Heterogeneous Embedding Propagation for Large-Scale E-Commerce User Alignment

Vincent W. Zheng, Mo Sha, Yuchen Li, Hongxia Yang, Yuan Fang, Zhenjie Zhang, Kian Lee Tan, Kevin Chen Chuan Chang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We study the important problem of user alignment in e-commerce: to predict whether two online user identities that access an e-commerce site from different devices belong to one real-world person. As input, we have a set of user activity logs from Taobao and some labeled user identity linkages. User activity logs can be modeled using a heterogeneous interaction graph (HIG), and subsequently the user alignment task can be formulated as a semi-supervised HIG embedding problem. HIG embedding is challenging for two reasons: its heterogeneous nature and the presence of edge features. To address the challenges, we propose a novel Heterogeneous Embedding Propagation (HEP) model. The core idea is to iteratively reconstruct a node's embedding from its heterogeneous neighbors in a weighted manner, and meanwhile propagate its embedding updates from reconstruction loss and/or classification loss to its neighbors. We conduct extensive experiments on large-scale datasets from Taobao, demonstrating that HEP significantly outperforms state-of-the-art baselines often by more than 10% in F-scores.

Original languageEnglish (US)
Title of host publication2018 IEEE International Conference on Data Mining, ICDM 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1434-1439
Number of pages6
ISBN (Electronic)9781538691588
DOIs
StatePublished - Dec 27 2018
Event18th IEEE International Conference on Data Mining, ICDM 2018 - Singapore, Singapore
Duration: Nov 17 2018Nov 20 2018

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
Volume2018-November
ISSN (Print)1550-4786

Conference

Conference18th IEEE International Conference on Data Mining, ICDM 2018
CountrySingapore
CitySingapore
Period11/17/1811/20/18

Keywords

  • E-commerce user alignment
  • Heterogeneous embedding propagation
  • Heterogeneous interaction graph

ASJC Scopus subject areas

  • Engineering(all)

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  • Cite this

    Zheng, V. W., Sha, M., Li, Y., Yang, H., Fang, Y., Zhang, Z., Tan, K. L., & Chang, K. C. C. (2018). Heterogeneous Embedding Propagation for Large-Scale E-Commerce User Alignment. In 2018 IEEE International Conference on Data Mining, ICDM 2018 (pp. 1434-1439). [8595007] (Proceedings - IEEE International Conference on Data Mining, ICDM; Vol. 2018-November). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICDM.2018.00198