Extracting Mobile User Profile using Easy-to-obtain and Less Invasive Data
Authors: 
Leonardo J. A. S Figueiredo, Raissa P. P. S. Figueiredo, Germano B. dos Santos, Fabrício A. Silva, Thais R. M. B. Silva, Antonio A. F. LoureiroAuthors Info & Claims
Leonardo J. A. S Figueiredo, Raissa P. P. S. Figueiredo, Germano B. dos Santos, Fabrício A. Silva, Thais R. M. B. Silva, Antonio A. F. LoureiroAuthors Info & ClaimsPages 53 - 60
Abstract
In recent years, we have been facing a significant increase in the interest of the research community and industry in geospatial data. Using this type of data to extract the behavior of mobile users benefits them when this knowledge is used on their behalf. For example, companies can offer personalized and location-based services based on the user's mobile profile. Thus, building a user profile regarding their behavior and mobile interests is paramount to improving the offered services and guiding future applications such as Intelligent Transportation Systems. However, mobile profiles require a large amount of geospatial data that is not always available or usually invades the user's privacy. To mitigate this problem, in this work, we investigate if it is possible to use the users' city and mobile apps to infer their mobility behaviors and consumption pattern. For this purpose, we created models based on four perspectives: device price, categories of visited venues, visited functional areas, and commuting patterns. The results revealed that our models can infer the users' mobile profiles with good precision and recall values, considering only less invasive information.
References
[1]
Licia Amichi, Aline Carneiro Viana, Mark Crovella, and Antonio A.F. Loureiro. 2020. Understanding Individuals' Proclivity for Novelty Seeking (SIGSPATIAL '20). Association for Computing Machinery, New York, NY, USA, 314--324. https://doi.org/10.1145/3397536.3422248
[2]
Isaac Brodsky. 2019. H3: Uber's Hexagonal Hierarchical Spatial Index. Uber Engineering Blog (May 2019). https://eng.uber.com/h3
[3]
Cláudio G. S. Capanema, Fabr'icio A. Silva, Thais R. M. B. Silva, and Antonio A. F. Loureiro. 2021. POI-RGNN: Using Recurrent and Graph Neural Networks to Predict the Category of the Next Point of Interest (PE-WASUN '21). Association for Computing Machinery, New York, NY, USA, 49--56. https://doi.org/10.1145/3479240.3488532
[4]
Xiang Cheng, Luoyang Fang, Xuemin Hong, and Liuqing Yang. 2017. Exploiting mobile big data: Sources, features, and applications. IEEE Network, Vol. 31, 1 (2017), 72--79.
[5]
Prasanta Kr Chopdar, Justin Paul, Nikolaos Korfiatis, and Miltiadis D Lytras. 2022. Examining the role of consumer impulsiveness in multiple app usage behavior among mobile shoppers. Journal of Business Research, Vol. 140 (2022), 657--669.
[6]
Leonardo J. A. S. Figueiredo, Germano B. dos Santos, Raissa P. P. M. Souza, Fabr'icio A. Silva, and Thais R. M. Braga Silva. 2021. MoreData: A Geospatial Data Enrichment Framework (SIGSPATIAL '21). Association for Computing Machinery, New York, NY, USA, 419--422. https://doi.org/10.1145/3474717.3484210
[7]
Asad Masood Khattak, Rabia Batool, Fahad Ahmed Satti, Jamil Hussain, Wajahat Ali Khan, Adil Mehmood Khan, and Bashir Hayat. 2020. Tweets classification and sentiment analysis for personalized tweets recommendation. Complexity, Vol. 2020 (2020), 1--11.
[8]
Daniel K Maduku and Philile Thusi. 2023. Understanding consumers' mobile shopping continuance intention: New perspectives from South Africa. Journal of Retailing and Consumer Services, Vol. 70 (2023), 103185.
[9]
Wandella Maia, Fabrício Silva, and Thais Silva. 2020. Um Estudo Sobre a Relação entre Smartphones e Dados Demográficos. In Anais do IV Workshop de Computação Urbana (Rio de Janeiro). SBC, Porto Alegre, RS, Brasil, 302--315. https://doi.org/10.5753/courb.2020.12371
[10]
Raul Montoliu, Jan Blom, and Daniel Gatica-Perez. 2013. Discovering places of interest in everyday life from smartphone data. Multimedia tools and applications, Vol. 62, 1 (2013), 179--207.
[11]
Luca Pappalardo, Filippo Simini, Salvatore Rinzivillo, Dino Pedreschi, Fosca Giannotti, and Albert-László Barabási. 2015. Returners and explorers dichotomy in human mobility. Nature communications, Vol. 6, 1 (2015), 1--8.
