Learning and adaptivity in interactive recommender systems
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Usage-based web recommendations: a reinforcement learning approach
Proceedings of the 2007 ACM conference on Recommender systems
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VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Adapting the interaction state model in conversational recommender systems
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Computational Intelligence techniques for Web personalization
Web Intelligence and Agent Systems
Improving recommender systems with adaptive conversational strategies
Proceedings of the 20th ACM conference on Hypertext and hypermedia
ICCBR '09 Proceedings of the 8th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
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Automatic optimization of web recommendations using feedback and ontology graphs
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A large number of websites use online recommendationsto make web users interested in their products orcontent. Since no single recommendation approach isalways best it is necessary to effectively combine differentrecommendation algorithms. This paper describes thearchitecture of a rule-based recommendation systemwhich combines recommendations from different algorithmsin a single recommendation database. Reinforcementlearning is applied to continuously evaluate theusers' acceptance of presented recommendations and toadapt the recommendations to reflect the users' interests.We describe the general architecture of the system, thedatabase structure, the learning algorithm and the testsetting for assessing the quality of the approach.