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.

翻译：推荐系统中的一个核心研究问题是提出既高度相关又多样化的项目批次，即既符合用户个性化偏好，又能引导用户走出舒适区的项目。这种多样性可能带来意外性和新颖性，从而提升用户参与度或增加收入。然而，这种情况下会出现许多现实问题：例如，为避免推荐虽不同但过于相似的项目以降低流失风险，以及处理大规模项目库（可达数百万项目）带来的计算成本。首先，我们考虑用户反馈模型被完美观测且预先已知的情况，提出一种高效算法B-DivRec，该算法结合行列式点过程和模糊去冗余程序来调整项目多样性程度。这有助于在用户历史中实现质量-多样性权衡。其次，我们提出一种方法来自适应用户的质量-多样性权衡，使得若多样性带来积极反馈则可增强推荐多样性，反之亦然。最后，我们在电影推荐和药物重定位的合成及真实数据集上，展示了B-DivRec在两种设置下的性能与通用性。