Communications of the ACM
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
An MDP-Based Recommender System
The Journal of Machine Learning Research
A Case-Based Song Scheduler for Group Customised Radio
ICCBR '07 Proceedings of the 7th international conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Collaborative Filtering for Implicit Feedback Datasets
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Hybrid web recommender systems
The adaptive web
Characterisation of explicit feedback in an online music recommendation service
Proceedings of the fourth ACM conference on Recommender systems
UMAP'11 Proceedings of the 19th international conference on User modeling, adaption, and personalization
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The very large majority of recommender systems are running as server-side applications, and they are controlled by the content provider, i.e., who provides the recommended items. This paper focuses on a different scenario: the user is supposed to be able to access content from multiple providers, in our application they offer radio channels, and it is up to a personal recommender installed on the clients' side to decide which channel to select and recommend to the user. We exploit the implicit feedback derived from the user's listening behavior, and we model channel recommendation as a sequential decision making problem. We have implemented a personal RS that integrates reinforcement learning techniques to decide what channel to play every time the user asks for a new music track or the current track finishes playing. In a live user study we show that the proposed system can sequentially select the next channel to play such that the users listen to the streamed tracks for a larger fraction, and for more time, compared to a baseline system not exploiting implicit feedback.