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Engineering doc2vec for automatic classification of product descriptions on O2O applications

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  • Hana Lee

    (Hongik University)

  • Young Yoon

    (Hongik University)

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.

Suggested Citation

  • Hana Lee & Young Yoon, 2018. "Engineering doc2vec for automatic classification of product descriptions on O2O applications," Electronic Commerce Research, Springer, vol. 18(3), pages 433-456, September.
    • Handle: RePEc:spr:elcore:v:18:y:2018:i:3:d:10.1007_s10660-017-9268-5
    DOI: 10.1007/s10660-017-9268-5
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    References listed on IDEAS

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    1. David A. Hull, 1996. "Stemming algorithms: A case study for detailed evaluation," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 47(1), pages 70-84, January.
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    Cited by:

    1. Guo Li & Na Li, 2019. "Customs classification for cross-border e-commerce based on text-image adaptive convolutional neural network," Electronic Commerce Research, Springer, vol. 19(4), pages 779-800, December.

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