[12]
Somya Ranjan Sahoo and Brij B Gupta. 2021. Multiple features based approach for automatic fake news detection on social networks using deep learning. Applied Soft Computing, Vol. 100 (2021), 106983.
[13]
Christos Sardianos, Iraklis Varlamis, and Grigoris Bouras. 2018. Extracting user habits from Google maps history logs. In 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). IEEE, 690--697.
[14]
Fabr'icio A. Silva, Augusto C. S. A. Domingues, and Thais R. M. Braga Silva. 2018. Discovering Mobile Application Usage Patterns from a Large-Scale Dataset. ACM Trans. Knowl. Discov. Data, Vol. 12, 5, Article 59 (June 2018), bibinfonumpages36 pages. https://doi.org/10.1145/3209669
[15]
Raissa PPM Souza, Leonardo JAS Figueiredo, Mateus P Silva, Fabr'icio A Silva, Thais RM B Silva, and Antônio AF Loureiro. 2022. Investigating the impact of demographic and device information in the recommendation of mobile applications. Journal of Internet Services and Applications, Vol. 12, 1 (2022), 21--32.
[16]
Zhen Tu, Hancheng Cao, Eemil Lagerspetz, Yali Fan, Huber Flores, Sasu Tarkoma, Petteri Nurmi, and Yong Li. 2021. Demographics of mobile app usage: Long-term analysis of mobile app usage. CCF Transactions on Pervasive Computing and Interaction, Vol. 3, 3 (2021), 235--252.
[17]
Iraklis Varlamis, Christos Sardianos, and Grigoris Bouras. 2020. Mining Habitual User Choices from Google Maps History Logs. In Putting Social Media and Networking Data in Practice for Education, Planning, Prediction and Recommendation. Springer, 151--175.
[18]
Donghan Yu, Yong Li, Fengli Xu, Pengyu Zhang, and Vassilis Kostakos. 2018. Smartphone app usage prediction using points of interest. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Vol. 1, 4 (2018), 1--21.
[19]
Bowen Zhao, Shaohua Tang, Ximeng Liu, and Xinglin Zhang. 2021. PACE: Privacy-Preserving and Quality-Aware Incentive Mechanism for Mobile Crowdsensing. IEEE Transactions on Mobile Computing, Vol. 20, 5 (2021), 1924--1939. https://doi.org/10.1109/TMC.2020.2973980
[20]
Hongyu Zhao, Jiazhi Xie, and Hongbin Wang. 2022. Co-learning Graph Convolution Network for Mobile User Profiling. Neural Processing Letters (2022), 1--18. io
Index Terms
- Extracting Mobile User Profile using Easy-to-obtain and Less Invasive Data
Recommendations
Anonymizing user location and profile information for privacy-aware mobile services
LBSN '10: Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Location Based Social NetworksDue to the growing use of mobile devices, location-based services have become popular. A location service often requires the user's exact location to provide appropriate services and this brings the risk of threats to privacy. In this paper, we propose ...
Empirical Assessment on User Acceptance of Mobile Applications Deployment
ICMB-GMR '10: Proceedings of the 2010 Ninth International Conference on Mobile Business / 2010 Ninth Global Mobility RoundtableNowadays, mobile phones are widespread. However, the development, distribution and installation of software on these end devices is a complex problem due to the very heterogeneous landscape of mobile end devices (especially with regard to software ...
A method for mobile user profile and reasoning
PRICAI'10: Proceedings of the 11th Pacific Rim international conference on Trends in artificial intelligenceBoth of mobile multimedia and mobile Internet are the important development directions of the mobile service. However, it would take great cost by using the high data transmission rate of wireless multimedia communication service. Under the premise of ...
Comments
Information & Contributors
Information
Published In
October 2023
129 pages
ISBN:9798400703706
DOI:10.1145/3616394
- General Chair:
- Monica Aguilar Igartua,
- Program Chairs:
- Luis J. de la Cruz Llopis,
- Thomas Begin
Copyright © 2023 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 the author(s) 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].
Sponsors
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 30 October 2023
Check for updates
Author Tags
Qualifiers
- Research-article
Funding Sources
- CNPq
- CAPES
- FAPEMIG and FAPESP
Conference
MSWiM '23
Sponsor:
MSWiM '23: Int'l ACM Conference on Modeling Analysis and Simulation of Wireless and Mobile Systems
October 30 - November 3, 2023
Quebec, Montreal, Canada
Acceptance Rates
Overall Acceptance Rate 70 of 240 submissions, 29%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 37Total Downloads
- Downloads (Last 12 months)11
- Downloads (Last 6 weeks)4
Reflects downloads up to 03 Mar 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in