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.

本文首次将有监督对比学习应用于电子商务中的产品匹配任务，使用来自不同电商的产品提供，通过对有标签的对比学习进行预训练，并提出了一种源感知采样策略，能够在训练数据不包含产品标识符的使用用例中使用对比学习，实验结果表明对比预训练与源感知采样相结合，可以显著提高多个广泛使用的基准测试性能，并在有明确监督的产品匹配用例中具有很高的潜力。