Heterogeneous embedding propagation for large-scale e-commerce user alignment

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...

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Main Authors: ZHENG, Vincent W., SHA, Mo, LI, Yuchen, YANG, Hongxia, FANG, Yuan, ZHANG, Zhenjie, TAN, Kian-Lee, CHANG, Kevin Chen-Chuan
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Language:English
Published: Institutional Knowledge at Singapore Management University 2018
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Online Access:https://ink.library.smu.edu.sg/sis_research/4231
https://ink.library.smu.edu.sg/context/sis_research/article/5234/viewcontent/hep.pdf
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spelling sg-smu-ink.sis_research-52342020-03-27T03:40:06Z Heterogeneous embedding propagation for large-scale e-commerce user alignment ZHENG, Vincent W. SHA, Mo LI, Yuchen YANG, Hongxia FANG, Yuan ZHANG, Zhenjie TAN, Kian-Lee CHANG, Kevin Chen-Chuan 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. 2018-11-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/4231 info:doi/10.1109/ICDM.2018.00198 https://ink.library.smu.edu.sg/context/sis_research/article/5234/viewcontent/hep.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University E-commerce User Alignment Heterogeneous Interaction Graph Heterogeneous Embedding Propagation Databases and Information Systems E-Commerce
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic E-commerce User Alignment
Heterogeneous Interaction Graph
Heterogeneous Embedding Propagation
Databases and Information Systems
E-Commerce
spellingShingle E-commerce User Alignment
Heterogeneous Interaction Graph
Heterogeneous Embedding Propagation
Databases and Information Systems
E-Commerce
ZHENG, Vincent W.
SHA, Mo
LI, Yuchen
YANG, Hongxia
FANG, Yuan
ZHANG, Zhenjie
TAN, Kian-Lee
CHANG, Kevin Chen-Chuan
Heterogeneous embedding propagation for large-scale e-commerce user alignment
description 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.
format text
author ZHENG, Vincent W.
SHA, Mo
LI, Yuchen
YANG, Hongxia
FANG, Yuan
ZHANG, Zhenjie
TAN, Kian-Lee
CHANG, Kevin Chen-Chuan
author_facet ZHENG, Vincent W.
SHA, Mo
LI, Yuchen
YANG, Hongxia
FANG, Yuan
ZHANG, Zhenjie
TAN, Kian-Lee
CHANG, Kevin Chen-Chuan
author_sort ZHENG, Vincent W.
title Heterogeneous embedding propagation for large-scale e-commerce user alignment
title_short Heterogeneous embedding propagation for large-scale e-commerce user alignment
title_full Heterogeneous embedding propagation for large-scale e-commerce user alignment
title_fullStr Heterogeneous embedding propagation for large-scale e-commerce user alignment
title_full_unstemmed Heterogeneous embedding propagation for large-scale e-commerce user alignment
title_sort heterogeneous embedding propagation for large-scale e-commerce user alignment
publisher Institutional Knowledge at Singapore Management University
publishDate 2018
url https://ink.library.smu.edu.sg/sis_research/4231
https://ink.library.smu.edu.sg/context/sis_research/article/5234/viewcontent/hep.pdf
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