Explainable Cross-Domain Recommendations Through Relational Learning

Authors

  • Sirawit Sopchoke Osaka University
  • Ken-ichi Fukui The Institute of Scientific and Industrial Research, Osaka University
  • Masayuki Numao The Institute of Scientific and Industrial Research, Osaka University

Keywords:

explainable, cross-domain recommendations, rule generation

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.

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Published

2018-04-29

How to Cite

Sopchoke, S., Fukui, K.- ichi, & Numao, M. (2018). Explainable Cross-Domain Recommendations Through Relational Learning. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/12176