{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,16]],"date-time":"2026-05-16T00:06:25Z","timestamp":1778889985238,"version":"3.51.4"},"reference-count":36,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2020,7,21]],"date-time":"2020-07-21T00:00:00Z","timestamp":1595289600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Manage. Inf. Syst."],"published-print":{"date-parts":[[2020,9,30]]},"abstract":"<jats:p>\n            E-commerce platforms categorize their products into a multi-level taxonomy tree with thousands of leaf categories. Conventional methods for product categorization are typically based on machine learning\n            <jats:italic>classification<\/jats:italic>\n            algorithms. These algorithms take product information as input (e.g., titles and descriptions) to classify a product into a leaf category. In this article, we propose a new paradigm based on\n            <jats:italic>machine translation<\/jats:italic>\n            . In our approach, we translate a product\u2019s natural language description into a sequence of tokens representing a root-to-leaf path in a product taxonomy. In our experiments on two large real-world datasets, we show that our approach achieves better predictive accuracy than a state-of-the-art classification system for product categorization. In addition, we demonstrate that our machine translation models can propose meaningful new paths between previously unconnected nodes in a taxonomy tree, thereby transforming the taxonomy into a directed acyclic graph. We discuss how the resultant taxonomy directed acyclic graph promotes user-friendly navigation, and how it is more adaptable to new products.\n          <\/jats:p>","DOI":"10.1145\/3382189","type":"journal-article","created":{"date-parts":[[2020,7,22]],"date-time":"2020-07-22T04:08:43Z","timestamp":1595390923000},"page":"1-14","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":17,"title":["E-Commerce Product Categorization via Machine Translation"],"prefix":"10.1145","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7601-903X","authenticated-orcid":false,"given":"Liling","family":"Tan","sequence":"first","affiliation":[{"name":"Rakuten Asia Pte Ltd"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maggie Yundi","family":"Li","sequence":"additional","affiliation":[{"name":"National University of Singapore, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Stanley","family":"Kok","sequence":"additional","affiliation":[{"name":"National University of Singapore, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2020,7,21]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"Chris Anderson. 2006. 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