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Engineering doc2vec for automatic classification of product descriptions on O2O applications

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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.

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Notes

  1. https://radimrehurek.com/gensim/.

  2. A comprehensive explanation is available in [32]. A visual explanation is available at https://ronxin.github.io/wevi/.

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Acknowledgements

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2016R1D1A1B03931324) and 2017 Hongik University Research Fund.

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Authors and Affiliations

  1. Department of Computer Engineering, Hongik University, 94 Seocho-gu Wowsan-ro, Seoul, South Korea

    Hana Lee & Young Yoon

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  1. Hana Lee
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  2. Young Yoon
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Correspondence to Young Yoon.

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Lee, H., Yoon, Y. Engineering doc2vec for automatic classification of product descriptions on O2O applications. Electron Commer Res 18, 433–456 (2018). https://doi.org/10.1007/s10660-017-9268-5

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