用对比学习提升电商推荐多样性，让冷门商品也有曝光机会。
Contrastive Learning for Diversity-Aware Product Recommendations in Retail
- 用精心挑选的负样本做对比学习，缓解热门商品垄断问题。
- 离线与线上实验均显示推荐多样性显著提升，性能不下降。
- 适合需要提升商品长尾覆盖的电商平台使用。
推荐系统常面临长尾分布和商品曝光不足的问题，少数热门商品主导推荐结果。在拥有海量商品的大型在线零售场景中，这一挑战尤为突出。本文提出一种方法，在不损害现有数字推荐流程性能的前提下，提升商品目录覆盖率。受近期负采样技术缓解流行度偏差的启发，我们引入对比学习，并采用精心设计的负样本。通过离线与线上评估，证明该方法能有效提升推荐多样性，确保更多商品获得曝光，同时维持出色的推荐表现。
原文摘要 · Abstract (English)
Recommender systems often struggle with long-tail distributions and limited item catalog exposure, where a small subset of popular items dominates recommendations. This challenge is especially critical in large-scale online retail settings with extensive and diverse product assortments. This paper introduces an approach to enhance catalog coverage without compromising recommendation quality in the existing digital recommendation pipeline at IKEA Retail. Drawing inspiration from recent advances in negative sampling to address popularity bias, we integrate contrastive learning with carefully selected negative samples. Through offline and online evaluations, we demonstrate that our method improves catalog coverage, ensuring a more diverse set of recommendations yet preserving strong recommendation performance.
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