of hit high rating items by WNBI increases obviously in contrast with NBI,especially when the length of recommendation list is shorter than 20,the numbers of hit items and hit high rating items both increase.