article

My Mobile Music: An Adaptive Personalization System for Digital Audio Players

Authors:

Tuck Siong Chung,

Roland T. Rust,

Michel WedelAuthors Info & Claims

Marketing Science, Volume 28, Issue 1

Pages 52 - 68

https://doi.org/10.1287/mksc.1080.0371

Published: 01 January 2009 Publication History

Abstract

New information technologies increasingly make it possible for service providers to adaptively personalize their service, fine-tuning the service over time for each individual customer, based on observation of that customer's behavior. We propose an “Adaptive Personalization System” and illustrate its implementation for digital audio players, a product category with rapidly expanding sales. The proposed system automatically downloads personalized playlists of MP3 songs into a consumer's mobile digital audio device and requires little proactive user effort (i.e., no explicit indication of preferences or ratings for songs). The system works in real time and is scalable to the massive data typically encountered in personalization applications. A simulation study shows the Adaptive Personalization System to outperform benchmark approaches. We implemented the Adaptive Personalization System on Palm PDAs and tested its performance with digital audio users. For actual users, the Adaptive Personalization System provides substantial improvements over benchmark approaches both in terms of the number of songs listened to and listening duration.

References

[1]

Aksoy, L., Bloom, P. N., Lurie, N. H. and Cooil, B., "Should recommendation agents think like people?," J. Service Res., v8, pp. 297-315, 2006.

[2]

Ansari, A. and Jedidi, K., "Bayesian factor analysis for multilevel binary observations," Psychometrika, v65, pp. 475-496, 2000.

[3]

Ansari, A. and Mela, C. F., "E-customization," J. Marketing Res., v40, pp. 131-145, 2003.

[4]

Ansari, A., Essegaier, S. and Kohli, R., "Internet recommendation systems," J. Marketing Res., v37, pp. 363-375, 2000.

[5]

Ariely, D., Lynch, J. G. and Aparicio, M., "Learning by collaborative and individual-based recommendation agents," J. Consumer Psych., v14, pp. 81-95, 2004.

[6]

Arulampalam, S., Maskell, S., Gordon, N. and Clapp, T., "A tutorial on particle filters for on-line nonlinear/non-Gaussian Bayesian tracking," IEEE Trans. Signal Processing, v50, pp. 174-188, 2002.

Digital Library

[7]

Bodapati, A., "Recommender systems with purchase data," J. Marketing Res., v45, pp. 77-93, 2008.

[8]

Castelluccio, M., "The music genome project," Strategic Finance, v88, pp. 57-58, 2006.

[9]

Chipman, H., George, E. I. and McCulloch, R. E., "The practical implementation of Bayesian model selection," Model Selection, v38, Institute of Mathematical Statistics, Beachwood, OH, pp. 65-116, 2001.

[10]

Cook, R. D. and Nachtsheim, C. J., "A comparison of algorithms for constructing exact d-optimal design," Technometrics, v22, pp. 315-324, 1980.

[11]

Cooke, A. D. J., Sujan, H., Sujan, M. and Weitz, B. A., "Marketing the unfamiliar: The role of context and item-specific information in electronic agent recommendations," J. Marketing Res., v39, pp. 488-497, 2002.

[12]

Cosley, D., Lam, S. K., Albert, I., Konstan, J. A. and Riedl, J., "Is seeing believing? How recommender interfaces affect users' opinions," Recommender Systems Soc. Comput., v5, pp. 585-592, 2003.

[13]

Crisan, D. and Doucet, A., "A survey of convergence results on particle filtering methods for practitioners," IEEE Trans. Signal Processing, v50, pp. 736-746, 2002.

Digital Library

[14]

Diehl, K. R., Kornish, L. and Lynch, J. G., "Smart agents: When lower search costs for quality information increase price sensitivity," J. Consumer Res., v30, pp. 56-71, 2003.

[15]

George, E. I. and Foster, D. P., "Calibration and empirical Bayes variable selection," Biometrika, v87, pp. 731-747, 2000.

[16]

Gilbride, T. J. and Allenby, G. A., "A choice model with conjunctive, disjunctive, and compensatory screening rules," Marketing Sci., v23, pp. 391-406, 2006.

[17]

Gower, J. C., "A general coefficient of similarity and some of its properties," Biometrics, v27, pp. 857-874, 1971.

[18]

Gretzel, U. and Fesenmaier, D. R., "Persuasion in recommender systems," Internat. J. Electronic Commerce, v11, pp. 81-100, 2006.

Digital Library

[19]

Haaijer, R. E., Wedel, M., Vriens, M. and Wansbeek, T. J., "Utility covariances and context effects in conjoint MNP models," Marketing Sci., v17, pp. 236-252, 1998.

Digital Library

[20]

Häubl, G. and Murray, K. B., "Preference construction and persistence in digital marketplaces: The role of electronic recommendation agents," J. Consumer Psych., v13, pp. 75-91, 2003.

[21]

Häubl, G. and Trifts, V., "Consumer decision making in online shopping environments: The effects of interactive decision aids," Marketing Sci., v19, pp. 4-21, 2000.

