# Contrastive Learning for Diversity-Aware Product Recommendations in Retail

arXiv: https://arxiv.org/abs/2602.08886 · HTML: https://mlsanity.com/paper/2602.08886/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 16 references that are in the map.
- First posted on arXiv 9 Feb 2026.

## Builds on (most cited first, 8 of 8)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [SimCSE: Simple Contrastive Learning of Sentence Embeddings](https://mlsanity.com/paper/2104.08821/) (2021)
- [Self-Attentive Sequential Recommendation](https://mlsanity.com/paper/1808.09781/) (2018)
- [Measuring the Business Value of Recommender Systems](https://mlsanity.com/paper/1908.08328/) (2019)
- [Fairness and Diversity in Recommender Systems: A Survey](https://mlsanity.com/paper/2307.04644/) (2023)
- [Optimizing generalized Gini indices for fairness in rankings](https://mlsanity.com/paper/2204.06521/) (2022)
- [Evaluating Performance and Bias of Negative Sampling in Large-Scale Sequential Recommendation Models](https://mlsanity.com/paper/2410.17276/) (2024)

## Abstract (arXiv)

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.