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Activity Sensor: Check-In Usage Mining for Local Recommendation

Authors:

Changsheng XuAuthors Info & Claims

ACM Transactions on Intelligent Systems and Technology (TIST), Volume 6, Issue 3

Article No.: 41, Pages 1 - 24

https://doi.org/10.1145/2700468

Published: 24 April 2015 Publication History

Abstract

While on the go, people are using their phones as a personal concierge discovering what is around and deciding what to do. Mobile phone has become a recommendation terminal customized for individuals—capable of recommending activities and simplifying the accomplishment of related tasks. In this article, we conduct usage mining on the check-in data, with summarized statistics identifying the local recommendation challenges of huge solution space, sparse available data, and complicated user intent, and discovered observations to motivate the hierarchical, contextual, and sequential solution. We present a point-of-interest (POI) category-transition--based approach, with a goal of estimating the visiting probability of a series of successive POIs conditioned on current user context and sensor context. A mobile local recommendation demo application is deployed. The objective and subjective evaluations validate the effectiveness in providing mobile users both accurate recommendation and favorable user experience.

References

[1]

Gediminas Adomavicius and Alexander Tuzhilin. 2005. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering 17, 6, 734--749.

Digital Library

[2]

Deepak Agarwal and Bee-Chung Chen. 2009. Regression-based latent factor models. In Proceedings of KDD. 19--28.

Digital Library

[3]

Ana O. Alves, Filipe Rodrigues, and Francisco C. Pereira. 2011. Tagging space from information extraction and popularity of points of interest. In Proceedings of AmI. 115--125.

Digital Library

[4]

Jie Bao, Yu Zheng, David Wilkie, and Mohamed F. Mokbel. 2013. A survey on recommendations in location-based social networks. Submitted to Geoinformatica.

Digital Library

[5]

Cristiana Bolchini, Carlo Curino, Elisa Quintarelli, Fabio A. Schreiber, and Letizia Tanca. 2007. A data-oriented survey of context models. ACM SIGMOD Record 36, 4, 19--26.

Digital Library

[6]

José Borges and Mark Levene. 2010. A comparison of scoring metrics for predicting the next navigation step with Markov model-based systems. International Journal of Information Technology and Decision Making 9, 4, 547--573.

[7]

Ian L. Bradley. 1971. Repetition as a factor in the development of musical preferences. Journal of Research in Music Education 19, 3, 295--298.

[8]

Jonathan Chang and Eric Sun. 2011. Location3: How users share and respond to location-based data on social. In Proceedings of ICWSM.

[9]

Kai-Ping Chang, Ling-Yin Wei, Mi-Yeh Yeh, and Wen-Chih Peng. 2011. Discovering personalized routes from trajectories. In Proceedings of GIS-LBSN. 33--40.

Digital Library

[10]

Chen Cheng, Haiqin Yang, Irwin King, and Michael R. Lyu. 2012. Fused matrix factorization with geographical and social influence in location-based social networks. In Proceedings of AAAI.

Digital Library

[11]

Chen Cheng, Haiqin Yang, Michael R. Lyu, and Irwin King. 2013. Where you like to go next: Successive point-of-interest recommendation. In Proceedings of IJCAI. 2605--2611.

Digital Library

[12]

Eunjoon Cho, Seth A. Myers, and Jure Leskovec. 2011. Friendship and mobility: User movement in location-based social networks. In Proceedings of KDD. 1082--1090.

Digital Library

[13]

Robert Cooley, Bamshad Mobasher, and Jaideep Srivastava. 1999. Data preparation for mining World Wide Web browsing patterns. Knowledge and Information Systems 1, 1, 5--32.

Digital Library

[14]

Justin Cranshaw, Eran Toch, Jason I. Hong, Aniket Kittur, and Norman M. Sadeh. 2010. Bridging the gap between physical location and online social networks. In Proceedings of UbiComp. 119--128.

