解决推荐系统过度重复推荐的问题，提升多样性与公平性。
Repeat-bias-aware Optimization of Beyond-accuracy Metrics for Next Basket Recommendation
- 提出一种无模型依赖的后处理优化算法，缓解重复推荐偏差。
- 在三个真实购物数据集上，显著提升多样性与公平性，召回损失可控。
- 适合关注推荐结果多样性和公平性的电商、零售场景应用。
在下一个购物篮推荐（NBR）中，需根据用户历史购物序列推荐一组商品。实际场景中，推荐结果常包含重复购买商品和探索性商品。现有先进方法倾向于过度推荐重复商品以提升实用性，导致推荐结果缺乏多样性与公平性。尽管对超越准确率的目标（如商品公平性与多样性）的评估与优化日益受到关注，但如何在重重复偏差背景下实现这些目标仍具挑战。研究发现，仅优化多样性或公平性而不考虑重复偏差，反而会加剧重复推荐。为此，本文提出一种模型无关的重复偏差感知优化算法，用于后处理现有NBR方法的推荐结果，旨在优化多样性或公平性的同时缓解重复偏差。设计了多种变体以适配不同NBR方法。在三个真实世界生鲜购物数据集上的实验表明，所提算法能有效提升多样性与商品公平性，并在可接受的召回率损失下缓解重复偏差。
原文摘要 · Abstract (English)
In next basket recommendation (NBR) a set of items is recommended to users based on their historical basket sequences. In many domains, the recommended baskets consist of both repeat items and explore items. Some state-of-the-art NBR methods are heavily biased to recommend repeat items so as to maximize utility. The evaluation and optimization of beyond-accuracy objectives for NBR, such as item fairness and diversity, has attracted increasing attention. How can such beyond-accuracy objectives be pursued in the presence of heavy repeat bias? We find that only optimizing diversity or item fairness without considering repeat bias may cause NBR algorithms to recommend more repeat items. To solve this problem, we propose a model-agnostic repeat-bias-aware optimization algorithm to post-process the recommended results obtained from NBR methods with the objective of mitigating repeat bias when optimizing diversity or item fairness. We consider multiple variations of our optimization algorithm to cater to multiple NBR methods. Experiments on three real-world grocery shopping datasets show that the proposed algorithms can effectively improve diversity and item fairness, and mitigate repeat bias at acceptable Recall loss.
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