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 of thinking AI in marketing are various personalized recommendation systems, such as Netflix movie recommendations and Amazon cross-selling recommendations.
“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
In the section labeled “Table of Contents” (page not provided), the paper develops a personalized music recommendation system and discusses recommendation systems in marketing literature. It does not establish that these are the most common marketing applications or mention Netflix movie recommendations or Amazon cross-selling. (LLM, conf=0.85)
Experimental — accuracy not yet measuredThe academic literature on AI in marketing can be sorted into four main types: technical AI algorithms, customers' psychological reactions, effects of AI on jobs and society, and managerial and strategic issues.
“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”
From A strategic framework for artificial intelligence in marketing (2021) · cites this paper as Chung et al. 2009
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- 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
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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.
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