On explainable recommender systems based on fuzzy rule generation techniques
Artificial Intelligence and Soft Computing: 18th International Conference …, 2019•Springer
This paper presents an application of the Zero-Order Takagi-Sugeno-Kang method to
explainable recommender systems. The method is based on the Wang-Mendel and the
Nozaki-Ishibuchi-Tanaka techniques for the generation of fuzzy rules, and it is best suited to
predict users' ratings. The model can be optimized using the Grey Wolf Optimizer without
affecting the interpretability. The performance of the methods has been shown using the
MovieLens 10M dataset.
explainable recommender systems. The method is based on the Wang-Mendel and the
Nozaki-Ishibuchi-Tanaka techniques for the generation of fuzzy rules, and it is best suited to
predict users' ratings. The model can be optimized using the Grey Wolf Optimizer without
affecting the interpretability. The performance of the methods has been shown using the
MovieLens 10M dataset.
Abstract
This paper presents an application of the Zero-Order Takagi-Sugeno-Kang method to explainable recommender systems. The method is based on the Wang-Mendel and the Nozaki-Ishibuchi-Tanaka techniques for the generation of fuzzy rules, and it is best suited to predict users’ ratings. The model can be optimized using the Grey Wolf Optimizer without affecting the interpretability. The performance of the methods has been shown using the MovieLens 10M dataset.
Springer
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