Digital Library

[15]

James Davidson, Benjamin Liebald, Junning Liu, Palash Nandy, Taylor Van Vleet, Ullas Gargi, Sujoy Gupta, Yu He, Mike Lambert, Blake Livingston, and Dasarathi Sampath. 2010. The YouTube video recommendation system. In Proceedings of RecSys. 293--296.

Digital Library

[16]

Huiji Gao, Jiliang Tang, and Huan Liu. 2012. gSCorr: Modeling geo-social correlations for new check-ins on location-based social networks. In Proceedings of CIKM. 1582--1586.

Digital Library

[17]

Marta C. Gonzalez, Cesar A. Hidalgo, and Albert-Laszlo Barabasi. 2008. Understanding individual human mobility patterns. Nature 453, 7196, 779--782.

[18]

Tzvetan Horozov, Nitya Narasimhan, and Venu Vasudevan. 2006. Using location for personalized POI recommendations in mobile environments. In Proceedings of SAINT. 124--129.

Digital Library

[19]

Yajie Hu and Mitsunori Ogihara. 2011. NextOne player: A music recommendation system based on user behavior. In Proceedings of ISMIR. 103--108.

[20]

Takeshi Kurashima, Tomoharu Iwata, Takahide Hoshide, Noriko Takaya, and Ko Fujimura. 2013. Geo topic model: Joint modeling of user’s activity area and interests for location recommendation. In Proceedings of WSDM. 375--384.

Digital Library

[21]

Nicholas D. Lane, Dimitrios Lymberopoulos, Feng Zhao, and Andrew T. Campbell. 2010. Hapori: Context-based local search for mobile phones using community behavioral modeling and similarity. In Proceedings of UbiComp. 109--118.

Digital Library

[22]

Georgios Lappas. 2011. From Web mining to social multimedia mining. In Proceedings of ASONAM. 336--343.

Digital Library

[23]

Neal Lathia, Stephen Hailes, Licia Capra, and Xavier Amatriain. 2010. Temporal diversity in recommender systems. In Proceedings of SIGIR. 210--217.

Digital Library

[24]

Defu Lian and Xing Xie. 2011. Collaborative activity recognition via check-in history. In Proceedings of LBSN.

Digital Library

[25]

Bin Liu and Hui Xiong. 2013. Point-of-interest recommendation in location based social networks with topic and location awareness. In Proceedings of SDM. 396--404.

[26]

Anastasios Noulas, Salvatore Scellato, Cecilia Mascolo, and Massimiliano Pontil. 2011. An empirical study of geographic user activity patterns in foursquare. In Proceedings of ICWSM.

[27]

Sung Eun Park, Sangkeun Lee, and Sang Goo Lee. 2011. Session-based collaborative filtering for predicting the next song. In Proceedings of CNSI. 353--358.

Digital Library

[28]

Steffen Rendle, Christoph Freudenthaler, and Lars Schmidt-Thieme. 2010. Factorizing personalized Markov chains for next-basket recommendation. In Proceedings of WWW. 811--820.

Digital Library

[29]

Adam Sadilek, Henry A. Kautz, and Jeffrey P. Bigham. 2012. Finding your friends and following them to where you are. In Proceedings of WSDM. 723--732.

Digital Library

[30]

Jitao Sang, Tao Mei, Ying-Qing Xu, Chen Zhao, Changsheng Xu, and Shipeng Li. 2013. Interaction design for mobile visual search. IEEE Transactions on Multimedia 15, 7, 1665--1676.

Digital Library

[31]

Jitao Sang and Changsheng Xu. 2012. Right buddy makes the difference: An early exploration of social relation analysis in multimedia applications. In Proceedings of MM. 19--28.

Digital Library

[32]

Jitao Sang, Changsheng Xu, and Jing Liu. 2012. User-aware image tag refinement via ternary semantic analysis. IEEE Transactions on Multimedia 14, 3--2, 883--895.

Digital Library

[33]

Fabrizio Sebastiani. 2002. Machine learning in automated text categorization. ACM Computing Surveys 34, 1, 1--47.

