Improving sparsity problem in group recommendation

Abstract

Group recommendation systems can be very challenging when the datasets are sparse and there are not many available ratings for items. In this paper, by enhancing basic memorybased techniques we resolve the data sparsity problem for users in the group. The results have shown that by conducting our techniques for the users in the group we have a higher group satisfaction and lower group dissatisfaction.