Is seeing believing?: how recommender system interfaces affect users' opinions
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Utility Covariances and Context Effects in Conjoint Mnp Models
Marketing Science
A Sequential Monte Carlo Method for Bayesian Analysis of Massive Datasets
Data Mining and Knowledge Discovery
Bayesian Statistics and Marketing
Marketing Science
Shilling recommender systems for fun and profit
Proceedings of the 13th international conference on World Wide Web
Management Science
Identifying Innovators for the Cross-Selling of New Products
Management Science
On Customized Goods, Standard Goods, and Competition
Marketing Science
Marketing Models of Service and Relationships
Marketing Science
Persuasion in Recommender Systems
International Journal of Electronic Commerce
International Journal of Electronic Commerce
Research Note: User Design of Customized Products
Marketing Science
Computer Methods and Programs in Biomedicine
A survey of convergence results on particle filtering methods forpractitioners
IEEE Transactions on Signal Processing
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
Up close and personalized: a marketing view of recommendation systems
Proceedings of the third ACM conference on Recommender systems
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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.