English(EN) Learning in Position-Aware Multinomial Logit Bandits: From Multiplicative to General Position Effects

新算法优化产品选择和展示位置

作者 PulseAugur 编辑部 · [2 个来源] · 2026-05-17 03:28

研究人员在多项 Logit 选择框架下开发了新的优化产品组合和展示位置的算法。这些算法同时处理乘法和一般位置效应模型，旨在改进现代平台上的决策。所提出的 P2MLE-UCB 和 GP2-UCB 方法在数值实验中实现了遗憾最优的表征，并优于现有基准。 AI

影响引入了优化产品选择和定位的新算法，可能改进推荐系统和电子商务平台。

排序理由该集群包含一篇详细介绍新算法和理论结果的学术论文。

AI 生成摘要 · Google Gemini · 来自 2 个来源。我们如何撰写摘要 →

报道来源 [2]

arXiv stat.ML TIER_1 English(EN) · Xi Chen, Shibo Dai, Jiameng Lyu, Yuan Zhou · 2026-05-19 04:00

Learning in Position-Aware Multinomial Logit Bandits: From Multiplicative to General Position Effects

arXiv:2605.17238v1 Announce Type: cross Abstract: We study the dynamic joint assortment selection and positioning problem, where the attraction of each product depends on both its intrinsic appeal and its display position under a Multinomial Logit (MNL) choice framework. Our stud…
arXiv stat.ML TIER_1 English(EN) · Yuan Zhou · 2026-05-17 03:28

Learning in Position-Aware Multinomial Logit Bandits: From Multiplicative to General Position Effects

We study the dynamic joint assortment selection and positioning problem, where the attraction of each product depends on both its intrinsic appeal and its display position under a Multinomial Logit (MNL) choice framework. Our study ranges from the multiplicative position effects …