More Web Proxy on the site http://driver.im/

research-article

Making Decisions about Privacy: Information Disclosure in Context-Aware Recommender Systems

Authors:

Bart P. Knijnenburg,

Alfred KobsaAuthors Info & Claims

ACM Transactions on Interactive Intelligent Systems (TiiS), Volume 3, Issue 3

Article No.: 20, Pages 1 - 23

https://doi.org/10.1145/2499670

Published: 01 October 2013 Publication History

Abstract

Recommender systems increasingly use contextual and demographical data as a basis for recommendations. Users, however, often feel uncomfortable providing such information. In a privacy-minded design of recommenders, users are free to decide for themselves what data they want to disclose about themselves. But this decision is often complex and burdensome, because the consequences of disclosing personal information are uncertain or even unknown. Although a number of researchers have tried to analyze and facilitate such information disclosure decisions, their research results are fragmented, and they often do not hold up well across studies. This article describes a unified approach to privacy decision research that describes the cognitive processes involved in users’ “privacy calculus” in terms of system-related perceptions and experiences that act as mediating factors to information disclosure. The approach is applied in an online experiment with 493 participants using a mock-up of a context-aware recommender system. Analyzing the results with a structural linear model, we demonstrate that personal privacy concerns and disclosure justification messages affect the perception of and experience with a system, which in turn drive information disclosure decisions. Overall, disclosure justification messages do not increase disclosure. Although they are perceived to be valuable, they decrease users’ trust and satisfaction. Another result is that manipulating the order of the requests increases the disclosure of items requested early but decreases the disclosure of items requested later.

Supplementary Material

PDF File (a20-knijnenburg_appendix.pdf)

The proof is given in an electronic appendix, available online in the ACM Digital Library.

Download
162.98 KB

References

[1]

Ackerman, M. S., Cranor, L. F., and Reagle, J. 1999. Privacy in e-commerce: Examining user scenarios and privacy preferences. In Proceedings of the 1st ACM Conference on Electronic Commerce. ACM Press, 1--8.

Digital Library

[2]

Acquisti, A. and Grossklags, J. 2005. Privacy and rationality in individual decision making. IEEE Secur. Privacy 3, 1, 26--33.

Digital Library

[3]

Acquisti, A. and Grossklags, J. 2008. What can behavioral economics teach us about privacy? In Digital Privacy: Theory, Technologies, and Practices, A. Acquisti, S. de Capitani di Vimercati, S. Gritzalis, and C. Lambrinoudakis Eds., Taylor & Francis, 363--377.

[4]

Acquisti, A., John, L., and Loewenstein, G. 2009. What is privacy worth? In Proceedings of the 21st Workshop on Information Systems and Economics.

[5]

Acquisti, A., John, L. K., and Loewenstein, G. 2011. The impact of relative standards on the propensity to disclose. J. Marketing Res. 1--15.

[6]

Adomavicius, G. and Tuzhilin, A. 2011. Context-aware recommender systems. In Recommender Systems Handbook, F. Ricci, L. Rokach, B. Shapira and P. B. Kantor Eds., Springer, 217--253.

[7]

Archer, R. L. and Berg, J. H. 1978. Disclosure reciprocity and its limits: A reactance analysis. J. Exp. Social Psych. 14, 6, 527--540.

[8]

Bélanger, F. and Crossler, R. E. 2011. Privacy in the digital age: A review of information privacy research in information systems. MIS Quart. 35, 4, 1017--1045.

Digital Library

[9]

Bentler, P. M. and Bonett, D. G. 1980. Significance tests and goodness of fit in the analysis of covariance structures. Psych. Bull. 88, 3, 588--606.

[10]

Besmer, A., Watson, J., and Lipford, H. R. 2010. The impact of social navigation on privacy policy configuration. In Proceedings of the 6th Symposium on Usable Privacy and Security.

Digital Library

[11]

Böhmer, M., Bauer, G., and Krüger, A. 2010. Exploring the design space of context-aware recommender systems that suggest mobile applications. In Proceedings of the 2nd Workshop on Context-Aware Recommender Systems.

