Text-Based Product Matching -- Semi-Supervised Clustering Approach

arXiv:2402.10091v1 Announce Type: cross
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 …

abstract arxiv client clustering commerce cs.ai cs.db cs.lg dynamic e-commerce element machine machine learning multiple optimization personalized price price optimization product products semi-supervised specificity tasks text type