research-article

CityTransfer: Transferring Inter- and Intra-City Knowledge for Chain Store Site Recommendation based on Multi-Source Urban Data

Authors:

Vincent W. Zheng,

Zhiwen YuAuthors Info & Claims

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Volume 1, Issue 4

Article No.: 135, Pages 1 - 23

https://doi.org/10.1145/3161411

Published: 08 January 2018 Publication History

Abstract

Chain businesses have been dominating the market in many parts of the world. It is important to identify the optimal locations for a new chain store. Recently, numerous studies have been done on chain store location recommendation. These studies typically learn a model based on the features of existing chain stores in the city and then predict what other sites are suitable for running a new one. However, these models do not work when a chain enterprise wants to open business in a new city where there is not enough data about this chain store. To solve the cold-start problem, we propose CityTransfer, which transfers chain store knowledge from semantically-relevant domains (e.g., other cities with rich knowledge, similar chain enterprises in the target city) for chain store placement recommendation in a new city. In particular, CityTransfer is a two-fold knowledge transfer framework based on collaborative filtering, which consists of the transfer rating prediction model, the inter-city knowledge association method and the intra-city semantic extraction method. Experiments using data of chain hotels from four different cities crawled from Ctrip (a popular travel reservation website in China) and the urban characters extracted from several other data sources validate the effectiveness of our approach on store site recommendation.

References

[1]

Y. Anzai, “Pattern recognition and machine learning,” Elsevier Press, 2012.

[2]

L. Baltrunas, B. Ludwig, and F. Ricci, “Matrix factorization techniques for context aware recommendation,” in Proc. of the ffth ACM conference on Recommender systems, 2011, pp. 301--304.

Digital Library

[3]

J. Benesty, J. Chen, Y. Huang, and I. Cohen, “Pearson correlation coefficient,” in Proc. of Noise reduction in speech processing, Springer, 2009, pp. 1--4.

[4]

Y. Bengio et al., “Learning deep architectures for AI,” Foundations and Trends in Machine Learning, vol. 2, no. 1, pp. 1--127, 2009.

Digital Library

[5]

B. Berman, J. R. Evans, and J. R. Lowry, “Retail Management: A Strategic Approach,” Prentice-Hall Press, London, 1995.

[6]

J. Bobadilla, F. Ortega, A. Hernando, and J. Bernal, “A collaborative filtering approach to mitigate the new user cold start problem,” Knowledge-Based Systems, vol.26, no. 2, pp. 225--238, 2012.

Digital Library

[7]

L. Bottou, “Large-scale machine learning with stochastic gradient descent,” in Proce. of COMPSTAT‘10, Springer, pp. 177--186, 2010.

[8]

L. Chen, D. Zhang, G. Pan, X. Ma, D. Yang, K. Kushlev, W. Zhang, and S. Li, “Bike sharing station placement leveraging heterogeneous urban open data,” in Proc. of ACM UbiComp‘15, pp. 571--575. 2015.

Digital Library

[9]

W. Chu and S. T. Park, “Personalized recommendation on dynamic content using predictive bilinear models,” in Proc. of WWW‘09, ACM, 2009. in Proc. of IEEE Affective Computing and Intelligent Interaction (ACII‘13), 2013, pp.511--516.

Digital Library

[10]

J. Deng, Z. Zhang, E. Marchi, and B. Schuller, “Sparse Autoencoder-based feature transfer learning for speech emotion recognition,”

[11]

Z. Fan, X. Song, R. Shibasaki, T. Li, and H. Kaneda, “CityCoupling: bridging intercity human mobility,” in Proc. of ACM UbiComp‘16, 2016, pp.718--728.

Digital Library

[12]

Z. Gantner, et al., “Learning attribute-to-feature mappings for cold-start recommendations,” in Proc. of IEEE ICDM‘10, 2010, pp. 176--185.

