Explainable Cross-Domain Recommendations Through Relational Learning
Abstract
We propose a method to generate explainable recommendation rules on cross-domain problems. Our two main contributions are: i) using relational learning to generate the rules which are able to explain clearly why the items were recommended to the particular user, ii) using the user's preferences of items on different domains and item attributes to generate novel or unexpected recommendations for the user. To illustrate that our method is indeed feasible and applicable, we conducted experiments on music and movie domains.
Cite
Text
Sopchoke et al. "Explainable Cross-Domain Recommendations Through Relational Learning." AAAI Conference on Artificial Intelligence, 2018. doi:10.1609/AAAI.V32I1.12176Markdown
[Sopchoke et al. "Explainable Cross-Domain Recommendations Through Relational Learning." AAAI Conference on Artificial Intelligence, 2018.](https://mlanthology.org/aaai/2018/sopchoke2018aaai-explainable/) doi:10.1609/AAAI.V32I1.12176BibTeX
@inproceedings{sopchoke2018aaai-explainable,
title = {{Explainable Cross-Domain Recommendations Through Relational Learning}},
author = {Sopchoke, Sirawit and Fukui, Ken-ichi and Numao, Masayuki},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2018},
pages = {8159-8160},
doi = {10.1609/AAAI.V32I1.12176},
url = {https://mlanthology.org/aaai/2018/sopchoke2018aaai-explainable/}
}