research-article

Your Cart tells You: Inferring Demographic Attributes from Purchase Data

Authors:

Xueqi ChengAuthors Info & Claims

WSDM '16: Proceedings of the Ninth ACM International Conference on Web Search and Data Mining

Pages 173 - 182

https://doi.org/10.1145/2835776.2835783

Published: 08 February 2016 Publication History

Abstract

Demographic attributes play an important role in retail market to characterize different types of users. Such signals however are often only available for a small fraction of users in practice due to the difficulty in manual collection process by retailers. In this paper, we aim to harness the power of big data to automatically infer users' demographic attributes based on their purchase data. Typically, demographic prediction can be formalized as a multi-task multi-class prediction problem, i.e., multiple demographic attributes (e.g., gender, age and income) are to be inferred for each user where each attribute may belong to one of N possible classes (N-2). Most previous work on this problem explores different types of features and usually predicts different attributes independently. However, modeling the tasks separately may lose the ability to leverage the correlations among different attributes. Meanwhile, manually defined features require professional knowledge and often suffer from under specification. To address these problems, we propose a novel Structured Neural Embedding (SNE) model to automatically learn the representations from users' purchase data for predicting multiple demographic attributes simultaneously. Experiments are conducted on a real-world retail dataset where five attributes (gender, marital status, income, age, and education level) are to be predicted. The empirical results show that our SNE model can improve the performance significantly compared with state-of-the-art baselines.

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Cited By

Zhang JZhang XHong DGupta RShang J(2025)Contextual Inference From Sparse Shopping Transactions Based on Motif PatternsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.345263837:2(572-583)Online publication date: Feb-2025
https://doi.org/10.1109/TKDE.2024.3452638
Shi TZhang YZhang JFeng FHe XHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Fair Recommendations with Limited Sensitive Attributes: A Distributionally Robust Optimization ApproachProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657822(448-457)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657822
Zou GWu LHu SYang SZhou CGan YZhang B(2024)User Profiling for Personalized Service Recommendation with Dual High-order Feature Learning2024 IEEE International Conference on Web Services (ICWS)10.1109/ICWS62655.2024.00049(269-280)Online publication date: 7-Jul-2024
https://doi.org/10.1109/ICWS62655.2024.00049
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Index Terms

Your Cart tells You: Inferring Demographic Attributes from Purchase Data
1. Information systems
  1. Information systems applications
    1. Data mining

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cover image ACM Conferences

WSDM '16: Proceedings of the Ninth ACM International Conference on Web Search and Data Mining

February 2016

746 pages

ISBN:9781450337168

DOI:10.1145/2835776

General Chairs:
Paul N. Bennett
Microsoft Research
,
Vanja Josifovski
Pinterest
,
Program Chairs:
Jennifer Neville
Purdue University
,
Filip Radlinski
Microsoft

Copyright © 2016 ACM.

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Published: 08 February 2016

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863 Program of China
973 Program of China
National Natural Science Foundation of China

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WSDM 2016

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WSDM 2016: Ninth ACM International Conference on Web Search and Data Mining

February 22 - 25, 2016

California, San Francisco, USA

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WSDM '16 Paper Acceptance Rate 67 of 368 submissions, 18%;

Overall Acceptance Rate 498 of 2,863 submissions, 17%

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Cited By

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https://doi.org/10.1109/TKDE.2024.3452638
Shi TZhang YZhang JFeng FHe XHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Fair Recommendations with Limited Sensitive Attributes: A Distributionally Robust Optimization ApproachProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657822(448-457)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657822
Zou GWu LHu SYang SZhou CGan YZhang B(2024)User Profiling for Personalized Service Recommendation with Dual High-order Feature Learning2024 IEEE International Conference on Web Services (ICWS)10.1109/ICWS62655.2024.00049(269-280)Online publication date: 7-Jul-2024
https://doi.org/10.1109/ICWS62655.2024.00049
Zhang BRasouli SFeng T(2024)Social demographics imputation based on similarity in multi-dimensional activity-travel pattern: A two-step approachTravel Behaviour and Society10.1016/j.tbs.2024.10084337(100843)Online publication date: Oct-2024
https://doi.org/10.1016/j.tbs.2024.100843
Zhang JZhang XHong DGupta RShang JElkind E(2023)Minimally supervised contextual inference from human mobilityProceedings of the Thirty-Second International Joint Conference on Artificial Intelligence10.24963/ijcai.2023/272(2450-2458)Online publication date: 19-Aug-2023
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Yin SMa W(2022)Attribute Inference Based on User Similarity and Random Walk2022 IEEE International Conference on Services Computing (SCC)10.1109/SCC55611.2022.00040(215-220)Online publication date: Jul-2022
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Ding SGao XDong YTong YFu X(2021)Estimating Multiple Socioeconomic Attributes via Home Location—A Case Study in ChinaJournal of Social Computing10.23919/JSC.2021.00032:1(71-88)Online publication date: Mar-2021
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