research-article

Inferring Private Demographics of New Users in Recommender Systems

Authors:

Hongyuan ZhaAuthors Info & Claims

MSWiM '17: Proceedings of the 20th ACM International Conference on Modelling, Analysis and Simulation of Wireless and Mobile Systems

Pages 237 - 244

https://doi.org/10.1145/3127540.3127566

Published: 21 November 2017 Publication History

Abstract

With the growing number of wireless and mobile devices ingrained into our daily lives, more and more people are interacting with online services that adopt recommender systems to suggest movies, news and points of interest. The private demographics of users such as age and gender in online recommender systems are very useful for many applications such as personalized ads, social study and marketing. However, users do not always provide details in their online profiles due to privacy concern. Most existing approaches can infer user private attributes based on sufficient interaction history but could fail for new users with few ratings. In this paper, we present a novel preference elicitation method, with which a recommender system asks cold-start users to rate selected items adaptively and infer the demographics rapidly via a few interactions. Specifically, latent user profiles are learned across the tasks of demographic inference and rating prediction simultaneously, which enables knowledge transfer through the two related tasks and improves the prediction accuracy for both tasks. The proposed method can also facilitate the understanding of the tradeoff between user privacy and the utility of personalization. Experimental results on real-world datasets demonstrate the performance of the proposed method in terms of the accuracy of both demographics inference and rating prediction.

References

[1]

D. Agarwal and B.C. Chen. 2009. Regression-based latent factor models. In Proc. of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 19--28.

Digital Library

[2]

O. Averjanova, F. Ricci, and Q. N. Nguyen. 2008. Map-based interaction with a conversational mobile recommender system. In Proc. of the Second International Conference on Mobile Ubiquitous Computing, Systems, Services and Technologies. 212--218.

Digital Library

[3]

N. F. Awad and M.S. Krishnan. 2006. The personalization privacy paradox: an empirical evaluation of information transparency and the willingness to be profiled online for personalization. MIS quarterly 30, 1 (2006), 13--28.

[4]

L. Baltrunas, B. Ludwig, S. Peer, and F. Ricci. 2012. Context relevance assessment and exploitation in mobile recommender systems. Personal and Ubiquitous Computing 16, 5 (2012), 507--526.

Digital Library

[5]

S. Bhagat, U. Weinsberg, S. Ioannidis, and N. Taft. 2014. Recommending with an agenda: active learning of private attributes using matrix factorization. In Proc. of the 8th ACM Conference on Recommender systems. 65--72.

Digital Library

[6]

B. Bi, M. Shokouhi, M. Kosinski, and T. Graepel. 2013. Inferring the demographics of search users: social data meets search queries. In Proc. of the International Conference on World Wide Web. 131--140.

Digital Library

[7]

C. Boutilier, R.S. Zemel, and B. Marlin. 2003. Active collaborative filtering. In Proc. of the 19th Conference on Uncertainty in Artificial Intelligence. 98--106.

[8]

L. Breiman, J. Friedman, R. Olshen, and C. Stone. 1984. Classification and Regression Trees. Wadsworth and Brooks, Monterey, CA.

[9]

J.A. Calandrino, A. Kilzer, A. Narayanan, E.W. Felten, and V. Shmatikov. 2011. "You Might Also Like:" Privacy Risks of Collaborative Filtering. In Proc. of the IEEE Symposium on Security and Privacy. 231--246.

[10]

N. Golbandi, Y. Koren, and R. Lempel. 2011. Adaptive bootstrapping of recommender systems using decision trees. In Proc. of the 4th ACM International Conference on Web Search and Data Mining. 595--604.

Digital Library

[11]

A.S. Harpale and Y. Yang. 2008. Personalized active learning for collaborative filtering. In Proc. of the ACM SIGIR Conference. 91--98.

Digital Library

[12]

L. He, N.N. Liu, and Q. Yang. 2011. Active dual collaborative filtering with both item and attribute feedback. In Proc. of the 25th AAAI Conference on Artificial Intelligence.

[13]

R. Jin and L. Si. 2004. A Bayesian approach toward active learning for collaborative filtering. In Proc. of the 20th Conference on Uncertainty in Artificial Intelligence. 278--285.

[14]

Y. Koren. 2010. Factor in the neighbors: scalable and accurate collaborative filtering. ACM Transactions on Knowledge Discovery from Data 4, 1 (2010), 1--24.

Digital Library

[15]

M. Kosinski, D. Stillwell, and T. Graepel. 2013. Private traits and attributes are predictable from digital records of human behavior. Proceedings of the National Academy of Sciences 110, 15 (2013), 5802--5805.

