research-article

Mining Shopping Patterns for Divergent Urban Regions by Incorporating Mobility Data

Authors:

Jiebo LuoAuthors Info & Claims

CIKM '16: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management

Pages 569 - 578

https://doi.org/10.1145/2983323.2983803

Published: 24 October 2016 Publication History

Abstract

What people buy is an important aspect or view of lifestyles. Studying people's shopping patterns in different urban regions can not only provide valuable information for various commercial opportunities, but also enable a better understanding about urban infrastructure and urban lifestyle. In this paper, we aim to predict citywide shopping patterns. This is a challenging task due to the sparsity of the available data -- over 60% of the city regions are unknown for their shopping records. To address this problem, we incorporate another important view of human lifestyles, namely mobility patterns. With information on "where people go", we infer "what people buy". Moreover, to model the relations between regions, we exploit spatial interactions in our method. To that end, Collective Matrix Factorization (CMF) with an interaction regularization model is applied to fuse the data from multiple views or sources. Our experimental results have shown that our model outperforms the baseline methods on two standard metrics. Our prediction results on multiple shopping patterns reveal the divergent demands in different urban regions, and thus reflect key functional characteristics of a city. Furthermore, we are able to extract the connection between the two views of lifestyles, and achieve a better or novel understanding of urban lifestyles.

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Cited By

Yao YZhang HShi XChen JLi WSong XShibasaki R(2023)LTP-Net: Life-Travel Pattern Based Human Mobility Signature IdentificationIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2023.330383524:12(14306-14319)Online publication date: Dec-2023
https://doi.org/10.1109/TITS.2023.3303835
Li WShi XHuang DShen XChen JKobayashi HZhang HSong XShibasaki R(2023)PredLife: Predicting Fine-Grained Future Activity PatternsIEEE Transactions on Big Data10.1109/TBDATA.2023.33102419:6(1658-1669)Online publication date: Dec-2023
https://doi.org/10.1109/TBDATA.2023.3310241
Li WZhang HChen JLi PYao YShi XShibasaki MKobayashi HSong XShibasaki R(2023)Metagraph-Based Life Pattern Clustering With Big Human Mobility DataIEEE Transactions on Big Data10.1109/TBDATA.2022.31557529:1(227-240)Online publication date: 1-Feb-2023
https://doi.org/10.1109/TBDATA.2022.3155752
Show More Cited By

Index Terms

Mining Shopping Patterns for Divergent Urban Regions by Incorporating Mobility Data
1. Human-centered computing
  1. Collaborative and social computing
    1. Empirical studies in collaborative and social computing
2. Information systems
  1. World Wide Web
    1. Web applications
      1. Social networks
    2. Web searching and information discovery
      1. Collaborative filtering

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cover image ACM Conferences

CIKM '16: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management

October 2016

2566 pages

ISBN:9781450340731

DOI:10.1145/2983323

General Chairs:
Snehasis Mukhopadhyay
Indiana University Purdue University Indianapolis, USA
,
ChengXiang Zhai
University of Illinois at Urbana-Champaign, USA
,
Program Chairs:
Elisa Bertino
Purdue University
,
Fabio Crestani
University of Lugano
,
Javed Mostafa
University of North Carolina
,
Jie Tang
Tsinghua University
,
Luo Si
Alibaba Group Inc & Purdue University
,
Xiaofang Zhou
University of Queensland
,
Yi Chang
Yahoo Research
,
Yunyao Li
IBM Research - Almaden
,
Parikshit Sondhi
WalmartLabs

Copyright © 2016 ACM.

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Published: 24 October 2016

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October 24 - 28, 2016

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CIKM '16 Paper Acceptance Rate 160 of 701 submissions, 23%;

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Cited By

Yao YZhang HShi XChen JLi WSong XShibasaki R(2023)LTP-Net: Life-Travel Pattern Based Human Mobility Signature IdentificationIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2023.330383524:12(14306-14319)Online publication date: Dec-2023
https://doi.org/10.1109/TITS.2023.3303835
Li WShi XHuang DShen XChen JKobayashi HZhang HSong XShibasaki R(2023)PredLife: Predicting Fine-Grained Future Activity PatternsIEEE Transactions on Big Data10.1109/TBDATA.2023.33102419:6(1658-1669)Online publication date: Dec-2023
https://doi.org/10.1109/TBDATA.2023.3310241
Li WZhang HChen JLi PYao YShi XShibasaki MKobayashi HSong XShibasaki R(2023)Metagraph-Based Life Pattern Clustering With Big Human Mobility DataIEEE Transactions on Big Data10.1109/TBDATA.2022.31557529:1(227-240)Online publication date: 1-Feb-2023
https://doi.org/10.1109/TBDATA.2022.3155752
Nie YYang WChen ZLu NHuang LHuang H(2022)Public Curb Parking Demand Estimation With POI DistributionIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2020.304684123:5(4614-4624)Online publication date: May-2022
https://doi.org/10.1109/TITS.2020.3046841
Yao YShibasaki RZhang H(2022)Travel similarity estimation and clusteringBig Data and Mobility as a Service10.1016/B978-0-323-90169-7.00004-X(77-111)Online publication date: 2022
https://doi.org/10.1016/B978-0-323-90169-7.00004-X
Zhao XJiao PZhang YZhou C(2021)Exploring Passengers’ Dependency Variety on Stations’ Functions in Urban SubwayJournal of Advanced Transportation10.1155/2021/17335792021(1-14)Online publication date: 13-Oct-2021
https://doi.org/10.1155/2021/1733579
Wang LZhang YZhao XLiu HZhang K(2020)Irregular Travel Groups Detection Based on Cascade Clustering in Urban SubwayIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2019.293349721:5(2216-2225)Online publication date: May-2020
https://doi.org/10.1109/TITS.2019.2933497
Xu SCao JLegg PLiu BLi S(2020)Venue2Vec: An Efficient Embedding Model for Fine-Grained User Location Prediction in Geo-Social NetworksIEEE Systems Journal10.1109/JSYST.2019.291308014:2(1740-1751)Online publication date: Jun-2020
https://doi.org/10.1109/JSYST.2019.2913080
Xu SFu XCao JLiu BWang Z(2020)Survey on user location prediction based on geo-social networking dataWorld Wide Web10.1007/s11280-019-00777-8Online publication date: 31-Jan-2020
https://doi.org/10.1007/s11280-019-00777-8
Wang YSiriaraya PMittal MXie HKawai Y(2020)Understanding Multilingual Correlation of Geo-Tagged Tweets for POI RecommendationWeb and Wireless Geographical Information Systems10.1007/978-3-030-60952-8_14(135-144)Online publication date: 22-Oct-2020
https://doi.org/10.1007/978-3-030-60952-8_14
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