research-article

From Fingerprint to Footprint: Cold-start Location Recommendation by Learning User Interest from App Data

Authors:

Depeng JinAuthors Info & Claims

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Volume 3, Issue 1

Article No.: 26, Pages 1 - 22

https://doi.org/10.1145/3314413

Published: 29 March 2019 Publication History

Abstract

With increasing diversity of user interest and preference, personalized location recommendation is essential and beneficial to our daily life. To achieve this, the most critical challenge is the cold-start recommendation problem, for we cannot learn preference from cold-start users without any historical records. In this paper, we demonstrate that it is feasible to make personalized location recommendation by learning user interest and location features from app usage data. By proposing a novel generative model to transfer user interests from app usage behavior to location preference, we achieve personalized location recommendation via learning the interest's correlation between locations and apps. Based on two real-world datasets, we evaluate our method's performance with a variety of scenarios and parameters. The results demonstrate that our method outperforms the state-of-the-art solutions in solving cold-start problem, i.e., when there are 60% cold-start users, we can still achieve a 77.0% hitrate in recommending the top five locations, which is at least 9.6% higher than the baselines. Our study is the first step forward for transferring user interests learning from online fingerprints to offline footprints, which paves the way for better personalized location recommendation services.

References

[1]

Apple. 2018. Licensed Application End User License Agreement. https://www.apple.com/legal/internet-services/itunes/dev/stdeula/.

[2]

Konrad Blaszkiewicz, Konrad Blaszkiewicz, Konrad Blaszkiewicz, and Alexander Markowetz. 2016. Differentiating smartphone users by app usage. In Proc. ACM Ubicomp. 519--523.

Digital Library

[3]

Sung-Hyuk Cha. 2007. Comprehensive survey on distance/similarity measures between probability density functions. City 1, 2 (2007), 1.

[4]

Karen Church, Denzil Ferreira, Nikola Banovic, and Kent Lyons. 2015. Understanding the challenges of mobile phone usage data. In Proceedings of the 17th International Conference on Human-Computer Interaction with Mobile Devices and Services. ACM, 504--514.

Digital Library

[5]

Nield David. 2017. All the Ways Your Smartphone and Its Apps Can Track You. https://gizmodo.com/all-the-ways-your-smartphone-and-its-apps-can-track-you-1821213704.

[6]

Hossein Falaki, Ratul Mahajan, Srikanth Kandula, Dimitrios Lymberopoulos, Ramesh Govindan, and Deborah Estrin. 2010. Diversity in smartphone usage. In Proc. ACM MobiSys. 179--194.

Digital Library

[7]

Denzil Ferreira, Jorge Goncalves, Vassilis Kostakos, Louise Barkhuus, and Anind K Dey. 2014. Contextual experience sampling of mobile application micro-usage. In Proceedings of the 16th international conference on Human-computer interaction with mobile devices and services. ACM, 91--100.

Digital Library

[8]

Chen Gao, Xiangnan He, Dahua Gan, Xiangning Chen, Fuli Feng, Yong Li, and Tat-Seng Chua. 2019. Neural Multi-Task Recommendation from Multi-Behavior Data. In ICDE.

[9]

Huiji Gao, Jiliang Tang, and Huan Liu. 2015. Addressing the cold-start problem in location recommendation using geo-social correlations. Data Mining and Knowledge Discovery 29, 2 (2015), 299--323.

Digital Library

[10]

Fosca Giannotti, Mirco Nanni, Fabio Pinelli, and Dino Pedreschi. 2007. Trajectory Pattern Mining. In Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '07). 330--339.

Digital Library

[11]

Google. 2018. Google's Privacy Policies. https://policies.google.com/privacy#infosharing

[12]

Ke Huang, Chunhui Zhang, Xiaoxiao Ma, and Guanling Chen. 2012. Predicting mobile application usage using contextual information. In Proc. ACM UbiComp. 1059--1065.

Digital Library

[13]

Simon L Jones, Denzil Ferreira, Simo Hosio, Jorge Goncalves, and Vassilis Kostakos. 2015. Revisitation analysis of smartphone app use. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM, 1197--1208.

Digital Library

[14]

Kaggle. 2018. TalkingData Mobile User Demographics. (April 2018). Retrieved Oct 11, 2018 from https://www.kaggle.com/c/talkingdata-mobile-user-demographics

[15]

Alexandros Karatzoglou, Linas Baltrunas, Karen Church, and Matthias Böhmer. 2012. Climbing the app wall: enabling mobile app discovery through context-aware recommendations. In Proceedings of the 21st ACM international conference on Information and knowledge management. ACM, 2527--2530.

