Contrastive Learning for Diversity-Aware Product Recommendations in Retail
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866908823055237120 |
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| author | Karlis, Vasileios Yıldırım, Ezgi Vos, David de Rijke, Maarten |
| author_facet | Karlis, Vasileios Yıldırım, Ezgi Vos, David de Rijke, Maarten |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_08886 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Contrastive Learning for Diversity-Aware Product Recommendations in Retail Karlis, Vasileios Yıldırım, Ezgi Vos, David de Rijke, Maarten Information Retrieval Machine Learning 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. |
| title | Contrastive Learning for Diversity-Aware Product Recommendations in Retail |
| topic | Information Retrieval Machine Learning |
| url | https://arxiv.org/abs/2602.08886 |