DUM: Diversity-Weighted Utility Maximization for Recommendations
release_wgbp7fnbkvgqfcpwsyzpaixjim
by
Azin Ashkan, Branislav Kveton, Shlomo Berkovsky, Zheng Wen
2014
Abstract
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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