Engineering doc2vec for automatic classification of product descriptions on O2O applications
DC Field | Value | Language |
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dc.contributor.author | Lee, Hana | - |
dc.contributor.author | Yoon, Young | - |
dc.date.available | 2020-07-10T04:19:20Z | - |
dc.date.created | 2020-07-06 | - |
dc.date.issued | 2018-09 | - |
dc.identifier.issn | 1389-5753 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hongik/handle/2020.sw.hongik/3277 | - |
dc.description.abstract | In this paper, we develop an automatic product classifier that can become a vital part of a natural user interface for an integrated online-to-offline (O2O) service platform. We devise a novel feature extraction technique to represent product descriptions that are expressed in full natural language sentences. We specifically adapt doc2vec algorithm that implements the document embedding technique. Doc2vec is a way to predict a vector of salient contexts that are specific to a document. Our classifier is trained to classify a product description based on the doc2vec-based feature that is augmented in various ways. We trained and tested our classifier with up to 53,000 real product descriptions from Groupon, a popular social commerce site that also offers O2O commerce features such as online ordering for in-store pick-up. Compared to the baseline approaches of using bag-of-words modeling and word-level embedding, our classifier showed significant improvement in terms of classification accuracy when our adapted doc2vec-based feature was used. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | SPRINGER | - |
dc.title | Engineering doc2vec for automatic classification of product descriptions on O2O applications | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Yoon, Young | - |
dc.identifier.doi | 10.1007/s10660-017-9268-5 | - |
dc.identifier.scopusid | 2-s2.0-85028559377 | - |
dc.identifier.wosid | 000440028100001 | - |
dc.identifier.bibliographicCitation | ELECTRONIC COMMERCE RESEARCH, v.18, no.3, pp.433 - 456 | - |
dc.relation.isPartOf | ELECTRONIC COMMERCE RESEARCH | - |
dc.citation.title | ELECTRONIC COMMERCE RESEARCH | - |
dc.citation.volume | 18 | - |
dc.citation.number | 3 | - |
dc.citation.startPage | 433 | - |
dc.citation.endPage | 456 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | ssci | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Business & Economics | - |
dc.relation.journalWebOfScienceCategory | Business | - |
dc.relation.journalWebOfScienceCategory | Management | - |
dc.subject.keywordAuthor | O2O application | - |
dc.subject.keywordAuthor | doc2vec | - |
dc.subject.keywordAuthor | Online advertisement | - |
dc.subject.keywordAuthor | Intelligent classification | - |
dc.subject.keywordAuthor | Paragraph embedding | - |
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