Digital Library

[16]

Conger Kate. 2017. Researchers: Uber's iOS App Had Secret Permissions That Allowed It to Copy Your Phone Screen. https://gizmodo.com/researchers-uber-s-ios-app-had-secret-permissions-that-1819177235.

[17]

Yehuda Koren, Robert Bell, and Chris Volinsky. 2009. Matrix factorization techniques for recommender systems. Computer 42, 8 (2009).

Digital Library

[18]

Vassilis Kostakos, Denzil Ferreira, Jorge Goncalves, and Simo Hosio. 2016. Modelling smartphone usage: a markov state transition model. In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM, 486--497.

Digital Library

[19]

Yuanchun Li, Yao Guo, and Xiangqun Chen. 2016. PERUIM:understanding mobile application privacy with permission-UI mapping. In Proc. ACM Ubicomp. 682--693.

Digital Library

[20]

Tzu-Heng Lin, Chen Gao, and Yong Li. 2018. Recommender Systems with Characterized Social Regularization. In CIKM. 1767--1770.

Digital Library

[21]

Qi Liu, Haiping Ma, Enhong Chen, and Hui Xiong. 2013. A survey of context-aware mobile recommendations. International Journal of Information Technology and Decision Making 12, 01 (2013), 139--172.

[22]

Qiang Liu, Shu Wu, Liang Wang, and Tieniu Tan. 2016. Predicting the Next Location: A Recurrent Model with Spatial and Temporal Contexts. In AAAI. 194--200.

Digital Library

[23]

Xuelian Long and James Joshi. 2013. A HITS-based POI recommendation algorithm for Location-Based Social Networks. In Ieee/acm International Conference on Advances in Social Networks Analysis and Mining. 642--647.

Digital Library

[24]

Hao Ma, Haixuan Yang, Michael R Lyu, and Irwin King. 2008. Sorec: social recommendation using probabilistic matrix factorization. In Proceedings of the 17th ACM conference on Information and knowledge management. ACM, 931--940.

Digital Library

[25]

Hao Ma, Dengyong Zhou, Chao Liu, Michael R. Lyu, and Irwin King. 2011. Recommender systems with social regularization. (2011), 287--296.

Digital Library

[26]

Eric Malmi and Ingmar Weber. 2016. You Are What Apps You Use: Demographic Prediction Based on User's Apps. (2016).

[27]

Anna Monreale, Fabio Pinelli, Roberto Trasarti, and Fosca Giannotti. 2009. WhereNext: A Location Predictor on Trajectory Pattern Mining. In Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '09). 637--646.

Digital Library

[28]

Hieu V Nguyen and Li Bai. 2010. Cosine similarity metric learning for face verification. In Asian conference on computer vision. Springer, 709--720.

Digital Library

[29]

Department of Computer Science and University of Helsinki. 2017. Context Recognition by User Situation Data Analysis (Context). (Oct. 2017). Retrieved Oct 11, 2018 from https://www.cs.helsinki.fi/group/context/#data

[30]

Google Play. 2017. Mafengwo in the Google Play. https://play.google.com/store/apps/developer?id=mafengwo.mobile.

[31]

Zhen Qin, Yilei Wang, Yong Xia, and Hongrong Cheng. 2014. Demographic information prediction based on smartphone application usage. In International Conference on Smart Computing. 183--190.

[32]

Badrul Sarwar, George Karypis, Joseph Konstan, and John Riedl. 2000. Application of dimensionality reduction in recommender system-a case study. Technical Report. Minnesota Univ Minneapolis Dept of Computer Science.

[33]

Martin Saveski and Amin Mantrach. 2014. Item cold-start recommendations: learning local collective embeddings. In RecSys.

Digital Library

[34]

Suvash Sedhain, Darius Braziunas, Darius Braziunas, Jordan Christensen, and Jordan Christensen. 2014. Social collaborative filtering for cold-start recommendations. In ACM Conference on Recommender Systems. 345--348.

Digital Library

[35]

Choonsung Shin, Jin-Hyuk Hong, and Anind K Dey. 2012. Understanding and prediction of mobile application usage for smart phones. In Proceedings of the 2012 ACM Conference on Ubiquitous Computing. ACM, 173--182.

Digital Library

[36]

Ajit P Singh and Geoffrey J Gordon. 2008. Relational learning via collective matrix factorization. In Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 650--658.

Digital Library

[37]

Zhen Tu, Runtong Li, Yong Li, Gang Wang, Di Wu, Pan Hui, Li Su, and Depeng Jin. 2018. Your apps give you away: distinguishing mobile users by their app usage fingerprints. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2, 3 (2018), 138.

Digital Library

[38]

Min Xie, Hongzhi Yin, Hao Wang, Fanjiang Xu, Weitong Chen, and Sen Wang. 2016. Learning graph-based poi embedding for location-based recommendation. In Proceedings of the 25th ACM International on Conference on Information and Knowledge Management. ACM, 15--24.

