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An Experimental Study of Text Representation Methods for Cross-Site Purchase Preference Prediction Using the Social Text Data

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Abstract

Nowadays, many e-commerce websites allow users to login with their existing social networking accounts. When a new user comes to an e-commerce website, it is interesting to study whether the information from external social media platforms can be utilized to alleviate the cold-start problem. In this paper, we focus on a specific task on cross-site information sharing, i.e., leveraging the text posted by a user on the social media platform (termed as social text) to infer his/her purchase preference of product categories on an e-commerce platform. To solve the task, a key problem is how to effectively represent the social text in a way that its information can be utilized on the e-commerce platform. We study two major kinds of text representation methods for predicting cross-site purchase preference, including shallow textual features and deep textual features learned by deep neural network models. We conduct extensive experiments on a large linked dataset, and our experimental results indicate that it is promising to utilize the social text for predicting purchase preference. Specially, the deep neural network approach has shown a more powerful predictive ability when the number of categories becomes large.

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Authors and Affiliations

  1. School of Information, Renmin University of China, Beijing, 100872, China

    Ting Bai, Hong-Jian Dou, Wayne Xin Zhao, Ding-Yi Yang & Ji-Rong Wen

  2. Beijing Key Laboratory of Big Data Management and Analysis Methods, Beijing, 100872, China

    Ting Bai, Hong-Jian Dou, Wayne Xin Zhao & Ji-Rong Wen

  3. Guangdong Key Laboratory of Big Data Analysis and Processing, Guangzhou, 510006, China

    Wayne Xin Zhao

Authors
  1. Ting Bai
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  2. Hong-Jian Dou
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  3. Wayne Xin Zhao
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Correspondence to Wayne Xin Zhao.

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Bai, T., Dou, HJ., Zhao, W.X. et al. An Experimental Study of Text Representation Methods for Cross-Site Purchase Preference Prediction Using the Social Text Data. J. Comput. Sci. Technol. 32, 828–842 (2017). https://doi.org/10.1007/s11390-017-1763-6

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  • DOI: https://doi.org/10.1007/s11390-017-1763-6

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