Analysis of recommendation algorithms for e-commerce
Proceedings of the 2nd ACM conference on Electronic commerce
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
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
Unifying collaborative and content-based filtering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
AWESOME: a data warehouse-based system for adaptive website recommendations
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Application of Item Response Theory to Collaborative Filtering
ISNN '09 Proceedings of the 6th International Symposium on Neural Networks on Advances in Neural Networks
An efficient web recommendation system based on modified IncSpan algorithm
International Journal of Knowledge and Web Intelligence
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Web recommendation systems have become a popular means to improve the usability of web sites. This paper describes the architecture of a rule-based recommendation system and presents its evaluation on two real-life applications. The architecture combines recommendations from different algorithms in a recommendation database and applies feedback-based machine learning to optimize the selection of the presented recommendations. The recommendations database also stores ontology graphs, which are used to semantically enrich the recommendations. We describe the general architecture of the system and the test setting, illustrate the application of several optimization approaches and present comparative results.