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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”
From A strategic framework for artificial intelligence in marketing (2021) · cites this paper as Chung et al. 2009
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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
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