New PR-entmax objective tackles popularity bias in recommendation systems

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-13 06:43

研究人员开发了一种名为PR-entmax的新目标函数，以解决序列推荐系统中的流行度偏差问题。该方法将物品流行度先验直接纳入稀疏概率映射中，使先验能够影响训练过程中哪些物品会接收到梯度更新。在基准数据集上的实验表明，PR-entmax在保持或增强推荐相关性的同时，能有效减少流行度偏差。 AI

影响这项研究通过减轻热门物品的过度曝光，有望带来更多样化和相关的推荐。

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arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Alexey Vasilev · 2026-10-13 06:43

通过稀疏先验正则化目标缓解序列推荐中的流行度偏差

Popularity bias is a well-known challenge in recommender systems, often leading to the over-exposure of popular items and reduced catalog coverage. In this work, we propose PR-entmax, a sparse prior-regularized objective that makes popularity correction context-dependent. We deri…