[12]

Buchanan, T., Paine, C., Joinson, A. N., and Reips, U.-D. 2007. Development of measures of online privacy concern and protection for use on the internet. J. Amer. Soc. Inf. Sci. Tech. 58, 2, 157--165.

Digital Library

[13]

Canny, J. 2002a. Collaborative filtering with privacy. In Proceedings of the IEEE Symposium on Security and Privacy. IEEE Press, 45--57.

Digital Library

[14]

Canny, J. 2002b. Collaborative filtering with privacy via factor analysis. In Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM Press, 238--245.

Digital Library

[15]

Chellappa, R. K. and Sin, R. G. 2005. Personalization versus privacy: An empirical examination of the online consumer’s dilemma. Inf. Tech. Manage. 6, 2, 181--202.

Digital Library

[16]

Consolvo, S., Smith, I., Matthews, T., LaMarca, A., Tabert, J., and Powledge, P. 2005. Location disclosure to social relations: Why, when, & what people want to share. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. 81--90.

Digital Library

[17]

Culnan, M. J. 1993. “How did they get my name?”: An exploratory investigation of consumer attitudes toward secondary information use. MIS Quart. 17, 3, 341--363.

Digital Library

[18]

Davidsson, C. and Moritz, S. 2011. Utilizing implicit feedback and context to recommend mobile applications from first use. In Proceedings of the Workshop on Context-Awareness in Retrieval and Recommendation. ACM Press, 19--22.

Digital Library

[19]

Egelman, S., Tsai, J., Cranor, L. F., and Acquisti, A. 2009. Timing is everything?: The effects of timing and placement of online privacy indicators. In Proceedings of the 27th International Conference on Human Factors in computing Systems. ACM Press, 319--328.

Digital Library

[20]

Fitzsimons, G. J. and Lehmann, D. R. 2004. Reactance to recommendations: When unsolicited advice yields contrary responses. Marketing Sci. 23, 1, 82--94.

Digital Library

[21]

Gigerenzer, G. and Goldstein, D. G. 1996. Reasoning the fast and frugal way: Models of bounded rationality. Psych. Rev. 103, 4, 650--669.

[22]

Girardello, A. and Michahelles, F. 2010. AppAware: Which mobile applications are hot? In Proceedings of the 12th International Conference on Human Computer Interaction with Mobile Devices and Services. ACM Press, 431--434.

Digital Library

[23]

Harris, L. and Associates. 1990. The Equifax Report on Consumers in the Information Age. Equifax Inc., Atlanta, GA.

[24]

Harris, L. and Associates. 1991. Harris-Equifax Consumer Privacy Survey 1991. Equifax Inc., Atlanta, GA.

[25]

Harris, L., Associates, and Westin, A. F. 1998. Personalized marketing and privacy on the net: What consumers want. Privacy Amer. Business Newsl.

[26]

Harris, L., Westin, A. F., and Associates. 2003. Consumer Privacy Attitudes: A Major Shift Since 2000 and Why. Harris Interactive, Inc.

[27]

Hassenzahl, M. 2005. The thing and I: Understanding the relationship between user and product. In Funology, Human-Computer Interaction Series, M. A. Blythe, K. Overbeeke, A. F. Monk and P. C. Wright Eds., Springer, 31--42.

[28]

Hu, L. and Bentler, P. M. 1999. Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Struct. Equation Model.: A Multidisciplinary J. 6, 1, 1--55.

[29]

Hui, K.-L., Tan, B. C. Y., and Goh, C.-Y. 2006. Online information disclosure: Motivators and measurements. ACM Trans. Internet Technol. 6, 4, 415--441.

Digital Library

[30]

Hui, K.-L., Teo, H. H., and Lee, S.-Y. T. 2007. The value of privacy assurance: An exploratory field experiment. MIS Quart. 31, 1, 19--33.

Digital Library

[31]

Iachello, G. and Hong, J. 2007. End-user privacy in human-computer interaction. Found. Trends in Human-Computer Interact. 1, 1, 1--137.

Digital Library

[32]

Jarvenpaa, S. and Tractinsky, N. 1999. Consumer trust in an internet store: A cross-cultural validation. J. Comput. Mediated Commun. 5, 2.

