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My Mobile Music: An Adaptive Personalization System for Digital Audio Players
Open accessAbstract
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.
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“The most common applications in marketing are various personalized recommendation systems (Chung et al. 2009; Chung et al. 2016), such as Netflix movie recommendations and Amazon cross-selling recommendations.”
From A strategic framework for artificial intelligence in marketing (2021) · cites this paper as Chung et al. 2009
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“The academic literature on AI in marketing may be sorted into four main types. These are (1) technical AI algorithms for solving specific marketing problems (e.g., Chung et al. 2009; Chung et al. 2016; Dzyabura and Hauser 2011, 2019), (2) customers ' psychological reactions to AI (e.g., Luo et al. 2019; Mende et al. 2019), (3) effects of AI on jobs and society (e.g., Autor and Dorn 2013; Frey and”
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A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking
Arulampalam, M. S., Maskell, S. R., Gordon, N., & Clapp, T. C. (2002) · IEEE Transactions on Signal Processing · doi:10.1109/78.978374 · Cited by 11,531
Sequential Monte Carlo Methods in Practice
Arnaud Doucet, B. (2001) · Springer eBooks · doi:10.1007/978-1-4757-3437-9 · Cited by 7,516
A General Coefficient of Similarity and Some of Its Properties
Gower, J. C. (1971) · Biometrics · doi:10.2307/2528823 · Cited by 5,517
Hoeting, J. A., Madigan, D., Raftery, A. E., & Volinsky, C. T. (1999) · Statistical Science · doi:10.1214/ss/1009212519 · Cited by 4,118
Konstan, J. A., Miller, B., Maltz, D. A., Herlocker, J. L., Gordon, L. R., & Riedl, J. (1997) · Communications of the ACM · doi:10.1145/245108.245126 · Cited by 2,367
Alba, J. W., Lynch, J. G., Weitz, B. A., Janiszewski, C. A., Lutz, R., Sawyer, A. G., & Wood, S. L. (1997) · Journal of Marketing · doi:10.1177/002224299706100303 · Cited by 2,211
Bayesian Experimental Design: A Review
Chaloner, K. M., & Verdinelli, I. (1995) · Statistical Science · doi:10.1214/ss/1177009939 · Cited by 1,843
Consumer Decision Making in Online Shopping Environments: The Effects of Interactive Decision Aids
Häubl, G., & Trifts, V. (2000) · Marketing Science · doi:10.1287/mksc.19.1.4.15178 · Cited by 1,731
The do re mi's of everyday life: The structure and personality correlates of music preferences.
Rentfrow, P. J., & Gosling, S. D. (2003) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.84.6.1236 · Cited by 1,695
The Effects of Personalization and Familiarity on Trust and Adoption of Recommendation Agents1
Komiak, S. Y. X., & Benbasat, I. (2006) · MIS Quarterly · doi:10.2307/25148760 · Cited by 1,374
Research works printed but not linked: 4
- Castelluccio M., 2006, Strategic Finance, 88, 57
- Cosley D., 2003, Recommender Systems Soc. Comput., 5, 585
- 10.1207/S15327663JCP13-1&2_07 · DOI
- Kuang C., 2007, Wired
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Rushan, M. R. I. (2020) · Behaviour and Information Technology · doi:10.1080/0144929x.2020.1787515 · Cited by 3
Sargın, S., Oralhan, B., & ÜVENÇ, A. S. (2020) · European Journal of Science and Technology · doi:10.31590/ejosat.793072 · Cited by 2
Mladenović, M., Jauković-Jocić, K., Đukić, T., Momčilović, O., & Maričić, S. (2024) · SCIENCE International Journal · doi:10.35120/sciencej0304099m · Cited by 5
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Xu, Z. Y., Zhu, Q., Guo, Y., Giuli, T. J., Prasad, K. V., & Huang, H. (2011) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2011.2159378 · Cited by 1
Topic Modeling of Market Responses for Large-Scale Transaction Data
Ishigaki, T., Terui, N., Sato, T., & Allenby, G. M. (2015) · Institutional Repositories DataBase (IRDB) · Cited by 1
Metadata
- Authors
- Chung, T. S., Rust, R. T., & Wedel, M.
- Affiliations
- Nanyang Technological University (SG)
- University of Maryland, College Park (US)
- Venue
- Marketing Science
- Published
- 2009-01
- Volume
- 28
- Issue
- 1
- Pages
- 52-68
- ISSN
- 0732-2399 (ISSN-L)
- 1526-548X
- OpenAlex
- W2043115469
- Cited by
- 119
- References
- 58
- Citations by year
- 2026: 5 · 2025: 3 · 2024: 4 · 2023: 8 · 2022: 18 · 2021: 5 · 2020: 14 · 2019: 10 · 2018: 6 · 2017: 15 · 2016: 7 · 2015: 4 · 2014: 4 · 2013: 6 · 2012: 3
- FWCI
- 7.28
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Locations
- Marketing Science (publishedVersion)
- EUR Research Repository (Erasmus University Rotterdam) (publishedVersion)
- University Libraries (University of Maryland) (submittedVersion)
- EUR Research Repository (Erasmus University Rotterdam) (submittedVersion)
- Funders
- Ministry of Education, India
- Topics
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Social Sciences › Decision Sciences › Management Science and Operations Research › Advanced Bandit Algorithms Research
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
- Keywords
- adaptive personalization, digital music players, personalized playlists, MP3, mobile music, user behavior observation, recommender systems
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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