Jointly Learning Explainable Rules for Recommendation with Knowledge Graph

Author:

Ma Weizhi1,Zhang Min1,Cao Yue2,Jin Woojeong2,Wang Chenyang1,Liu Yiqun1,Ma Shaoping1,Ren Xiang2

Affiliation:

1. Tsinghua University, China

2. University of Southern California, USA

Publisher

ACM Press

Reference49 articles.

1. Immanuel Bayer, Xiangnan He, Bhargav Kanagal, and Steffen Rendle. 2017. A generic coordinate descent framework for learning from implicit feedback. In Proceedings of the 26th International Conference on World Wide Web. International World Wide Web Conferences Steering Committee, 1341-1350.

2. Kurt Bollacker, Colin Evans, Praveen Paritosh, Tim Sturge, and Jamie Taylor. 2008. Freebase: a collaboratively created graph database for structuring human knowledge. In Proceedings of the 2008 ACM SIGMOD international conference on Management of data. AcM, 1247-1250.

3. Rose Catherine and William Cohen. 2016. Personalized recommendations using knowledge graphs: A probabilistic logic programming approach. In Proceedings of the 10th ACM Conference on Recommender Systems. ACM, 325-332.

4. Rose Catherine, Kathryn Mazaitis, Maxine Eskenazi, and William Cohen. 2017. Explainable entity-based recommendations with knowledge graphs. arXiv preprint arXiv:1707.05254(2017).

5. Weiyu Cheng, Yanyan Shen, Yanmin Zhu, and Linpeng Huang. 2018. DELF: A Dual-Embedding based Deep Latent Factor Model for Recommendation.. In IJCAI. 3329-3335.

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