Measuring the Impact of Explanation Bias: A Study of Natural Language Justifications for Recommender Systems

Author:

Balog Krisztian1ORCID,Radlinski Filip2ORCID,Petrov Andrey2ORCID

Affiliation:

1. Google, Norway

2. Google, United Kingdom

Publisher

ACM

Reference34 articles.

1. Measuring Recommendation Explanation Quality

2. Transparent, Scrutable and Explainable User Models for Personalized Recommendation

3. Mustafa Bilgic and Raymond  J. Mooney . 2005 . Explaining Recommendations: Satisfaction vs . Promotion. In Proceedings of Beyond Personalization 2005: A Workshop on the Next Stage of Recommender Systems Research at the 2005 International Conference on Intelligent User Interfaces. 13–18 . Mustafa Bilgic and Raymond J. Mooney. 2005. Explaining Recommendations: Satisfaction vs. Promotion. In Proceedings of Beyond Personalization 2005: A Workshop on the Next Stage of Recommender Systems Research at the 2005 International Conference on Intelligent User Interfaces. 13–18.

4. Or Biran and Courtenay  V. Cotton . 2017 . Explanation and Justification in Machine Learning: A Survey. In IJCAI 2017 Workshop on Explainable Artificial Intelligence. Or Biran and Courtenay V. Cotton. 2017. Explanation and Justification in Machine Learning: A Survey. In IJCAI 2017 Workshop on Explainable Artificial Intelligence.

5. Crowd-Based Personalized Natural Language Explanations for Recommendations

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