Engineering doc2vec for automatic classification of product descriptions on O2O applications

Hana Lee () and Young Yoon ()
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Hana Lee: Hongik University
Young Yoon: Hongik University

Electronic Commerce Research, 2018, vol. 18, issue 3, No 1, 433-456

Abstract: 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.

Keywords: O2O application; doc2vec; Online advertisement; Intelligent classification; Paragraph embedding (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1007/s10660-017-9268-5

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