An Adaptive Aggregation Method Based on Movie Genre for Group Recommendation

Abstract

Group Recommendation means recommending satisfying activities to a group of users in social networks. Most existing aggregation methods mainly focus on aggregating the user's rating for the item according to the rating feature. They ignore the rationality and interpretability of user weights in aggregate function. Moreover, the user weights determined by these methods cannot be adaptively changed with different groups and items. In this paper, we propose an adaptive (AG) aggregation method based on item genre. The AG method does two things: first, uses item genre information to extract a reasonable user weight; second, performs adaptive weight modeling. In addition, we consider that there will be interactions among members in the group decision-making process, so the weights of members may be adjusted appropriately. In order to consider this reasonable factor in our recommendation system, we improved the AG method from the perspective of group member weight deviation. Extensive experimental results on MovieLens dataset clearly demonstrate the effectiveness of our proposed AG.