[33]

John, L. K., Acquisti, A., and Loewenstein, G. 2011. Strangers on a plane: Context-dependent willingness to divulge sensitive information. J. Consumer Res. 37, 5, 858--873.

[34]

Kahneman, D. and Tversky, A. 1979. Prospect theory: An analysis of decision under risk. Econometrica 47, 2, 263--292.

[35]

Kahneman, D., Slovic, P., and Tversky, A. 1982. Judgment under Uncertainty: Heuristics and Biases. Cambridge University Press, Cambridge, UK.

[36]

Kahneman, D., Knetsch, J. L., and Thaler, R. H. 1990. Experimental tests of the endowment effect and the Coase theorem. J. Political Economy 98, 6, 1325--1348.

[37]

Keith, M. J., Babb, J. S., Lowry, P. B., Furner, C. P., and Abdullat, A. 2011. The Roles of privacy assurance, network effects, and information cascades in the adoption of and willingness to pay for location-based services with mobile applications. In Proceedings of the Dewald Roode Information Security Workshop. http://ifip.byu.edu/2011/Keith&percnt;20et&percnt;20al.&percnt;20Mobile&percnt;20Applications.pdf.

[38]

Kittur, A., Chi, E. H., and Suh, B. 2008. Crowdsourcing user studies with Mechanical Turk. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM Press, 453--456.

Digital Library

[39]

Knijnenburg, B. P. and Kobsa, A. 2013. Helping users with information disclosure decisions: Potential for adaptation. In Proceedings of the ACM International Conference on Intelligent User Interfaces. ACM Press, 407--416.

Digital Library

[40]

Knijnenburg, B. P., Willemsen, M. C., Gantner, Z., Soncu, H., and Newell, C. 2012. Explaining the user experience of recommender systems. User Model. User-Adapted Interact. 22, 4--5, 441--504.

Digital Library

[41]

Kobsa, A. 2007. Privacy-enhanced web personalization. In The Adaptive Web: Methods and Strategies of Web Personalization, P. Brusilovsky, A. Kobsa, and W. Nejdl Eds., Springer, 628--670.

Digital Library

[42]

Kobsa, A. and Teltzrow, M. 2005. Contextualized communication of privacy practices and personalization benefits: Impacts on users’ data sharing and purchase behavior. In Revised Selected Papers of the 4th International Workshop on Privacy Enhancing Technologies, D. Martin and A. Serjantov Eds., Springer, 329--343.

Digital Library

[43]

Kolter, J. and Pernul, G. 2009. Generating user-understandable privacy preferences. In Proceedings of the International Conference on Availability, Reliability and Security. IEEE, 299--306.

[44]

Laufer, R. S. and Wolfe, M. 1977. Privacy as a concept and a social issue: A multidimensional developmental theory. J. Social Issues 33, 3, 22--42.

[45]

Laufer, R. S., Proshansky, H. M., and Wolfe, M. 1973. Some analytic dimensions of privacy. In Proceedings of the Lund Conference on Architectural Psychology, R. Küller Ed., Dowden, Hutchinson & Ross.

[46]

Lederer, S., Mankoff, J., and Dey, A. K. 2003. Who wants to know what when? Privacy preference determinants in ubiquitous computing. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM Press, 724--725.

Digital Library

[47]

Lee, H. J. and Park, S. J. 2007. MONERS: A news recommender for the mobile web. Expert Syst. Appl. 32, 1, 143--150.

[48]

Lee, J. and Lee, J. 2007. Context awareness by case-based reasoning in a music recommendation system. In Proceedings of the 4th International Symposium on Ubiquitous Computing Systems. H. Ichikawa, W.-D. Cho, I. Satoh, and H. Youn Eds., Springer, 45--58.

Digital Library

[49]

Li, H., Sarathy, R., and Xu, H. 2010. Understanding situational online information disclosure as a privacy calculus. J. Comput. Inf. Syst. 51, 1, 62--71.

[50]

Li, H., Sarathy, R., and Xu, H. 2011. The role of affect and cognition on online consumers’ decision to disclose personal information to unfamiliar online vendors. Decision Support Syst. 51, 3, 434--445.

