A Group Recommendation Algorithm Based on Group Members' Weights and Project Popularity

Abstract

With the rapid development of many types of social networks, the number of people involved in group activities is increasing, and thus group recommendation systems have been widely studied. This paper proposes a collaborative filtering algorithm to mine groups' interests. In view of the different roles played by different users in the group, the algorithm combines the members' weights factor. And because of the differences in the degree of concern among groups and members for the various categories, the weighted mean similarity method is used. In order to solve the problem of a groups' narrow field of view of the existing algorithms and to more effectively mine unpopular items, the algorithm considers project popularity to improve the novelty of the recommendations. Experiments on the MovieLens 1M GroupLens data set show that the algorithm can effectively improve the accuracy of the recommendations. And to a certain extent, it improves the novelty of the ecommendations.