DUM: Diversity-Weighted Utility Maximization for Recommendations
This addresses the problem of balancing relevance and variety in recommender systems for users, but appears incremental as it builds on existing diversification approaches.
The paper tackled the trade-off between diversity and utility in recommendation lists by proposing a method to maximize utility subject to diversity constraints, and demonstrated its superiority over baselines in online user studies and offline analysis.
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.