Digital Library

[51]

Li, X. and Santhanam, R. 2008. Will it be disclosure or fabrication of personal information? An examination of persuasion strategies on prospective employees. Int. J. Inf. Secur. Privacy 2, 4, 91--109.

[52]

Mabley, K. 2000. Privacy vs. Personalization: Part III. Cyber Dialogue, Inc.

[53]

Malhotra, N. K., Kim, S. S., and Agarwal, J. 2004. Internet users’ information privacy concerns (IUIPC): The construct, the scale, and a causal model. Inf. Syst. Res. 15, 4, 336--355.

Digital Library

[54]

Metzger, M. J. 2004. Privacy, trust, and disclosure: Exploring barriers to electronic commerce. J. Comput. Mediated Commun. 9, 4.

[55]

Metzger, M. J. 2006. Effects of site, vendor, and consumer characteristics on web site trust and disclosure. Commun. Res. 33, 3, 155--179.

[56]

Nissenbaum, H. F. 2010. Privacy in Context: Technology, Policy, and the Integrity of Social Life. Stanford Law Books, Stanford, CA.

Digital Library

[57]

Nowak, G. J. and Phelps, J. 1995. Direct marketing and the use of individual-level consumer information: Determining how and when “privacy” matters. J. Direct Marketing 9, 3, 46--60.

[58]

Oh, J.-M. and Moon, N. 2012. User-selectable interactive recommendation system in mobile environment. Multimedia Tools Appl. 57, 2, 295--313.

Digital Library

[59]

Patil, S., Page, X., and Kobsa, A. 2011. With a little help from my friends: can social navigation inform interpersonal privacy preferences? In Proceedings of the ACM Conference on Computer Supported Cooperative Work. ACM Press, 391--394.

Digital Library

[60]

Polat, H. and Du, W. 2005a. Privacy-preserving collaborative filtering. Int. J. Electron. Commerce 9, 4, 9--35.

[61]

Polat, H. and Du, W. 2005b. SVD-based collaborative filtering with privacy. In Proceedings of the ACM Symposium on Applied Computing. ACM Press, 791--795.

Digital Library

[62]

Pu, P., Chen, L., and Hu, R. 2011. A user-centric evaluation framework for recommender systems. In Proceedings of the 5th ACM Conference on Recommender Systems. ACM Press, 157--164.

Digital Library

[63]

Ricci, F. 2011. Mobile recommender systems. Inf. Tech. Tourism 12, 3, 205--231.

[64]

Rifon, N. J., LaRose, R., and Choi, S. M. 2005. Your privacy is sealed: Effects of web privacy seals on trust and personal disclosures. J. Consumer Affairs 39, 2, 339--360.

[65]

Smith, H. J., Dinev, T., and Xu, H. 2011. Information privacy research: An interdisciplinary review. MIS Quart. 35, 4, 989--1015.

Digital Library

[66]

Smith, H. J., Milberg, S. J., and Burke, S. J. 1992. Concern for privacy instrument. Working document, School of Business, Georgetown University, Washington, DC.

[67]

Smith, H. J., Milberg, S. J., and Burke, S. J. 1996. Information privacy: Measuring individuals’ concerns about organizational practices. MIS Quart. 20, 2, 167--196.

Digital Library

[68]

Solove, D. J. 2006. A taxonomy of privacy. U. Penn. Law Rev. 154, 3, 477--564.

[69]

Spiekermann, S., Grossklags, J., and Berendt, B. 2001. E-privacy in 2nd generation e-commerce: Privacy Preferences versus actual Behavior. In Proceedings of the 3rd ACM Conference on Electronic Commerce. ACM Press, 38--47.

Digital Library

[70]

Stewart, K. A. and Segars, A. H. 2002. An empirical examination of the concern for information privacy instrument. Inf. Syst. Res. 13, 1, 36--49.

Digital Library

[71]

Taylor, D., Davis, D., and Jillapalli, R. 2009. Privacy concern and online personalization: The moderating effects of information control and compensation. Electronic Commerce Res. 9, 3, 203--223.

