ABSTRACT
In the Recommender Systems field ensemble techniques gain growing interest. This approach is based on the idea of mixing many recommenders and to get an average prediction from all of them. Even if it is useful this process may be very expensive from a computational point of view. We propose the use of Operations Research techniques in order to optimize the balance of different predictors and to accelerate it. We show that this problem can be generalized, thus we provide a mathematical framework which helps to find further improvements.
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Index Terms
- Optimal recommender systems blending
Recommendations
A Scalable, Accurate Hybrid Recommender System
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A Clustering Approach for Personalizing Diversity in Collaborative Recommender Systems
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A Novel Framework for Improving Recommender Diversity
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