Engineering doc2vec for automatic classification of product descriptions on O2O applications
- Authors
- Lee, Hana; Yoon, Young
- Issue Date
- Sep-2018
- Publisher
- SPRINGER
- Keywords
- O2O application; doc2vec; Online advertisement; Intelligent classification; Paragraph embedding
- Citation
- ELECTRONIC COMMERCE RESEARCH, v.18, no.3, pp.433 - 456
- Journal Title
- ELECTRONIC COMMERCE RESEARCH
- Volume
- 18
- Number
- 3
- Start Page
- 433
- End Page
- 456
- URI
- https://scholarworks.bwise.kr/hongik/handle/2020.sw.hongik/3277
- DOI
- 10.1007/s10660-017-9268-5
- ISSN
- 1389-5753
- 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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