Digital Library

[22]

Hoeting, J. A., Madigan, D., Raftery, A. E. and Volinsky, C. T., "Bayesian model averaging: A tutorial," Statist. Sci., v14, pp. 382-417, 1999.

[23]

Kamakura, W. A. and Wedel, M., "Factor analysis and missing data," J. Marketing Res., v37, pp. 490-498, 2000.

[24]

Kamakura, W. A., Kosslar, B. and Wedel, M., "Identifying innovators for the cross selling of new products," Management Sci., v50, pp. 1120-1132, 2004.

Digital Library

[25]

Komiak, S. Y. X. and Benbasat, I., "The effects of personalization and familiarity on trust and adoption of recommendation agents," MIS Quart., v30, pp. 941-960, 2006.

Digital Library

[26]

Kong, A., Liu, J. S. and Wong, W. H., "Sequential imputations and Bayesian missing data problems," J. Amer. Statist. Assoc., v89, pp. 278-288, 1994.

[27]

Kuang, C., "Free music now! Lala.com's plan to give songs away could upend the industry," Wired, 2007.

[28]

Kuhfeld, W. F., Tobias, R. D. and Garratt, M., "Efficient experimental design with marketing research applications," J. Marketing Res., v31, pp. 545-557, 1994.

[29]

Lam, S. K. and Riedl, J., "Shilling recommender systems for fun and profit," WWW '04: Proc. 13th Internat. Conf. World Wide Web, ACM Press, New York, pp. 393-402, 2004.

[30]

Madigan, D. and Raftery, A. E., "Model selection and accounting for model uncertainty in graphical models using Occam's window," J. Amer. Statist. Assoc., v89, pp. 1535-1546, 1994.

[31]

Montgomery, A. L., Hosanagar, K., Krishnan, R. and Clay, K. B., "Designing a better shopbot," Management Sci., v50, pp. 189-206, 2004.

Digital Library

[32]

Nikolaeva, R. and Sriram, S., "The moderating role of consumer and product characteristics on the value of customized on-line recommendation," Internat. J. Electronic Commerce, v11, pp. 101-123, 2006.

Digital Library

[33]

Ogungbenro, K., Graham, G., Gueorguieva, I. and Aarons, L., "The use of a modified Federov exchange algorithm to optimise sampling times for population pharmacokinetic experiments," Comput. Methods Programs Biomedicine, v80, pp. 115-125, 2005.

Digital Library

[34]

Pronzato, L. and Thierry, É., "Sequential experimental design and response optimisation," Statist. Methods Appl., v11, pp. 277-292, 2003.

[35]

Rainie, L. and Madden, M., "Podcasting," 2005.

[36]

Randall, T., Terwiesch, C. and Ulrich, K. T., "User design of customized products," Marketing Sci., v26, pp. 268-280, 2007.

Digital Library

[37]

Ridgeway, G. and Madigan, D., "A sequential Monte Carlo method for Bayesian analysis of massive data sets," Data Mining Knowledge Discovery, v7, pp. 301-319, 2003.

[38]

Rossi, P. and Allenby, G., "Bayesian statistics and marketing," Marketing Sci., v22, pp. 304-328, 2003.

Digital Library

[39]

Rossi, P. E., Allenby, G. and McCulloch, R., Bayesian Statistics and Marketing, John Wiley & Sons, Chichester, West Sussex, UK, 2005.

[40]

Rossi, P. E., Gilula, Z. and Allenby, G. M., "Overcoming scale usage heterogeneity: A Bayesian hierarchical approach," J. Amer. Statist. Assoc., v96, pp. 20-31, 2001.

[41]

Rust, R. T. and Chung, T. S., "Marketing models of service and relationships," Marketing Sci., v25, pp. 560-580, 2006.

Digital Library

[42]

Saha, A. and Hilton, L., "Expo-power: A flexible hazard function for duration data models," Econom. Lett., v54, pp. 227-233, 1997.

[43]

Sandor, Z. and Wedel, M., "Designing conjoint choice experiments using managers' prior beliefs," J. Marketing Res., v38, pp. 430-444, 2001.

[44]

Sha, N., Tadesse, M. G. and Vannucci, M., "Bayesian variable selection for the analysis of microarray data with censored outcomes," BioInformatics, v22, pp. 2262-2268, 2006.

Digital Library

[45]

Sun, B., "Technology innovation and implications for customer relationship management," Marketing Sci., v25, pp. 594-597, 2006.

Digital Library

[46]

Syam, N. B. and Kumar, N., "On customized goods, standard goods, and competition," Marketing Sci., v25, pp. 525-537, 2006.

Digital Library

[47]

Tam, K. Y. and Ho, S. Y., "Understanding the impact of web personalization on user information processing and decision outcomes," MIS Quart., v30, pp. 865-890, 2006.

Digital Library

[48]

Verdinelli, I. and Kadane, J. B., "Bayesian designs for maximizing information and outcome," J. Amer. Statist. Assoc., v87, pp. 510-515, 1992.

[49]

Xiao, B. and Benbasat, I., "E-commerce product recommendation agents: Use, characteristics, and impact," MIS Quart., v31, pp. 137-209, 2007.