Digital Library

[13]

D. Goldberg, D. Nichols, B. M. Oki, and D. Terry, “Using collaborative filtering to weave an information tapestry,” Communications of the ACM, vol. 35, no. 12, pp. 61--70, 1992.

Digital Library

[14]

B. Guo, Z. Wang, Z. Yu, Y. Wang, N. Y. Yen, R. Huang, and X. Zhou, “Mobile crowd sensing and computing: The review of an emerging human-powered sensing paradigm,” ACM Computing Surveys (CSUR), vol. 48, no. 1, pp. 1--31, 2015.

Digital Library

[15]

R. Guttman, A. Moukas, and P. Maes, “Agent-mediated electronic commerce: A survey,” Knowledge Engineering Review, vol. 13, no. 3, pp. 147--159, 1998.

Digital Library

[16]

J. Han, J. Pei, and M. Kamber, “Data mining: concepts and techniques,” Elsevier Press, 2011.

[17]

L. Hu, J. Cao, G. Xu, L. Cao, Z. Gu, and C. Zhu, “Personalized recommendation via cross-domain triadic factorization,” in Proc. of ACM WWW‘13, 2013, pp. 595--606.

Digital Library

[18]

P. Jensen, “Network-based predictions of retail store commercial categories and optimal locations,” Physical Review E, vol. 74, no. 3, pp. 1--3, 2006.

[19]

D. Karamshuk, A. Noulas, S. Scellato, V. Nicosia, and C. Mascolo, “Geo-spotting: mining online location-based services for optimal retail store placement,” in Proc. of ACM SIGKDD‘13, 2013, pp.793--801.

Digital Library

[20]

H. A. Karimi, Advanced location-based technologies and services, CRC Press, 2013.

[21]

J. A. Konstan, et al., “GroupLens: applying collaborative filtering to Usenet news,” Communications of the ACM, vol. 40 no. 3, pp. 77--87, 1997.

Digital Library

[22]

Y. Koren, “Factorization meets the neighborhood: a multifaceted collaborative fltering model,” in Proc. of ACM SIGKDD‘08, 2008, pp. 426--434.

Digital Library

[23]

Y. Koren, R. Bell, and C. Volinsky, “Matrix factorization techniques for recommender systems,” Computer, vol. 42, no. 8, pp. 30--37, 2009.

Digital Library

[24]

X. N. Lam, T. Vu, T. D. Le, and A. D. Duong, “Addressing cold-start problem in recommendation systems,” in Proc. of the 2nd international conference on Ubiquitous information management and communication, 2008, pp. 208--211.

Digital Library

[25]

R. Lee, S. Wakamiya, and K. Sumiya, “Urban area characterization based on crowd behavioral lifelogs over Twitter,” Personal and ubiquitous computing, vol. 17, no. 4, pp. 605--620, 2013.

Digital Library

[26]

J. Li, B. Guo, Z. Wang, M. Li, and Z. Yu, “Where to place the next outlet? harnessing cross-space urban data for multi-scale chain store recommendation,” in Proc. of ACM UbiComp‘16: Adjunct, 2016, pp.149--152.

Digital Library

[27]

Y. Li, et al., “Location selection for ambulance stations: a data-driven approach,” in Proc. of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems, 2015, pp. 1--4.

Digital Library

[28]

E. Malmi, T. M. T. Do, and D. Gatica-Perez, “Checking in or checked in: comparing large-scale manual and automatic location disclosure patterns,” in Proc. of the 11th ACM International Conference on Mobile and Ubiquitous Multimedia, 2012, pp. 1--10.

Digital Library

[29]

A.Ng, “Sparse autoencoder,” CS294A Lecture notes, vol. 72, 2011, pp. 1--19.

[30]

S. J. Pan and Q. Yang, “A survey on transfer learning,” IEEE Transactions on knowledge and data engineering, vol. 22, no. 10, 2010, pp. 1345--1359.

Digital Library

[31]

M. J. Pazzani and D. Billsus, “Content-based recommendation systems,” The adaptive web, Springer, Berlin, Heidelberg, 2007, pp. 325--341.

