For instance, on MovieLens we achieve as much as a 17% improvement in prediction accuracy for niche movies, cold-start items, and even the most badly-modeled ...
We offer a new technique called focused learning, based on hyperparameter optimization and a customized matrix factorization objective. Applying focused ...
Beyond Globally Optimal: Focused Learning for Improved ...
research.google › pubs › beyond-globall...
We offer a new technique called focused learning, based on hyperparameter optimization and a customized matrix factorization objective. Applying focused ...
For instance, on MovieLens we achieve as much as a 17% improvement in prediction accuracy for niche movies, cold-start items, and even the most badly-modeled ...
Sep 17, 2019 · Bibliographic details on Beyond Globally Optimal: Focused Learning for Improved Recommendations.
... Beyond Globally Optimal: Focused Learning for Improved Recommendations”,. WWW, 2017. [13] H. Wang, “MatRec: Matrix Factorization for Highly Skewed Dataset ...
In this paper, we investigate whether better results can be obtained by learning multiple multi-target models on several partitions of the targets.
Sep 8, 2024 · Recommender systems rely heavily on the predictive accuracy of the learning algorithm. Most work on improving accuracy has focused on the ...
Oct 22, 2024 · We demonstrate how and when targeted training improves over global training through theoretical analysis and simulation. Our experiments on ...
Beyond globally optimal: Focused learning for improved recommendations. In Proceedings of the 26th International Conference on World Wide Web, pages 203–212 ...