Digital Library

[39]

Fengli Xu, Pengyu Zhang, and Yong Li. 2016. Context-aware real-time population estimation for metropolis. In ACM International Joint Conference on Pervasive and Ubiquitous Computing. 1064--1075.

Digital Library

[40]

Ye Xu, Mu Lin, Hong Lu, Giuseppe Cardone, Nicholas Lane, Zhenyu Chen, Andrew Campbell, and Tanzeem Choudhury. 2013. Preference, context and communities:a multi-faceted approach to predicting smartphone app usage patterns. In Proc. ACM ISWC. 69--76.

Digital Library

[41]

Josh Jia-Ching Ying, Wang-Chien Lee, Tz-Chiao Weng, and Vincent S. Tseng. 2011. Semantic Trajectory Mining for Location Prediction. In Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (GIS '11). 34--43.

Digital Library

[42]

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 1, 4 (2018), 174.

Digital Library

[43]

C. Zhang, K. Zhang, Q. Yuan, L. Zhang, T Hanratty, and J. Han. 2016. GMove: Group-Level Mobility Modeling Using Geo-Tagged Social Media. In ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 1305--1314.

Digital Library

[44]

Mi Zhang, Jie Tang, Xuchen Zhang, and Xiangyang Xue. 2014. Addressing cold start in recommender systems: a semi-supervised co-training algorithm. In SIGIR.

Digital Library

[45]

Shuo Zhang, Khaled Alanezi, Mike Gartrell, Richard Han, Qin Lv, and Shivakant Mishra. 2017. Understanding Group Event Scheduling via the OutWithFriendz Mobile Application. In Proc. ACM Ubicomp.

[46]

Sha Zhao, Julian Ramos, Jianrong Tao, Ziwen Jiang, Shijian Li, Zhaohui Wu, Gang Pan, and Anind K. Dey. 2016. Discovering different kinds of smartphone users through their application usage behaviors. In Proc. ACM UbiComp. 498--509.

Digital Library

[47]

Shenglin Zhao, Tong Zhao, Haiqin Yang, Michael R Lyu, and Irwin King. 2016. STELLAR: Spatial-Temporal Latent Ranking for Successive Point-of-Interest Recommendation. In AAAI. 315--322.

Digital Library

[48]

Wayne Xin Zhao, Sui Li, Yulan He, Edward Y. Chang, Ji-Rong Wen, and Xiaoming Li. 2016. Connecting Social Media to E-Commerce: Cold-Start Product Recommendation Using Microblogging Information. IEEE Transactions on Knowledge and Data Engineering 28 (2016), 1147--1159.

Digital Library

[49]

Xiaoxing Zhao, Yuanyuan Qiao, Zhongwei Si, Jie Yang, and Anders Lindgren. 2016. Prediction of user app usage behavior from geo-spatial data. In Proc. ACM GeoRich. 1--6.

Digital Library

[50]

Vincent Wenchen Zheng, Bin Cao, Yu Zheng, Xing Xie, and Qiang Yang. 2010. Collaborative Filtering Meets Mobile Recommendation: A User-Centered Approach. In AAAI, Vol. 10. 236--241.

[51]

Hengshu Zhu, Enhong Chen, Kuifei Yu, Huanhuan Cao, Hui Xiong, and Jilei Tian. 2012. Mining personal context-aware preferences for mobile users. In Data Mining (ICDM), 2012 IEEE 12th International Conference on. IEEE, 1212--1217.

Digital Library

Cited By

Xu SGuan DSerra ESpezzano F(2024)CrossPred: A Cross-City Mobility Prediction Framework for Long-Distance Travelers via POI Feature MatchingProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679893(4148-4152)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679893
Ouyang YGuo BWang QLiang YYu Z(2023)Learning Dynamic App Usage Graph for Next Mobile App RecommendationIEEE Transactions on Mobile Computing10.1109/TMC.2022.316111422:8(4742-4753)Online publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1109/TMC.2022.3161114
Gupta VBedathur S(2023)Modeling Spatial Trajectories Using Coarse-Grained Smartphone LogsIEEE Transactions on Big Data10.1109/TBDATA.2022.32047599:2(608-620)Online publication date: 1-Apr-2023
https://doi.org/10.1109/TBDATA.2022.3204759
Show More Cited By

Index Terms

From Fingerprint to Footprint: Cold-start Location Recommendation by Learning User Interest from App Data
1. Information systems
  1. Information systems applications
    1. Data mining
      1. Collaborative filtering
    2. Decision support systems
  2. World Wide Web
    1. Web searching and information discovery
      1. Personalization

