An Improved Method Multi-View Group Recommender System (IMVGRS)

Abstract

Today, one of the users' issues on the web is finding their desired information from a massive amount of data. Recommender systems aid users in making decisions and choosing their suitable items by personalizing the contents for users by their interest. In the past, most of the researches has been done on individual recommender systems. But now, attention has been drawn to group recommender systems. For this reason, this paper tried to improve a group recommender system. In this article, an Improved Multi-View Group Recommender System (IMVGRS) has been proposed. This multi-view group recommender system recommends to a group of the user from two standpoints of user preferences (ratings) and social connection (trust). First, the dimension of the data has been reduced with the Singular-Value Decomposition (SVD) method. Second, the system has been clustered with the complete linkage method. Experimental results, show the effectiveness of the proposed improved method.