Communications of the ACM
Usability Engineering
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Refining Conversational Case Libraries
ICCBR '97 Proceedings of the Second International Conference on Case-Based Reasoning Research and Development
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
Learning user interaction models for predicting web search result preferences
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
International Journal of Electronic Commerce
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
Adapting the interaction state model in conversational recommender systems
Proceedings of the 10th international conference on Electronic commerce
Hybrid reinforcement/supervised learning of dialogue policies from fixed data sets
Computational Linguistics
Optimizing dialogue management with reinforcement learning: experiments with the NJFun system
Journal of Artificial Intelligence Research
A personalized system for conversational recommendations
Journal of Artificial Intelligence Research
Knowledge-Based Systems
Hybrid web recommender systems
The adaptive web
Personalization in e-commerce applications
The adaptive web
Proceedings of the 2nd ACM SIGCHI symposium on Engineering interactive computing systems
Critiquing-based recommenders: survey and emerging trends
User Modeling and User-Adapted Interaction
Harnessing geo-tagged resources for Web personalization
Proceedings of the 27th Annual ACM Symposium on Applied Computing
Student progress assessment with the help of an intelligent pupil analysis system
Engineering Applications of Artificial Intelligence
Towards effective course-based recommendations for public tenders
International Journal of Knowledge and Web Intelligence
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Conversational recommender systems (CRSs) assist online users in their information-seeking and decision making tasks by supporting an interactive process. Although these processes could be rather diverse, CRSs typically follow a fixed strategy, e.g., based on critiquing or on iterative query reformulation. In a previous paper, we proposed a novel recommendation model that allows conversational systems to autonomously improve a fixed strategy and eventually learn a better one using reinforcement learning techniques. This strategy is optimal for the given model of the interaction and it is adapted to the users' behaviors. In this paper we validate our approach in an online CRS by means of a user study involving several hundreds of testers. We show that the optimal strategy is different from the fixed one, and supports more effective and efficient interaction sessions.