[32]

D. Quercia, L. M. Aiello, and R. Schifanella, “The Emotional and Chromatic Layers of Urban Smells,” in Proc. of ICWSM‘16, 2016, pp. 309--318.

[33]

A. I. Schein, et al., “Methods and metrics for cold-start recommendations,” in Proc. ACM SIGIR‘02, 2002, pp. 253--260.

Digital Library

[34]

X. Su and T. M. Khoshgoftaar, “A survey of collaborative filtering techniques,” Advances in artificial intelligence, no. 4, 2009, pp. 1--19.

Digital Library

[35]

G. H. Toroslu, “A singular value decomposition approach for recommendation systems,” The graduate school of natural and applied sciences of middle east technical university, Ph.D. Dissertation, 2010.

[36]

L. H. Ungar and D. P. Foster, “Clustering methods for collaborative filtering,” in Proc. of the Workshop on Recommendation Systems, AAAI Press, 1998.

[37]

Y. Wei, Y. Zheng, and Q. Yang, “Transfer knowledge between cities,” in Proc. of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016, pp. 1905--1914.

Digital Library

[38]

Y. Wei, Y. Zhu, C. W. Leung, Y. Song, and Q. Yang, “Instilling social to physical: Co-regularized heterogeneous transfer learning,” in Thirtieth AAAI Conference on Artificial Intelligence (AAAI‘16), 2016, pp. 1338--1344.

[39]

Z. Yu, M. Tian, Z. Wang, B. Guo, and T. Mei, “Shop-Type Recommendation Leveraging the Data from Social Media and Location-Based Services,” ACM Transactions on Knowledge Discovery from Data (TKDD), vol. 11, no. 1, 2016, pp. 1--21.

Digital Library

[40]

J. Yuan, Y. Zheng, and X. Xie, “Discovering regions of different functions in a city using human mobility and POIs,” in Proc. of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, 2012, pp.186--194.

Digital Library

[41]

V. W. Zheng, E. W. Xiang, Q. Yang, and D. Shen, “Transferring Localization Models over Time,” in Proc. of AAAI‘08, 2008, pp. 1421--1426.

[42]

Y. Zheng, L. Capra, O. Wolfson, and H. Yang, “Urban computing: concepts, methodologies, and applications,” ACM Transactions on Intelligent Systems and Technology (TIST), vol. 5, no. 3, 2014, pp. 1--55.

Digital Library

Cited By

Li PChang YWei MHsieh H(2024)Estimating Future Financial Development of Urban Areas for Deploying Bank Branches: A Local-Regional Interpretable ModelACM Transactions on Management Information Systems10.1145/365647915:2(1-26)Online publication date: 8-Apr-2024
https://dl.acm.org/doi/10.1145/3656479
Jin JSong YKan DZhang BLyu YZhang JLu H(2024)Learning context-aware region similarity with effective spatial normalization over Point-of-Interest dataInformation Processing & Management10.1016/j.ipm.2024.10367361:3(103673)Online publication date: May-2024
https://doi.org/10.1016/j.ipm.2024.103673
Yang TGao YHuang ZLiu Y(2023)UPTDNetInternational Journal of Intelligent Systems10.1155/2023/90915702023Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1155/2023/9091570
Show More Cited By

Recommendations

User Similarity Adjustment for Improved Recommendations
MIKE 2015: Proceedings of the Third International Conference on Mining Intelligence and Knowledge Exploration - Volume 9468

Recommender systems are becoming more and more attractive in both research and commercial communities due to Information overload problem and the popularity of the Internet applications. Collaborative Filtering, a popular branch of recommendation ...
Typicality-Based Collaborative Filtering Recommendation

Collaborative filtering (CF) is an important and popular technology for recommender systems. However, current CF methods suffer from such problems as data sparsity, recommendation inaccuracy, and big-error in predictions. In this paper, we borrow ideas ...
A hybrid online-product recommendation system: Combining implicit rating-based collaborative filtering and sequential pattern analysis

Many online shopping malls in which explicit rating information is not available still have difficulty in providing recommendation services using collaborative filtering (CF) techniques for their users. Applying temporal purchase patterns derived from ...