[16]

B. Lakshminarayanan, G. Bouchard, and C. Archambeau. 2011. Robust Bayesian Matrix Factorisation. In Proc. of the International Conference on Artificial Intelligence and Statistics.

[17]

N. D. Lawrence and R. Urtasun. 2009. Non-linear matrix factorization with gaussian processes. In Proc. of the International Conference on Machine Learning.

Digital Library

[18]

A. Mislove, B. Viswanath, K.P. Gummadi, and P. Druschel. 2010. You are who you know: inferring user profiles in online social networks. In Proc. of the ACM International Conference on Web Search and Data Mining. 251--260.

Digital Library

[19]

A. Narayanan and V. Shmatikov. 2008. Robust de-anonymization of large sparse datasets. In Proc. of the IEEE Symposium on Security and Privacy. 111--125.

Digital Library

[20]

M. Pennacchiotti and A.M. Popescu. 2011. Democrats, republicans and starbucks afficionados: user classification in twitter. In Proc. of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 430--438.

Digital Library

[21]

K. P. Puttaswamy and B. Y. Zhao. 2010. Preserving privacy in location-based mobile social applications. In Proc. of the 11th Workshop on Mobile Computing Systems & Applications. 1--6.

Digital Library

[22]

D. Rao, D. Yarowsky, A. Shreevats, and M. Gupta. 2010. Classifying latent user attributes in twitter. In Proc. of the 2nd international workshop on Search and mining user-generated contents. 37--44.

Digital Library

[23]

A.M. Rashid, I. Albert, D. Cosley, S.K. Lam, S.M. McNee, J.A. Konstan, and J. Riedl. 2002. Getting to know you: learning new user preferences in recommender systems. In Proc. of the 7th International Conference on Intelligent User Interfaces. 127--134.

Digital Library

[24]

A.M. Rashid, G. Karypis, and J. Riedl. 2008. Learning preferences of new users in recommender systems: an information theoretic approach. In SIGKDD Workshop on Web Mining and Web Usage Analysis.

Digital Library

[25]

R. Salakhutdinov and A. Mnih. 2008. Bayesian probabilistic matrix factorization using Markov chain Monte Carlo. In Proc. of the International Conference on Machine Learning.

Digital Library

[26]

A. I. Schein, A. Popescul, L. H. Ungar, and D. M. Pennock. 2002. Methods and metrics for cold-start recommendations. In Proc. of the ACM SIGIR Conference. 253--260.

Digital Library

[27]

M. Strobbe, O. Van Laere, S. Dauwe, B. Dhoedt, F. De Turck, P. Demeester, C. van Nimwegen, and J. Vanattenhoven. 2010. Interest based selection of user generated content for rich communication services. Journal of Network and Computer Applications 33, 2 (2010), 84--97.

Digital Library

[28]

M. Sun, F. Li, J. Lee, K. Zhou, G. Lebanon, and H. Zha. 2013. Learning multiplequestion decision trees for cold-start recommendation. In Proc. of Conference on Web Search and Data Mining.

[29]

U. Weinsberg, S. Bhagat, S. Ioannidis, and N. Taft. 2012. Blurme: inferring and obfuscating user gender based on ratings. In Proc. of the 6th ACM conference on Recommender systems. 195--202.

Digital Library

[30]

B. Zenker and B. Ludwig. 2009. ROSE: assisting pedestrians to find preferred events and comfortable public transport connections. In Proc. of the 6th International Conference on Mobile Technology, Application & Systems. 16.

Digital Library

[31]

E. Zheleva and L. Getoor. 2009. To join or not to join: the illusion of privacy in social networks with mixed public and private user profiles. In Proc. of the International Conference on World Wide Web. 531--540.

Digital Library

[32]

Y. Zhong, N.J. Yuan,W. Zhong, F. Zhang, and X. Xie. 2015. You are where you go: inferring demographic attributes from location check-ins. In Proc. of the ACM International Conference on Web Search and Data Mining. 295--304.

Digital Library

[33]

K. Zhou, S. Yang, and H. Zha. 2011. Functional matrix factorizations for cold-start recommendation. In Proc. of the ACM SIGIR Conference. 315--324.