Recommendations

Collaborative filtering based on an iterative prediction method to alleviate the sparsity problem
iiWAS '09: Proceedings of the 11th International Conference on Information Integration and Web-based Applications & Services

Collaborative filtering (CF) is one of the most popular recommender system technologies. It tries to identify users that have relevant interests and preferences by calculating similarities among user profiles. The idea behind this method is that, it may ...
A Genre-Based Item-Item Collaborative Filtering: Facing the Cold-Start Problem
ICSCA '19: Proceedings of the 2019 8th International Conference on Software and Computer Applications

Recommender System is a technique which is used to recommend an item or product to a user based on the user's preference'. Collaborative filtering is an approach that is vastly used in recommender systems. Item-item-based collaborative filtering is a ...
Personalized location recommendation using mobile phone usage information
Abstract
Location recommendation has become a hot research area in recent years. The cold-start problem is still a great challenge in personalized location recommendation, which makes it difficult to infer a new user’s preferences, because a new user ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

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 3, Issue 1

March 2019

786 pages

EISSN:2474-9567

DOI:10.1145/3323054

Issue’s Table of Contents

Copyright © 2019 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].

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 29 March 2019

Accepted: 01 January 2019

Revised: 01 November 2018

Received: 01 August 2018

Published in IMWUT Volume 3, Issue 1

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed

Funding Sources

research fund of Tsinghua University - Tencent Joint Laboratory for Internet Innovation Technology
National Key Research and Development Program of China
National Nature Science Foundation of China
Beijing National Research Center for Information Science and Technology

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

18
Total Citations
View Citations
485
Total Downloads

Downloads (Last 12 months)29
Downloads (Last 6 weeks)4

Reflects downloads up to 26 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Xu SGuan DSerra ESpezzano F(2024)CrossPred: A Cross-City Mobility Prediction Framework for Long-Distance Travelers via POI Feature MatchingProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679893(4148-4152)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679893
Ouyang YGuo BWang QLiang YYu Z(2023)Learning Dynamic App Usage Graph for Next Mobile App RecommendationIEEE Transactions on Mobile Computing10.1109/TMC.2022.316111422:8(4742-4753)Online publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1109/TMC.2022.3161114
Gupta VBedathur S(2023)Modeling Spatial Trajectories Using Coarse-Grained Smartphone LogsIEEE Transactions on Big Data10.1109/TBDATA.2022.32047599:2(608-620)Online publication date: 1-Apr-2023
https://doi.org/10.1109/TBDATA.2022.3204759
Chen JLin KChen XWang XHsu T(2023)Location Recommendations Based on Multi-view Learning and Attention-Enhanced Graph NetworksBig Data and Social Computing10.1007/978-981-99-3925-1_5(83-95)Online publication date: 30-Jun-2023
https://doi.org/10.1007/978-981-99-3925-1_5
Chen XChen JLian XMai W(2022)Resolving Data Sparsity via Aggregating Graph-Based User–App–Location Association for Location RecommendationsApplied Sciences10.3390/app1214688212:14(6882)Online publication date: 7-Jul-2022
https://doi.org/10.3390/app12146882
Pan LYu CLi JHuang TBi XShi Y(2022)Automatically Generating and Improving Voice Command Interface from Operation Sequences on SmartphonesProceedings of the 2022 CHI Conference on Human Factors in Computing Systems10.1145/3491102.3517459(1-21)Online publication date: 29-Apr-2022
https://dl.acm.org/doi/10.1145/3491102.3517459
Lu XLiu JGan SLi TXiao YLiu Y(2022)Recommendation Model Based on Dynamic Interest Group Identification and Data CompensationIEEE Transactions on Network and Service Management10.1109/TNSM.2021.311270219:1(89-99)Online publication date: Mar-2022
https://doi.org/10.1109/TNSM.2021.3112702
Shaygan MMeese CLi WZhao XNejad M(2022)Traffic prediction using artificial intelligence: Review of recent advances and emerging opportunitiesTransportation Research Part C: Emerging Technologies10.1016/j.trc.2022.103921145(103921)Online publication date: Dec-2022
https://doi.org/10.1016/j.trc.2022.103921
Abdullah NRasheed RNasir MRahman M(2021)Eliciting Auxiliary Information for Cold Start User Recommendation: A SurveyApplied Sciences10.3390/app1120960811:20(9608)Online publication date: 15-Oct-2021
https://doi.org/10.3390/app11209608
Choi SLee DPark C(2020)On the Smaller Number of Inputs for Determining User Preferences in Recommender SystemsMathematics10.3390/math81221388:12(2138)Online publication date: 1-Dec-2020
https://doi.org/10.3390/math8122138
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