conference paper
Towards a knowledge based Explainable Recommender Systems
Amina Samiha, Amina Adadia, Mohammed Berradab
- aComputer and Interdisciplinary, Physics Laboratory (LIPI), Sidi Mohammed Ben Abdellah University, fez morocco
- bComputer and Interdisciplinary Physics Laboratory (LIPI), Sidi Mohammed Ben Abdellah University, fez morocco
International Conference on Big Data and Internet of Things (BDIOT)2019-10-23 39 citations16 references
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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.
TL;DRThe goal of this work is to improve the explainability of recommender systems by using a knowledge extraction method. (Semantic Scholar)