Text-Based Product Matching -- Semi-Supervised Clustering Approach
Abstract
Matching identical products present in multiple product feeds constitutes a crucial element of many tasks of e-commerce, such as comparing product offerings, dynamic price optimization, and selecting the assortment personalized for the client. It corresponds to the well-known machine learning task of entity matching, with its own specificity, like omnipresent unstructured data or inaccurate and inconsistent product descriptions. This paper aims to present a new philosophy to product matching utilizing a semi-supervised clustering approach. We study the properties of this method by experimenting with the IDEC algorithm on the real-world dataset using predominantly textual features and fuzzy string matching, with more standard approaches as a point of reference. Encouraging results show that unsupervised matching, enriched with a small annotated sample of product links, could be a possible alternative to the dominant supervised strategy, requiring extensive manual data labeling.
Cite
@article{arxiv.2402.10091,
title = {Text-Based Product Matching -- Semi-Supervised Clustering Approach},
author = {Alicja Martinek and Szymon Łukasik and Amir H. Gandomi},
journal= {arXiv preprint arXiv:2402.10091},
year = {2024}
}