Virtual user approach for group recommender systems using precedence relations

• We describe virtual user strategy and then examine its properties.
• The experimental results show that virtual strategy achieve better Precision and Recall.
• We propose incremental algorithms to update virtual user profile.
• A new measure called monotonicity is introduced to judge the efficiency of a recommender system.
• Virtual-user profile + Monotonicity yield recommendations having higher accuracy on benchmark datasets.

In this paper, we propose a novel virtual user strategy using precedence relations and develop a new scheme for group recommender systems. User profiles are provided in terms of the precedence relations of items as used by group members. A virtual user for a group is constructed by taking transitive precedence of items of all members into consideration. The profile of the virtual user represents the combined profile of the group. There has not been any earlier attempt to define virtual user profile using precedence relations. We show that the proposed framework exhibits many interesting properties. Earlier approaches construct virtual user profile by considering the set of common items used by all members of the group. In the present work, we propose a method of computing weightage for each item, not necessarily common to all members, using transitive precedence. We also introduce a new measure called monotonicity to measure the performance of any recommender system. In a top-k recommendation, monotonicity tries to measure the number of items continued to be recommended when a technique is utilized incrementally. We experimented extensively for different combinations of parameter settings and for different group sizes on MovieLens data. We show that our framework has better performance in terms of precision and recall when compared with other methods. We show that our recommendation framework exhibits robust monotonicity.