Group Recommender Systems: A Virtual User Approach Based on Precedence Mining
The recommendation framework based on precedence mining as outlined in  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 group-sizes 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.
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