Title Temporal Bipartite Graph Recommendation Method Based on User Feedback
Xinlian Liu · Journal of Henan University · 2015
Bipartite Graph recommendation methods are based on the similarity of usersshopping behavior,and the resource allocation algorithm is employed to output the recommendation results.In fact,the shopping behavior of users does not simply answer yes or no for the recommendation,but indicates more information.For example,items which users were satisfied with can indicate their interests correctly,as well as the awful shopping experiences mean the users make a wrong decision with the items they have bought.It was the same when the recommendation results came from the items they have purchased several years ago.If we do not consider their feedback information in our recommendation,the results can be confused with users.A recommendation method combined with temporal and rating information based on bipartite graph was proposed in the paper.In the proposed method,the initial weights of items can be allocated adaptively by the usersfeedback,so the recommendation can be correctly and timely.Experiments on several real life datasets show notable improvement on the Top-N hits metric.