Communications of the ACM
Mining navigation history for recommendation
Proceedings of the 5th international conference on Intelligent user interfaces
Automatic personalization based on Web usage mining
Communications of the ACM
Using information scent to model user information needs and actions and the Web
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
Web usage mining: discovery and applications of usage patterns from Web data
ACM SIGKDD Explorations Newsletter
SEWeP: using site semantics and a taxonomy to enhance the Web personalization process
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Item-based top-N recommendation algorithms
ACM Transactions on Information Systems (TOIS)
Web personalization integrating content semantics and navigational patterns
Proceedings of the 6th annual ACM international workshop on Web information and data management
Modeling user interests by conceptual clustering
Information Systems - Special issue: The semantic web and web services
Usage-based web recommendations: a reinforcement learning approach
Proceedings of the 2007 ACM conference on Recommender systems
Incorporating concept hierarchies into usage mining based recommendations
WebKDD'06 Proceedings of the 8th Knowledge discovery on the web international conference on Advances in web mining and web usage analysis
Adapting the interaction state model in conversational recommender systems
Proceedings of the 10th international conference on Electronic commerce
Improving recommender systems with adaptive conversational strategies
Proceedings of the 20th ACM conference on Hypertext and hypermedia
Implicit feedback techniques on recommender systems applied to electronic books
Computers in Human Behavior
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Different efforts have been made to address the problem of information overload on the Internet. Recommender systems aim at directing users through this information space, toward the resources that best meet their needs and interests. Web Content Recommendation has been an active application area for Information Filtering, Web Mining and Machine Learning research. Recent studies show that combining the conceptual and usage information can improve the quality of web recommendations. In this paper we exploit this idea to enhance a reinforcement learning framework, primarily devised for web recommendations based on web usage data. A hybrid web recommendation method is proposed by making use of the conceptual relationships among web resources to derive a novel model of the problem, enriched with semantic knowledge about the usage behavior. With our hybrid model for the web page recommendation problem we show the apt and flexibility of the reinforcement learning framework in the web recommendation domain, and demonstrate how it can be extended in order to incorporate various sources of information. We evaluate our method under different settings and show how this method can improve the overall quality of web recommendations.