SC-Block++: A Blocking Algorithm Based on Adaptive Flood Regularization
Arthur Ning, Flavius Frăsincar, Tarmo Robal · 2025
The rapid surge in the number of Web shops presents a challenge for consumers: navigating through the vast amount of stores and products available. Therefore, entity resolution has become an important task to aggregate product information across different Web shops. As entity resolution is a computationally demanding process, its pipelines are divided into two: a blocking phase, which uses a computationally cheap method to select candidate product pairs, and a matching phase with a computationally expensive method to identify matching pairs from the set of candidate pairs. In this paper, we propose SC-Block++, an extension to a state-of-the-art blocking algorithm SC-Block. SC-Block utilizes a RoBERTa base transformer model, trained using Supervised Contrastive Learning, to position the product records in an embedding space, and produces a set of candidate pairs using a nearest-neighbour search. We extend the training procedure of the RoBERTa base transformer model by incorporating Adaptive Flood Regularization (AdaFlood), a regularization method aimed to prevent overfitting and to improve the generalization performance of the model. We compare SC-Block++ to SC-Block, and other benchmark methods on three different data sets, and find that SC-Block++ is able to construct candidate pairs more effectively than the other blocking schemes.