mlsanityData to 14 Oct 2026

Adaptive Quality-Diversity Trade-offs for Large-Scale Batch Recommendation

arXiv 2602.02024 ↗First posted 2 Feb 2026cs.IR cs.LGcited by 0

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Abstract (arXiv)

A core research question in recommender systems is to propose batches of highly relevant and diverse items, that is, items personalized to the user's preferences, but which also might get the user out of their comfort zone. This diversity might induce properties of serendipidity and novelty which might increase user engagement or revenue. However, many real-life problems arise in that case: e.g., avoiding to recommend distinct but too similar items to reduce the churn risk, and computational cost for large item libraries, up to millions of items. First, we consider the case when the user feedback model is perfectly observed and known in advance, and introduce an efficient algorithm called B-DivRec combining determinantal point processes and a fuzzy denuding procedure to adjust the degree of item diversity. This helps enforcing a quality-diversity trade-off throughout the user history. Second, we propose an approach to adaptively tailor the quality-diversity trade-off to the user, so that diversity in recommendations can be enhanced if it leads to positive feedback, and vice-versa. Finally, we illustrate the performance and versatility of B-DivRec in the two settings on synthetic and real-life data sets on movie recommendation and drug repurposing.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 33 of its 95 references that are in the map. First posted on arXiv 2 Feb 2026.

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References · 33

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21 more
  1. 2016Low-Rank Factorization of Determinantal Point Processes for Recommendationcited by 14
  2. 2021Fast Statistical Leverage Score Approximation in Kernel Ridge Regressioncited by 14
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  21. 2025Diversified recommendations of cultural activities with personalized determinantal point processescited by 1

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