Digital Library

[34]

Panagiotis Symeonidis, Antonis Krinis, and Yannis Manolopoulos. 2013. GeoSocialRec: Explaining recommendations in location-based social networks. In Proceedings of ADBIS. 84--97.

Digital Library

[35]

Xiaozhe Wang, Ajith Abraham, and Kate A. Smith. 2005. Intelligent Web traffic mining and analysis. Journal of Network and Computer Applications 28, 2, 147--165.

Digital Library

[36]

Ling-Yin Wei, Yu Zheng, and Wen-Chih Peng. 2012. Constructing popular routes from uncertain trajectories. In Proceedings of KDD. 195--203.

Digital Library

[37]

Ian H. Witten and Eibe Frank. 2005. Data Mining: Practical Machine Learning Tools and Techniques (2nd ed.). Morgan Kaufmann, San Francisco, CA.

Digital Library

[38]

Chang Xu, Dacheng Tao, and Chao Xu. 2014. Large-margin multi-view information bottleneck. IEEE Transactions on Pattern Analysis and Machine Intelligence 36, 8, 1559--1572.

Digital Library

[39]

Mao Ye, Peifeng Yin, and Wang-Chien Lee. 2010. Location recommendation for location-based social networks. In Proceedings of GIS. 458--461.

Digital Library

[40]

Mao Ye, Peifeng Yin, Wang-Chien Lee, and Dik Lun Lee. 2011. Exploiting geographical influence for collaborative point-of-interest recommendation. In Proceedings of SIGIR. 325--334.

Digital Library

[41]

Nicholas Jing Yuan, Fuzheng Zhang, Defu Lian, Kai Zheng, Siyu Yu, and Xing Xie. 2013. We know how you live: Exploring the spectrum of urban lifestyles. In Proceedings of COSN. 3--14.

Digital Library

[42]

Vincent Wenchen Zheng, Bin Cao, Yu Zheng, Xing Xie, and Qiang Yang. 2010a. Collaborative filtering meets mobile recommendation: A user-centered approach. In Proceedings of AAAI.

Digital Library

[43]

Vincent Wenchen Zheng, Yu Zheng, Xing Xie, and Qiang Yang. 2010b. Collaborative location and activity recommendations with GPS history data. In Proceedings of WWW. 1029--1038.

Digital Library

[44]

Yu Zheng and Xing Xie. 2011. Learning travel recommendations from user-generated GPS traces. ACM Transactions on Intelligent Systems and Technology 2, 1, Article No. 2.

Digital Library

[45]

Tianyi Zhou, Wei Bian, and Dacheng Tao. 2013. Divide-and-conquer anchoring for near-separable nonnegative matrix factorization and completion in high dimensions. In Proceedings of ICDM. 917--926.

[46]

Jinfeng Zhuang, Tao Mei, Steven C. H. Hoi, Ying-Qing Xu, and Shipeng Li. 2011. When recommendation meets mobile: Contextual and personalized recommendation on the go. In Proceedings of UbiComp. 153--162.

Digital Library

Cited By

Chen HHuang JDeng QWang JKong LDeng X(2023)Multi-granularity user interest modeling and interest drift detectionIntelligent Data Analysis10.3233/IDA-21651727:2(555-577)Online publication date: 15-Mar-2023
https://dl.acm.org/doi/10.3233/IDA-216517
Afzali JDrzewiecki ABalog KDiaz FShah CSuel TCastells PJones RSakai T(2021)POINTREC: A Test Collection for Narrative-driven Point of Interest RecommendationProceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3404835.3463243(2478-2484)Online publication date: 11-Jul-2021
https://dl.acm.org/doi/10.1145/3404835.3463243
Ferro-Diez LVillegas NDiaz-Cely J(2020)Location Data Analytics in the Business Value Chain: A Systematic Literature ReviewIEEE Access10.1109/ACCESS.2020.30368358(204639-204659)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.3036835
Show More Cited By

Index Terms

Activity Sensor: Check-In Usage Mining for Local Recommendation
1. Information systems
  1. Information systems applications

