Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

11-2018

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 Prop- agation (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.

Keywords

E-commerce User Alignment, Heterogeneous Interaction Graph, Heterogeneous Embedding Propagation

Discipline

Databases and Information Systems | E-Commerce

Research Areas

Data Science and Engineering

Publication

2018 IEEE International Conference on Data Mining ICDM: Singapore, November 17-20: Proceedings

First Page

1434

Last Page

1439

ISBN

9781538691595

Identifier

10.1109/ICDM.2018.00198

Publisher

IEEE Computer Society

City or Country

Los Alamos, CA

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1109/ICDM.2018.00198

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