DUM: Diversity-Weighted Utility Maximization for Recommendations

Ashkan, Azin, Kveton, Branislav, Berkovsky, Shlomo, Wen, Zheng

arXiv.org Machine Learning 

The need for diversification of recommendation lists manifests in a number of recommender systems use cases. However, an increase in diversity may undermine the utility of the recommendations, as relevant items in the list may be replaced by more diverse ones. In this work we propose a novel method for maximizing the utility of the recommended items subject to the diversity of user's tastes, and show that an optimal solution to this problem can be found greedily. We evaluate the proposed method in two online user studies as well as in an offline analysis incorporating a number of evaluation metrics. The results of evaluations show the superiority of our method over a number of baselines.

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