Digital Library

[72]

Teo, H. H., Wan, W., and Li, L. 2004. Volunteering personal information on the Internet: Effects of reputation, privacy initiatives, and reward on online consumer behavior. In Proceedings of the 37th Hawaii International Conference on System Sciences. IEEE Press, 181--190.

Digital Library

[73]

Thaler, R. 1980. Toward a positive theory of consumer choice. J. Econ. Behav. Org. 1, 1, 39--60.

[74]

Tsai, J. Y., Egelman, S., Cranor, L., and Acquisti, A. 2010. The effect of online privacy information on purchasing behavior: An experimental study. Inf. Syst. Res.

Digital Library

[75]

Turow, J., King, J., Hoofnagle, C. J., Bleakley, A., and Hennessy, M. 2009. Americans reject tailored advertising and three activities that enable it. Manuscript (posted on the Social Science Research Network http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1478214).

[76]

Van de Garde-Perik, E., Markopoulos, P., de Ruyter, B., Eggen, B., and Ijsselsteijn, W. 2008. Investigating privacy attitudes and behavior in relation to personalization. Social Sci. Comput. Rev. 26, 1, 20--43.

Digital Library

[77]

Wang, W. and Benbasat, I. 2007. Recommendation agents for electronic commerce: Effects of explanation facilities on trusting beliefs. J. Manage. Inf. Syst. 23, 4, 217--246.

Digital Library

[78]

Wenning, R. and Schunter, M. 2006. The platform for privacy preferences 1.1 (P3P1.1) Specification. W3C Working Group Note.

[79]

Westin, A. F., Harris, L., et al. 1981. The Dimensions of Privacy: A National Opinion Research Survey of Attitudes toward Privacy. Garland Publishing, New York.

[80]

Xu, H., Luo, X. (Robert), Carroll, J. M., and Rosson, M. B. 2011. The personalization privacy paradox: An exploratory study of decision making process for location-aware marketing. Decision Support Syst. 51, 42--52.

Digital Library

[81]

Xu, H., Teo, H.-H., Tan, B., and Agarwal, R. 2009. The role of push-pull technology in privacy calculus: The case of location-based services. J. Manage. Inf. Syst. 26, 3, 135--174.

Digital Library

[82]

Zhang, N., Wang, C., and Xu, Y. 2011. Privacy in online social networks. In Proceedings of the International Conference on Information Systems. http://aisel.aisnet.org/icis2011/proceedings/ISsecurity/3.

[83]

Zheng, V. W., Zheng, Y., Xie, X., and Yang, Q. 2012. Towards mobile intelligence: Learning from GPS history data for collaborative recommendation. Artif. Intell. 184--185, 17--37.

Digital Library

Cited By

Kim JZo H(2025)Am I watching or being watched? Exploring the selective disclosure paradox in users’ self-censorship to dataveillance awareness in video recommender systemsTelematics and Informatics10.1016/j.tele.2025.10225398(102253)Online publication date: Apr-2025
https://doi.org/10.1016/j.tele.2025.102253
Wang CBoerman SKroon AMöller JH de Vreese C(2024)The artificial intelligence divide: Who is the most vulnerable?New Media & Society10.1177/14614448241232345Online publication date: 26-Feb-2024
https://doi.org/10.1177/14614448241232345
Aliyu SKhan SMbodj AOsho OLi LKnijnenburg BCherubini M(2024)Participatory Design to Address Disclosure-Based CyberbullyingProceedings of the 2024 ACM Designing Interactive Systems Conference10.1145/3643834.3660716(1547-1565)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1145/3643834.3660716
Show More Cited By

Index Terms

Making Decisions about Privacy: Information Disclosure in Context-Aware Recommender Systems

Recommendations

Helping users with information disclosure decisions: potential for adaptation
IUI '13: Proceedings of the 2013 international conference on Intelligent user interfaces

Personalization relies on personal data about each individual user. Users are quite often reluctant though to disclose information about themselves and to be "tracked" by a system. We investigated whether different types of rationales (justifications) ...
Preventing sensitive relationships disclosure for better social media preservation

