A preference elicitation method based on bipartite graphical correlation and implicit trust

Abstract

In the age of big data, information overload is getting worse. Most of the existing recommender systems which apply data analysis and behavioral analysis to make personal recommendation have suffered from the problem of low prediction accuracy. To address this problem, a new preference elicitation algorithm is developed by using bipartite graphical correlation and implicit trust. More precisely, to compute the bipartite graphical correlation, an improved single-source shortest path is firstly presented to get the shortest behavior path based on the user-item bipartite graph. While the Markov separation algorithm is employed to separate uncorrelated vertexes and obtain the values of bipartite graphical correlation. Secondly, the implicit trust is used to represent trust relationship between users and items which have no historical behaviors. Independent trust group is applied to represent the closed-loop path on the user-item bipartite graph, and a compute method is developed to get the values of implicit trust based on the trusty values of independent trust group and explicit trust. Finally, a weighted prediction method based on bipartite graphical correlation and implicit trust is employed to compute the final ratings. Our empirical experiments are performed on a sparse data set, the results of which have demonstrated that our method can achieve lower P@R and efficiently improve recommendation quality.