提出自适应质量与多样性权衡的推荐算法，提升个性化与惊喜感。
Adaptive Quality-Diversity Trade-offs for Large-Scale Batch Recommendation
- 结合确定性点过程与模糊去噪，动态调节推荐多样性。
- 在真实数据集上实现高质量且多样化的批量推荐，提升用户参与度。
- 适用于电影推荐与药物重定位等大规模场景，效果稳定可靠。
推荐系统的核心挑战之一是为用户生成高相关性且多样化的项目批次——既符合用户偏好，又能引导其探索舒适区之外的内容。这种多样性可能带来意外发现和新颖性，从而提高用户参与度或收益。然而实际应用中存在诸多难题：避免推荐过于相似但不同的项目以降低流失风险，以及面对百万级项目库时的计算成本。本文首先假设用户反馈模型完全已知，提出高效算法B-DivRec，结合确定性点过程与模糊去噪技术，动态调整项目多样性，实现用户历史全程的质量-多样性平衡。其次，提出自适应策略，根据反馈实时优化质量与多样性权衡：若多样性带来正反馈则增强，反之则收敛。最后，在电影推荐与药物重定位的合成及真实数据集上验证了B-DivRec的性能与泛化能力。
原文摘要 · Abstract (English)
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.
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