Argumentation-Based Recommendations: Fantastic Explanations and How to Find Them
Abstract
A significant problem of recommender systems is their inability to explain recommendations, resulting in turn in ineffective feedback from users and the inability to adapt to users’ preferences. We propose a hybrid method for calculating predicted ratings, built upon an item/aspect-based graph with users’ partially given ratings, that can be naturally used to provide explanations for recommendations, extracted from user-tailored Tripolar Argumentation Frameworks (TFs). We show that our method can be understood as a gradual semantics for TFs, exhibiting a desirable, albeit weak, property of balance. We also show experimentally that our method is competitive in generating correct predictions, compared with state-of-the-art methods, and illustrate how users can interact with the generated explanations to improve quality of recommendations.
Cite
Text
Rago et al. "Argumentation-Based Recommendations: Fantastic Explanations and How to Find Them." International Joint Conference on Artificial Intelligence, 2018. doi:10.24963/IJCAI.2018/269Markdown
[Rago et al. "Argumentation-Based Recommendations: Fantastic Explanations and How to Find Them." International Joint Conference on Artificial Intelligence, 2018.](https://mlanthology.org/ijcai/2018/rago2018ijcai-argumentation/) doi:10.24963/IJCAI.2018/269BibTeX
@inproceedings{rago2018ijcai-argumentation,
title = {{Argumentation-Based Recommendations: Fantastic Explanations and How to Find Them}},
author = {Rago, Antonio and Cocarascu, Oana and Toni, Francesca},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2018},
pages = {1949-1955},
doi = {10.24963/IJCAI.2018/269},
url = {https://mlanthology.org/ijcai/2018/rago2018ijcai-argumentation/}
}