Digital Library

[50]

Ying, Y. P., Feinberg, F. and Wedel, M., "Leveraging missing ratings to improve online recommendation systems," J. Marketing Res., v43, pp. 355-365, 2006.

Cited By

Guhl DPaetz FWagner UWedel M(2024)Predicting and optimizing marketing performance in dynamic marketsOR Spectrum10.1007/s00291-024-00755-146:1(1-27)Online publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1007/s00291-024-00755-1
Mele CMarzullo MMorande SSpena T(2022)How Artificial Intelligence Enhances Human Learning AbilitiesService Science10.1287/serv.2021.028914:2(77-89)Online publication date: 1-Jun-2022
https://dl.acm.org/doi/10.1287/serv.2021.0289
Nobile TKalbaska N(2020)An Exploration of Personalization in Digital Communication. Insights in FashionHCI in Business, Government and Organizations10.1007/978-3-030-50341-3_35(456-473)Online publication date: 19-Jul-2020
https://dl.acm.org/doi/10.1007/978-3-030-50341-3_35
Show More Cited By

Recommendations

New Recommendation Techniques for Multicriteria Rating Systems

Traditional single-rating recommender systems have been successful in a number of personalization applications, but the research area of multicriteria recommender systems has been largely untouched. Taking full advantage of multicriteria ratings in ...
A novel music recommender by discovering preferable perceptual-patterns from music pieces
SAC '10: Proceedings of the 2010 ACM Symposium on Applied Computing

Nowadays, advanced information and communication technologies ease the access of music pieces. However, it is still hard for the users to find what she/he prefers from a huge amount of music works. To solve this problem, most music recommenders based on ...
Integrating collaborative filtering and matching-based search for product recommendations

Currently, recommender systems (RS) have been widely applied in many commercial e-commerce sites to help users deal with the information overload problem. Recommender systems provide personalized recommendations to users and, thus, help in making good ...

Comments

Information & Contributors

Information

Published In

cover image Marketing Science

Marketing Science Volume 28, Issue 1

January 2009

78 pages

ISSN:1526-548X

Issue’s Table of Contents

Publisher

INFORMS

Linthicum, MD, United States

Publication History

Published: 01 January 2009

Received: 19 July 2007

Author Tags

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

13
Total Citations
View Citations
2
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 02 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

Guhl DPaetz FWagner UWedel M(2024)Predicting and optimizing marketing performance in dynamic marketsOR Spectrum10.1007/s00291-024-00755-146:1(1-27)Online publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1007/s00291-024-00755-1
Mele CMarzullo MMorande SSpena T(2022)How Artificial Intelligence Enhances Human Learning AbilitiesService Science10.1287/serv.2021.028914:2(77-89)Online publication date: 1-Jun-2022
https://dl.acm.org/doi/10.1287/serv.2021.0289
Nobile TKalbaska N(2020)An Exploration of Personalization in Digital Communication. Insights in FashionHCI in Business, Government and Organizations10.1007/978-3-030-50341-3_35(456-473)Online publication date: 19-Jul-2020
https://dl.acm.org/doi/10.1007/978-3-030-50341-3_35
Farias VLi A(2019)Learning Preferences with Side InformationManagement Science10.1287/mnsc.2018.309265:7(3131-3149)Online publication date: 1-Jul-2019
https://dl.acm.org/doi/10.1287/mnsc.2018.3092
Ansari ALi YZhang J(2018)Probabilistic Topic Model for Hybrid Recommender SystemsMarketing Science10.1287/mksc.2018.111337:6(987-1008)Online publication date: 1-Nov-2018
https://dl.acm.org/doi/10.1287/mksc.2018.1113
Datta HKnox GBronnenberg B(2018)Changing Their TuneMarketing Science10.1287/mksc.2017.105137:1(5-21)Online publication date: 1-Jan-2018
https://dl.acm.org/doi/10.1287/mksc.2017.1051
Gan MJiang R(2018)FLOWERFuture Generation Computer Systems10.1016/j.future.2017.02.02778:P1(462-473)Online publication date: 1-Jan-2018
https://dl.acm.org/doi/10.1016/j.future.2017.02.027
Lv HWan YWu F(2017)Effect of online personalization services on complementary firmsElectronic Commerce Research and Applications10.1016/j.elerap.2017.06.00124:C(12-22)Online publication date: 1-Jul-2017
https://dl.acm.org/doi/10.1016/j.elerap.2017.06.001
Feng XFu SQin J(2016)Determinants of consumers' attitudes toward mobile advertisingComputers in Human Behavior10.1016/j.chb.2016.05.02463:C(334-341)Online publication date: 1-Oct-2016
https://dl.acm.org/doi/10.1016/j.chb.2016.05.024
Luo XAndrews MFang ZPhang C(2014)Mobile TargetingManagement Science10.1287/mnsc.2013.183660:7(1738-1756)Online publication date: 1-Jul-2014
https://dl.acm.org/doi/10.1287/mnsc.2013.1836
Show More Cited By

View Options

View options

Figures

Tables

Media

View Issue’s Table of Contents