Comments

Information & Contributors

Information

Published In

cover image Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Volume 1, Issue 4

December 2017

1298 pages

EISSN:2474-9567

DOI:10.1145/3178157

Issue’s Table of Contents

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 January 2018

Accepted: 01 October 2017

Revised: 01 July 2017

Received: 01 May 2017

Published in IMWUT Volume 1, Issue 4

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed

Funding Sources

National Basic Research Program of China
National Natural Science Foundation of China

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

47
Total Citations
View Citations
1,093
Total Downloads

Downloads (Last 12 months)92
Downloads (Last 6 weeks)2

Reflects downloads up to 09 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Li PChang YWei MHsieh H(2024)Estimating Future Financial Development of Urban Areas for Deploying Bank Branches: A Local-Regional Interpretable ModelACM Transactions on Management Information Systems10.1145/365647915:2(1-26)Online publication date: 8-Apr-2024
https://dl.acm.org/doi/10.1145/3656479
Jin JSong YKan DZhang BLyu YZhang JLu H(2024)Learning context-aware region similarity with effective spatial normalization over Point-of-Interest dataInformation Processing & Management10.1016/j.ipm.2024.10367361:3(103673)Online publication date: May-2024
https://doi.org/10.1016/j.ipm.2024.103673
Yang TGao YHuang ZLiu Y(2023)UPTDNetInternational Journal of Intelligent Systems10.1155/2023/90915702023Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1155/2023/9091570
Yan HWang HZhang DYang YFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)Identifying Regional Driving Risks via Transductive Cross-City Transfer Learning Under Negative TransferProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614924(2877-2886)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3614924
Wang YZhao DRen YZhang DMa H(2023)SPAP: Simultaneous Demand Prediction and Planning for Electric Vehicle Chargers in a New CityACM Transactions on Knowledge Discovery from Data10.1145/356557717:4(1-25)Online publication date: 24-Feb-2023
https://dl.acm.org/doi/10.1145/3565577
Wang QGuo BCheng LYu ZLiu H(2023)CausalSE: Understanding Varied Spatial Effects with Missing Data Toward Adding New Bike-sharing StationsACM Transactions on Knowledge Discovery from Data10.1145/353642717:2(1-24)Online publication date: 20-Mar-2023
https://dl.acm.org/doi/10.1145/3536427
Wang LChai DLiu XChen LChen K(2023)Exploring the Generalizability of Spatio-Temporal Traffic Prediction: Meta-Modeling and an Analytic FrameworkIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2021.313076235:4(3870-3884)Online publication date: 1-Apr-2023
https://doi.org/10.1109/TKDE.2021.3130762
Huang YSong XZhu YZhang SYu J(2023)Traffic Prediction With Transfer Learning: A Mutual Information-Based ApproachIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2023.326639824:8(8236-8252)Online publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1109/TITS.2023.3266398
Gonzalez-Vidal AMendoza-Bernal JNiu SSkarmeta ASong H(2023)A Transfer Learning Framework for Predictive Energy-Related Scenarios in Smart BuildingsIEEE Transactions on Industry Applications10.1109/TIA.2022.317922259:1(26-37)Online publication date: Jan-2023
https://doi.org/10.1109/TIA.2022.3179222
Hsieh HLai BChen YChang Y(2023)Multi-Performance Estimation for Deploying Bank Branches Based on a Multi-Task Attentive Tree-Enhanced ModelIEEE Transactions on Emerging Topics in Computational Intelligence10.1109/TETCI.2022.32145767:1(237-249)Online publication date: Feb-2023
https://doi.org/10.1109/TETCI.2022.3214576
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Issue’s Table of Contents