{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/supervised-contrastive-learning-for-product","title":"Supervised Contrastive Learning for Product Matching","arxiv_id":"2202.02098","date":"2022-02-04","proceeding":null,"authors":["Ralph Peeters","Christian Bizer"],"abstract":"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.","url_abs":"https://arxiv.org/abs/2202.02098v2","url_pdf":"https://arxiv.org/pdf/2202.02098v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"supervised-contrastive-learning-for-product","repo_url":"https://github.com/wbsg-uni-mannheim/contrastive-product-matching","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"contrastive-learning","task_name":"Contrastive Learning"},{"task_slug":"data-augmentation","task_name":"Data Augmentation"},{"task_slug":"entity-resolution","task_name":"Entity Resolution"},{"task_slug":"information-retrieval","task_name":"Information Retrieval"},{"task_slug":"retrieval","task_name":"Retrieval"}],"methods":[{"method_slug":"absolute-position-encodings","method_name":"Absolute Position Encodings"},{"method_slug":"adam","method_name":"Adam"},{"method_slug":"attention","method_name":"Attention"},{"method_slug":"bpe","method_name":"BPE"},{"method_slug":"contrastive-learning","method_name":"Contrastive Learning"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"label-smoothing","method_name":"Label Smoothing"},{"method_slug":"layer-normalization","method_name":"Layer Normalization"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"multi-head-attention","method_name":"Multi-Head Attention"},{"method_slug":"position-wise-feed-forward-layer","method_name":"Position-Wise Feed-Forward Layer"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"transformer","method_name":"Transformer"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/entity-resolution-on-abt-buy","task":"Entity Resolution","dataset":"Abt-Buy","model":"RoBERTa-SupCon","rank_in_archive_order":2,"of":16,"metrics":{"F1 (%)":"94.29"},"uses_additional_data":false},{"leaderboard":"/sota/entity-resolution-on-amazon-google","task":"Entity Resolution","dataset":"Amazon-Google","model":"RoBERTa-SupCon","rank_in_archive_order":3,"of":17,"metrics":{"F1 (%)":"79.28"},"uses_additional_data":false},{"leaderboard":"/sota/entity-resolution-on-wdc-computers-small","task":"Entity Resolution","dataset":"WDC Computers-small","model":"RoBERTa-SupCon","rank_in_archive_order":2,"of":6,"metrics":{"F1 (%)":"95.21"},"uses_additional_data":false},{"leaderboard":"/sota/entity-resolution-on-wdc-computers-xlarge","task":"Entity Resolution","dataset":"WDC Computers-xlarge","model":"RoBERTa-SupCon","rank_in_archive_order":1,"of":6,"metrics":{"F1 (%)":"98.33"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2202.02098","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}