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A Neural Labeled Network Embedding Approach to Product Adopter Prediction

  • Qi Gu
  • Ting Bai
  • Wayne Xin ZhaoEmail author
  • Ji-Rong Wen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11292)

Abstract

On e-commerce websites, it is common to see that a user purchases products for others. The person who actually uses the product is called the adopter. Product adopter information is important for learning user interests and understanding purchase behaviors. However, effective acquisition or prediction of product adopter information has not been well studied. Existing methods mainly rely on explicit extraction patterns, and can only identify exact occurrences of adopter mentions from review data. In this paper, we propose a novel Neural Labeled Network Embedding approach (NLNE) to inferring product adopter information from purchase records. Compared with previous studies, our method does not require any review text data, but try to learn effective prediction model using only purchase records, which are easier to obtain than review data. Specially, we first propose an Adopter-labeled User-Product Network Embedding (APUNE) method to learn effective representations for users, products and adopter labels. Then, we further propose a neural prediction approach for inferring product adopter information based on the learned embeddings using APUNE. Our NLNE approach not only retains the expressive capacity of labeled network embedding, but also is endowed with the predictive capacity of neural networks. Extensive experiments on two real-world datasets (i.e., JingDong and Amazon) demonstrate the effectiveness of our model for the studied task.

Keywords

Labeled network embedding Product adopters Neural network e-commerce 

Notes

Acknowledgement

This work was partially supported by the National Natural Science Foundation of China under Grant No. 61502502, the National Basic Research 973 Program of China under Grant No. 2014CB340403 and the Beijing Natural Science Foundation under Grant No. 4162032. Ting Bai was supported by the Outstanding Innovative Talents Cultivation Funded Programs 2016 of Renmin University of China.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Qi Gu
    • 1
    • 2
  • Ting Bai
    • 1
    • 2
  • Wayne Xin Zhao
    • 1
    • 2
    Email author
  • Ji-Rong Wen
    • 1
    • 2
  1. 1.School of InformationRenmin University of ChinaBeijingChina
  2. 2.Beijing Key Laboratory of Big Data Management and Analysis MethodsBeijingChina

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