GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
IEEE Transactions on Knowledge and Data Engineering
Recommender Systems: An Introduction
Recommender Systems: An Introduction
Rethinking the recommender research ecosystem: reproducibility, openness, and LensKit
Proceedings of the fifth ACM conference on Recommender systems
MyMediaLite: a free recommender system library
Proceedings of the fifth ACM conference on Recommender systems
Predicting performance in recommender systems
Proceedings of the fifth ACM conference on Recommender systems
When recommenders fail: predicting recommender failure for algorithm selection and combination
Proceedings of the sixth ACM conference on Recommender systems
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The problem of information overload has been a relevant and active research topic for the past twenty years. Since then, numerous algorithms and recommendation approaches have been proposed, which gives rise to a new type of problem: recommendation algorithm overload. Although hybrid recommendation techniques, which combine the strengths of individual recommenders, have become well-accepted, the procedure of building and tuning a hybrid recommender is still a tedious and time-consuming process. In our work, we focus on dynamically building personalized hybrid recommender systems on an individual user basis. By means of a dynamic online learning strategy we combine the most appropriate recommendation algorithms for a user based on realtime relevance feedback. Learning effectiveness of genetic algorithms, machine learning techniques and other optimization approaches will be studied in both an offline and online setting.