Applying uncertainty theory to group recommender systems taking account of experts preferences

This study aims to generate recommendations for a group by weighting the preferences of each group member. We applied uncertain statistics to the preference scores of a panel of experts and a genetic algorithm (GA) to balance the weights of the group members (UGA). A group profile was then designed on the basis of the user ratings and the scores of the experts. A novel similarity measure was then developed based on uncertainty theory to refine the number of K-nearest neighbors (KNN). By integrating uncertain statistics and the novel similarity measure, group profiles were developed from the MovieLens and Gym datasets. Experiments demonstrated that the proposed approach was significantly better than two baseline approaches in generating group recommendations.

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