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Towards a knowledge based Explainable Recommender Systems
38
Zitationen
3
Autoren
2019
Jahr
Abstract
Most current Machine Learning based recommender systems act like black boxes, not offering the user any insight into the system logic or justification for the recommendations. Thus, risking losing trust with users and failing to achieve acceptance. The goal of this work is to improve the explainability of recommender systems by using a knowledge extraction method.
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