# Supervised Contrastive Learning for Product Matching

arXiv: https://arxiv.org/abs/2202.02098 · HTML: https://mlsanity.com/paper/2202.02098/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 9 references that are in the map.
- First posted on arXiv 4 Feb 2022.

## Builds on (most cited first, 6 of 6)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [SimCSE: Simple Contrastive Learning of Sentence Embeddings](https://mlsanity.com/paper/2104.08821/) (2021)
- [Deep Entity Matching with Pre-Trained Language Models](https://mlsanity.com/paper/2004.00584/) (2020)

## Built on by (most cited first, 24 of 24)

- [Entity Matching using Large Language Models](https://mlsanity.com/paper/2310.11244/) (2023)
- [Using ChatGPT for Entity Matching](https://mlsanity.com/paper/2305.03423/) (2023)
- [WDC Products: A Multi-Dimensional Entity Matching Benchmark](https://mlsanity.com/paper/2301.09521/) (2023)
- [SEED: Domain-Specific Data Curation With Large Language Models](https://mlsanity.com/paper/2310.00749/) (2023)
- [SC-Block: Supervised Contrastive Blocking within Entity Resolution Pipelines](https://mlsanity.com/paper/2303.03132/) (2023)
- [Cost-Efficient Prompt Engineering for Unsupervised Entity Resolution](https://mlsanity.com/paper/2310.06174/) (2023)
- [Block-SCL: Blocking Matters for Supervised Contrastive Learning in Product Matching](https://mlsanity.com/paper/2207.02008/) (2022)
- [LEA: Improving Sentence Similarity Robustness to Typos Using Lexical Attention Bias](https://mlsanity.com/paper/2307.02912/) (2023)
- [End-to-end multi-modal product matching in fashion e-commerce](https://mlsanity.com/paper/2403.11593/) (2024)
- [Preventing Collapse in Contrastive Learning with Orthonormal Prototypes (CLOP)](https://mlsanity.com/paper/2403.18699/) (2024)
- [Schema Matching on Graph: Iterative Graph Exploration for Efficient and Explainable Data Integration](https://mlsanity.com/paper/2511.20285/) (2025)
- [Multilingual Transformers for Product Matching -- Experiments and a New Benchmark in Polish](https://mlsanity.com/paper/2205.15712/) (2022)
- [TransClean: Finding False Positives in Multi-Source Entity Matching under Real-World Conditions via Transitive Consistency](https://mlsanity.com/paper/2506.04006/) (2025)
- [Labeling Training Data for Entity Matching Using Large Language Models](https://mlsanity.com/paper/2606.28823/) (2026)
- [Text-Based Product Matching -- Semi-Supervised Clustering Approach](https://mlsanity.com/paper/2402.10091/) (2024)
- [APrompt4EM: Augmented Prompt Tuning for Generalized Entity Matching](https://mlsanity.com/paper/2405.04820/) (2024)
- [GraLMatch: Matching Groups of Entities with Graphs and Language Models](https://mlsanity.com/paper/2406.15015/) (2024)
- [Leveraging large language models for efficient representation learning for entity resolution](https://mlsanity.com/paper/2411.10629/) (2024)
- [Optimizing Product Deduplication in E-Commerce with Multimodal Embeddings](https://mlsanity.com/paper/2509.15858/) (2025)
- [Confidence Calibration in Large Language Model-Based Entity Matching](https://mlsanity.com/paper/2509.19557/) (2025)
- [Fine-grained auxiliary learning for real-world product recommendation](https://mlsanity.com/paper/2510.04551/) (2025)
- [Semi-Supervised Contrastive Learning with Orthonormal Prototypes](https://mlsanity.com/paper/2512.07880/) (2025)
- [Beyond Scale and Generation: Understanding Language Model-based Entity Matching](https://mlsanity.com/paper/2607.24688/) (2026)
- [Billiger.de Products: A Bilingual Entity Matching Benchmark](https://mlsanity.com/paper/2609.37713/) (2026)

## Abstract (arXiv)

Contrastive learning has moved the state of the art for many tasks in computer vision and information retrieval in recent years. This poster is the first work that applies supervised contrastive learning to the task of product matching in e-commerce using product offers from different e-shops. More specifically, we employ a supervised contrastive learning technique to pre-train a Transformer encoder which is afterward fine-tuned for the matching task using pair-wise training data. We further propose a source-aware sampling strategy that enables contrastive learning to be applied for use cases in which the training data does not contain product identifiers. We show that applying supervised contrastive pre-training in combination with source-aware sampling significantly improves the state-of-the-art performance on several widely used benchmarks: For Abt-Buy, we reach an F1-score of 94.29 (+3.24 compared to the previous state-of-the-art), for Amazon-Google 79.28 (+ 3.7). For WDC Computers datasets, we reach improvements between +0.8 and +8.84 in F1-score depending on the training set size. Further experiments with data augmentation and self-supervised contrastive pre-training show that the former can be helpful for smaller training sets while the latter leads to a significant decline in performance due to inherent label noise. We thus conclude that contrastive pre-training has a high potential for product matching use cases in which explicit supervision is available.