Group recommender systems: A virtual user approach based on precedence mining

Abstract

The recommendation framework based on precedence mining as outlined in [3] is limited to personal recommendation and cannot be trivially extended for group recommendation scenario. In this paper, we extend the precedence mining model for group recommendation by proposing a novel way of defining a virtual user by taking transitive precedence relation into account. We obtained experimental results for different combinations of parameter settings and for different groupsizes on MovieLens data-set based on our virtual-user model. We show that our framework has better performance in terms of precision and recall when compared with other methods. © Springer International Publishing 2013.