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

• URL: http://arxiv.org/abs/2402.10091v1
• Date: Thu, 1 Feb 2024 18:52:26 GMT
• Title: Text-Based Product Matching -- Semi-Supervised Clustering Approach
• Authors: Alicja Martinek, Szymon {\L}ukasik, Amir H. Gandomi
• Abstract summary: 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.
• Score: 9.748519919202986
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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