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

arXiv: https://arxiv.org/abs/2602.02024 · HTML: https://mlsanity.com/paper/2602.02024/

## 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.

## Builds on (most cited first, 30 of 33)

- [MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers](https://mlsanity.com/paper/2002.10957/) (2020)
- [LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation](https://mlsanity.com/paper/2002.02126/) (2020)
- [Heterogeneous Graph Attention Network](https://mlsanity.com/paper/1903.07293/) (2019)
- [Universal Sentence Encoder](https://mlsanity.com/paper/1803.11175/) (2018)
- [Random Features for Kernel Approximation: A Survey on Algorithms, Theory, and Beyond](https://mlsanity.com/paper/2004.11154/) (2020)
- [Follow the Leader If You Can, Hedge If You Must](https://mlsanity.com/paper/1301.0534/) (2013)
- [Diversified Sampling for Batched Bayesian Optimization with Determinantal Point Processes](https://mlsanity.com/paper/2110.11665/) (2021)
- [Learning the Parameters of Determinantal Point Process Kernels](https://mlsanity.com/paper/1402.4862/) (2014)
- [Enhancing Recommendation Diversity by Re-ranking with Large Language Models](https://mlsanity.com/paper/2401.11506/) (2024)
- [Determinantal Point Processes for Coresets](https://mlsanity.com/paper/1803.08700/) (2018)
- [Sampling from a k-DPP without looking at all items](https://mlsanity.com/paper/2006.16947/) (2020)
- [Faster Greedy MAP Inference for Determinantal Point Processes](https://mlsanity.com/paper/1703.03389/) (2017)
- [Low-Rank Factorization of Determinantal Point Processes for Recommendation](https://mlsanity.com/paper/1602.05436/) (2016)
- [Fast Statistical Leverage Score Approximation in Kernel Ridge Regression](https://mlsanity.com/paper/2103.05238/) (2021)
- [Modeling Attrition in Recommender Systems with Departing Bandits](https://mlsanity.com/paper/2203.13423/) (2022)
- [Multiple-criteria Based Active Learning with Fixed-size Determinantal Point Processes](https://mlsanity.com/paper/2107.01622/) (2021)
- [Large-Margin Determinantal Point Processes](https://mlsanity.com/paper/1411.1537/) (2014)
- [Learning Determinantal Point Processes in Sublinear Time](https://mlsanity.com/paper/1610.05925/) (2016)
- [Contextual Distillation Model for Diversified Recommendation](https://mlsanity.com/paper/2406.09021/) (2024)
- [Measuring Diversity: Axioms and Challenges](https://mlsanity.com/paper/2410.14556/) (2024)
- [DPPNet: Approximating Determinantal Point Processes with Deep Networks](https://mlsanity.com/paper/1901.02051/) (2019)
- [Hardness of Maximum Likelihood Learning of DPPs](https://mlsanity.com/paper/2205.12377/) (2022)
- [Beyond Item Dissimilarities: Diversifying by Intent in Recommender Systems](https://mlsanity.com/paper/2405.12327/) (2024)
- [Metrics for popularity bias in dynamic recommender systems](https://mlsanity.com/paper/2310.08455/) (2023)
- [A Family of Distributions of Random Subsets for Controlling Positive and Negative Dependence](https://mlsanity.com/paper/2408.01022/) (2024)
- [Recommendation Is a Dish Better Served Warm](https://mlsanity.com/paper/2508.07856/) (2025)
- [Direct formulation to Cholesky decomposition of a general nonsingular correlation matrix](https://mlsanity.com/paper/1412.1181/) (2014)
- [Conditional Sequential Slate Optimization](https://mlsanity.com/paper/2108.05618/) (2021)
- [A Serendipitous Recommendation System Considering User Curiosity](https://mlsanity.com/paper/2504.06633/) (2025)
- [Diverse Mini-Batch Selection in Reinforcement Learning for Efficient Chemical Exploration in de novo Drug Design](https://mlsanity.com/paper/2506.21158/) (2025)

## 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.