  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.