Digital Library

Cited By

Gomaa WKhamis M(2023)A perspective on human activity recognition from inertial motion dataNeural Computing and Applications10.1007/s00521-023-08863-935:28(20463-20568)Online publication date: 31-Jul-2023
https://doi.org/10.1007/s00521-023-08863-9
Slokom Mde Wolf PLarson M(2023)Exploring Privacy-Preserving Techniques on Synthetic Data as a Defense Against Model Inversion AttacksInformation Security10.1007/978-3-031-49187-0_1(3-23)Online publication date: 1-Dec-2023
https://doi.org/10.1007/978-3-031-49187-0_1
Sun MLi FZhang J(2018)A Multi-Modality Deep Network for Cold-Start RecommendationBig Data and Cognitive Computing10.3390/bdcc20100072:1(7)Online publication date: 5-Mar-2018
https://doi.org/10.3390/bdcc2010007
Show More Cited By

Index Terms

Inferring Private Demographics of New Users in Recommender Systems
1. Information systems
  1. Information retrieval
    1. Users and interactive retrieval
      1. Personalization
2. Security and privacy
  1. Software and application security
    1. Web application security

Recommendations

View-based recommender systems
RecSys '09: Proceedings of the third ACM conference on Recommender systems

Different recommender systems based on collaborative technology have been proposed that recommend new relevant products to users by exploring past user preference patterns. The most common approach generates recommendations based on user consumption ...
Cross-representation mediation of user models

Personalization is considered a powerful methodology for improving the effectiveness of information search and decision making. It has led to the dissemination of systems capable of suggesting relevant and personalized information (or items) to the users,...
Acquiring User Information Needs for Recommender Systems
WI-IAT '13: Proceedings of the 2013 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT) - Volume 03

Most recommender systems attempt to use collaborative filtering, content-based filtering or hybrid approach to recommend items to new users. Collaborative filtering recommends items to new users based on their similar neighbours, and content-based ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

MSWiM '17: Proceedings of the 20th ACM International Conference on Modelling, Analysis and Simulation of Wireless and Mobile Systems

November 2017

340 pages

ISBN:9781450351621

DOI:10.1145/3127540

General Chair:
Antonio Loureiro
Federal University of Minas Gerais, Brazil
,
Program Chairs:
Hsiao-Chun Wu
Louisiana State University, USA
,
Richard Yu
Carleton University, Canada

Copyright © 2017 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

SIGSIM: ACM Special Interest Group on Simulation and Modeling

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 21 November 2017

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

Louisiana Board of Regents

Conference

MSWiM '17

Sponsor:

SIGSIM

MSWiM '17: 20th ACM Int'l Conference on Modelling, Analysis and Simulation of Wireless and Mobile Systems

November 21 - 25, 2017

Florida, Miami, USA

Acceptance Rates

MSWiM '17 Paper Acceptance Rate 29 of 142 submissions, 20%;

Overall Acceptance Rate 398 of 1,577 submissions, 25%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

6
Total Citations
View Citations
140
Total Downloads

Downloads (Last 12 months)8
Downloads (Last 6 weeks)1

Reflects downloads up to 21 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Gomaa WKhamis M(2023)A perspective on human activity recognition from inertial motion dataNeural Computing and Applications10.1007/s00521-023-08863-935:28(20463-20568)Online publication date: 31-Jul-2023
https://doi.org/10.1007/s00521-023-08863-9
Slokom Mde Wolf PLarson M(2023)Exploring Privacy-Preserving Techniques on Synthetic Data as a Defense Against Model Inversion AttacksInformation Security10.1007/978-3-031-49187-0_1(3-23)Online publication date: 1-Dec-2023
https://doi.org/10.1007/978-3-031-49187-0_1
Sun MLi FZhang J(2018)A Multi-Modality Deep Network for Cold-Start RecommendationBig Data and Cognitive Computing10.3390/bdcc20100072:1(7)Online publication date: 5-Mar-2018
https://doi.org/10.3390/bdcc2010007
Slokom MPera SEkstrand MAmatriain XO'Donovan J(2018)Comparing recommender systems using synthetic dataProceedings of the 12th ACM Conference on Recommender Systems10.1145/3240323.3240325(548-552)Online publication date: 27-Sep-2018
https://dl.acm.org/doi/10.1145/3240323.3240325
Shang JSun MCollins-Thompson K(2018)Demographic Inference Via Knowledge Transfer in Cross-Domain Recommender Systems2018 IEEE International Conference on Data Mining (ICDM)10.1109/ICDM.2018.00162(1218-1223)Online publication date: Nov-2018
https://doi.org/10.1109/ICDM.2018.00162
Srifi MAit Hammou BAit Lahcen AMouline S(2018)A Concise Survey on Content RecommendationsBig Data, Cloud and Applications10.1007/978-3-319-96292-4_31(393-405)Online publication date: 14-Aug-2018
https://doi.org/10.1007/978-3-319-96292-4_31

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 Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents