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.

翻译：推荐系统在处理长尾分布和有限商品目录曝光方面常面临挑战，其中少数热门商品往往主导推荐结果。这一挑战在拥有海量多样化产品组合的大规模在线零售环境中尤为突出。本文提出一种方法，旨在提升宜家零售现有数字推荐流程中的目录覆盖率，同时不损害推荐质量。受近期利用负采样应对流行度偏差的研究进展启发，我们将对比学习与精心挑选的负样本相结合。通过离线和在线评估，我们证明该方法能有效提升目录覆盖率，在保持强劲推荐性能的同时，确保推荐结果具有更高的多样性。