A fundamental aspect of all social networks is information sharing. It is one of the most common forms of online interaction that is tightly associated with social media preservation and information disclosure. As such, information sharing is commonly ...
Recommender Systems for Privacy Management: A Framework
HASE '14: Proceedings of the 2014 IEEE 15th International Symposium on High-Assurance Systems Engineering

Social media and online service providers are increasingly collecting personal information. In order for users to make decisions about their online privacy, they will have to read through a dense and hard-to-understand privacy policy. We developed a ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Transactions on Interactive Intelligent Systems

ACM Transactions on Interactive Intelligent Systems Volume 3, Issue 3

October 2013

154 pages

ISSN:2160-6455

EISSN:2160-6463

DOI:10.1145/2533670

Issue’s Table of Contents

Copyright © 2013 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: 01 October 2013

Accepted: 01 February 2013

Revised: 01 December 2012

Received: 01 March 2012

Published in TIIS Volume 3, Issue 3

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed

Funding Sources

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

93
Total Citations
View Citations
2,173
Total Downloads

Downloads (Last 12 months)129
Downloads (Last 6 weeks)5

Reflects downloads up to 01 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Kim JZo H(2025)Am I watching or being watched? Exploring the selective disclosure paradox in users’ self-censorship to dataveillance awareness in video recommender systemsTelematics and Informatics10.1016/j.tele.2025.10225398(102253)Online publication date: Apr-2025
https://doi.org/10.1016/j.tele.2025.102253
Wang CBoerman SKroon AMöller JH de Vreese C(2024)The artificial intelligence divide: Who is the most vulnerable?New Media & Society10.1177/14614448241232345Online publication date: 26-Feb-2024
https://doi.org/10.1177/14614448241232345
Aliyu SKhan SMbodj AOsho OLi LKnijnenburg BCherubini M(2024)Participatory Design to Address Disclosure-Based CyberbullyingProceedings of the 2024 ACM Designing Interactive Systems Conference10.1145/3643834.3660716(1547-1565)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1145/3643834.3660716
Musick GDuan WNajafian SSengupta SFlathmann CKnijnenburg BMcNeese N(2024)To Share or Not to Share: Understanding and Modeling Individual Disclosure Preferences in Recommender Systems for the WorkplaceProceedings of the ACM on Human-Computer Interaction10.1145/36330748:GROUP(1-28)Online publication date: 16-Feb-2024
https://dl.acm.org/doi/10.1145/3633074
Sun ZPan GYang RZang GZhou J(2024)Consumer privacy decision-making in digital era: an integrative review and taxonomyAslib Journal of Information Management10.1108/AJIM-12-2023-0526Online publication date: 10-Oct-2024
https://doi.org/10.1108/AJIM-12-2023-0526
Starke CMetikoš LHelberger Nde Vreese C(2024)Contesting personalized recommender systems: a cross-country analysis of user preferencesInformation, Communication & Society10.1080/1369118X.2024.2363926(1-20)Online publication date: 3-Jul-2024
https://doi.org/10.1080/1369118X.2024.2363926
Haruyama MHidaka K(2024)What influences users to provide explicit feedback? A case of food delivery recommendersUser Modeling and User-Adapted Interaction10.1007/s11257-023-09385-834:3(753-796)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1007/s11257-023-09385-8
Najafian SMusick GKnijnenburg BTintarev N(2024)How do people make decisions in disclosing personal information in tourism group recommendations in competitive versus cooperative conditions?User Modeling and User-Adapted Interaction10.1007/s11257-023-09375-w34:3(549-581)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1007/s11257-023-09375-w
Pera MCena FLandoni MMusto CStarke A(2024)Human Factors in User Modeling for Intelligent SystemsA Human-Centered Perspective of Intelligent Personalized Environments and Systems10.1007/978-3-031-55109-3_1(3-42)Online publication date: 1-May-2024
https://doi.org/10.1007/978-3-031-55109-3_1
(2024)Towards Transparent and Parsimonious Customer Data CollectionNew Frontiers of Customer Strategy10.1002/9781394300747.ch7(81-98)Online publication date: 21-Jun-2024
https://doi.org/10.1002/9781394300747.ch7
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.

Figures

Tables

Media

View Issue’s Table of Contents