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Information & Contributors

Information

Published In

cover image ACM Transactions on Intelligent Systems and Technology

ACM Transactions on Intelligent Systems and Technology Volume 6, Issue 3

Survey Paper, Regular Papers and Special Section on Participatory Sensing and Crowd Intelligence

May 2015

319 pages

ISSN:2157-6904

EISSN:2157-6912

DOI:10.1145/2764959

Editor:
Huan Liu
Arizona State University, USA

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Copyright © 2015 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 24 April 2015

Accepted: 01 September 2014

Revised: 01 September 2014

Received: 01 December 2013

Published in TIST Volume 6, Issue 3

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Funding Sources

Beijing Natural Science Foundation (4131004)
National Natural Science Foundation of China (61432019, 61225009, 61303176, 61272256)
National Basic Research Program of China (2012CB316304)

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Cited By

Chen HHuang JDeng QWang JKong LDeng X(2023)Multi-granularity user interest modeling and interest drift detectionIntelligent Data Analysis10.3233/IDA-21651727:2(555-577)Online publication date: 15-Mar-2023
https://dl.acm.org/doi/10.3233/IDA-216517
Afzali JDrzewiecki ABalog KDiaz FShah CSuel TCastells PJones RSakai T(2021)POINTREC: A Test Collection for Narrative-driven Point of Interest RecommendationProceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3404835.3463243(2478-2484)Online publication date: 11-Jul-2021
https://dl.acm.org/doi/10.1145/3404835.3463243
Ferro-Diez LVillegas NDiaz-Cely J(2020)Location Data Analytics in the Business Value Chain: A Systematic Literature ReviewIEEE Access10.1109/ACCESS.2020.30368358(204639-204659)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.3036835
Chen H(2019)Modeling and Detecting Drift in User Interest Based on Hierarchical Classification2019 IEEE 4th International Conference on Big Data Analytics (ICBDA)10.1109/ICBDA.2019.8713190(246-251)Online publication date: Mar-2019
https://doi.org/10.1109/ICBDA.2019.8713190
Chen JYing PZou M(2019)Improving music recommendation by incorporating social influenceMultimedia Tools and Applications10.1007/s11042-018-5745-778:3(2667-2687)Online publication date: 1-Feb-2019
https://dl.acm.org/doi/10.1007/s11042-018-5745-7
Falcão AWanderley Pda Silva Leite Tde Souza Baptista Cde Queiroz Jde Oliveira MRocha J(2019)Crowdsourcing Urban Issues in Smart Cities: A Usability Assessment of the Crowd4City SystemElectronic Government and the Information Systems Perspective10.1007/978-3-030-27523-5_11(147-159)Online publication date: 26-Aug-2019
https://dl.acm.org/doi/10.1007/978-3-030-27523-5_11
Nurbakova DMitrovic TZhang JChen LChin D(2018)Recommendation of Activity Sequences during Distributed EventsProceedings of the 26th Conference on User Modeling, Adaptation and Personalization10.1145/3209219.3213592(261-264)Online publication date: 3-Jul-2018
https://dl.acm.org/doi/10.1145/3209219.3213592
Falcão Ade Souza Baptista Cde Oliveira MRocha Jda Silva Leite Tde Queiroz JHaddad HWainwright RChbeir R(2018)Towards a reputation model applied to geosocial networksProceedings of the 33rd Annual ACM Symposium on Applied Computing10.1145/3167132.3167319(1756-1763)Online publication date: 9-Apr-2018
https://dl.acm.org/doi/10.1145/3167132.3167319
Gu FNiu JDas SHe Z(2018)RunnerPal: A Runner Monitoring and Advisory System Based on Smart DevicesIEEE Transactions on Services Computing10.1109/TSC.2016.262637211:2(262-276)Online publication date: 1-Mar-2018
https://doi.org/10.1109/TSC.2016.2626372
Guan JXu MKong X(2018)Learning social regularized user representation in recommender systemSignal Processing10.1016/j.sigpro.2017.09.015144:C(306-310)Online publication date: 1-Mar-2018
https://dl.acm.org/doi/10.1016/j.sigpro.2017.09.015
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