MUSS: Multilevel Subset Selection for Relevance and Diversity

• URL: http://arxiv.org/abs/2503.11126v2
• Date: Tue, 20 May 2025 07:43:34 GMT
• Title: MUSS: Multilevel Subset Selection for Relevance and Diversity
• Authors: Vu Nguyen, Andrey Kan,
• Abstract summary: In recommender systems, one is interested in selecting relevant items, while providing a diversified recommendation.<n>We present a novel theoretical approach for analyzing this type of problems, and show that our method achieves a constant factor approximation of the optimal objective.
• Score: 4.8254343133177295
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The problem of relevant and diverse subset selection has a wide range of applications, including recommender systems and retrieval-augmented generation (RAG). For example, in recommender systems, one is interested in selecting relevant items, while providing a diversified recommendation. Constrained subset selection problem is NP-hard, and popular approaches such as Maximum Marginal Relevance (MMR) are based on greedy selection. Many real-world applications involve large data, but the original MMR work did not consider distributed selection. This limitation was later addressed by a method called DGDS which allows for a distributed setting using random data partitioning. Here, we exploit structure in the data to further improve both scalability and performance on the target application. We propose MUSS, a novel method that uses a multilevel approach to relevant and diverse selection. In a recommender system application, our method can not only improve the performance up to $4$ percent points in precision, but is also $20$ to $80$ times faster. Our method is also capable of outperforming baselines on RAG-based question answering accuracy. We present a novel theoretical approach for analyzing this type of problems, and show that our method achieves a constant factor approximation of the optimal objective. Moreover, our analysis also resulted in a $\times 2$ tighter bound for DGDS compared to previously known bound.

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