Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Reinforcement Learning Architecture for Web Recommendations
ITCC '04 Proceedings of the International Conference on Information Technology: Coding and Computing (ITCC'04) Volume 2 - Volume 2
IEEE Transactions on Knowledge and Data Engineering
An MDP-Based Recommender System
The Journal of Machine Learning Research
Case-based recommender systems
The Knowledge Engineering Review
Learning more effective dialogue strategies using limited dialogue move features
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Learning and adaptivity in interactive recommender systems
Proceedings of the ninth international conference on Electronic commerce
Usage-based web recommendations: a reinforcement learning approach
Proceedings of the 2007 ACM conference on Recommender systems
A hybrid web recommender system based on Q-learning
Proceedings of the 2008 ACM symposium on Applied computing
COOPERATIVE QUERY REWRITING FOR DECISION MAKING SUPPORT AND RECOMMENDER SYSTEMS
Applied Artificial Intelligence
A personalized system for conversational recommendations
Journal of Artificial Intelligence Research
ExpertClerk: navigating shoppers' buying process with the combination of asking and proposing
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Improving recommender systems with adaptive conversational strategies
Proceedings of the 20th ACM conference on Hypertext and hypermedia
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Conventional conversational recommender systems support interaction strategies that are hard-coded into the system in advance. In this context, Reinforcement Learning techniques have been proposed to learn an optimal, user-adapted interaction strategy, by encoding relevant information as features describing the state of the interaction. In this regard, a crucial problem is to select this subset of relevant features from a larger set, for any given recommendation task. In this paper, we tackle this issue of state features selection by proposing and exploiting two criteria for determining feature relevancy. Our results show that adding a feature might not always be beneficial, that the relevancy is influenced by the user behavior, and also by the numerical reinforcement signal which is exploited by the adaptive system for learning the optimal strategy. These results, obtained in off-line simulations and in a simplified scenario, were exploited to design an adaptive recommender system for an online travel planning application.