Proceedings

Cross-Domain Collaborative Filtering with Review Text

Xin Xin, Zhirun Liu, Chin-Yew Lin, Heyan Huang, Xiaochi Wei, Ping Guo

Download BibTeX Cite

Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence. Main Track. Pages 1827-1833.

Abstract

Most existing cross-domain recommendation algorithms focus on modeling ratings, while ignoring review texts. The review text, however, contains rich information, which can be utilized to alleviate data sparsity limitations, and interpret transfer patterns. In this paper, we investigate how to utilize the review text to improve cross-domain collaborative filtering models. The challenge lies in the existence of non-linear properties in some transfer patterns. Given this, we extend previous transfer learning models in collaborative filtering, from linear mapping functions to non-linear ones, and propose a cross-domain recommendation framework with the review text incorporated. Experimental verifications have demonstrated, for new users with sparse feedback, utilizing the review text obtains 10% improvement in the AUC metric, and the nonlinear method outperforms the linear ones by 4%.

Xin Xin, Zhirun Liu, Chin-Yew Lin, Heyan Huang, Xiaochi Wei, and Ping Guo. 2015. Cross-Domain Collaborative Filtering with Review Text. In Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-15. IJCAI, 1827–1833. https://ijcai.org/proceedings/2015/260
Welcome Future Conferences AI Journal Division IJCAI Proceedings About IJCAI IJCAI Awards