Text-Based Product Matching -- Semi-Supervised Clustering Approach
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866910332618801152 |
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| author | Martinek, Alicja Łukasik, Szymon Gandomi, Amir H. |
| author_facet | Martinek, Alicja Łukasik, Szymon Gandomi, Amir H. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_10091 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Text-Based Product Matching -- Semi-Supervised Clustering Approach Martinek, Alicja Łukasik, Szymon Gandomi, Amir H. Databases Artificial Intelligence Machine Learning 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. |
| title | Text-Based Product Matching -- Semi-Supervised Clustering Approach |
| topic | Databases Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2402.10091 |