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 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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