An end-to-end neighborhood-based interaction model for knowledge-enhanced recommendation
Author:
Affiliation:
1. Shanghai Jiao Tong University
2. Beijing University of Posts and Telecommunications and Renmin University of China
3. Université de Montréal
4. Mila-Quebec Institute for Learning Algorithms and HEC Montréal and CIFAR AI Research Chair
Funder
National Natural ScienceFoundation of China
NaturalSciences and Engineering Research Council of Canada
Canada CIFAR AI Chair Program
Shanghai Sailing Program
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3326937.3341257
Reference32 articles.
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2. Jie Chen Tengfei Ma and Cao Xiao. 2018. FastGCN: fast learning with graph convolutional networks via importance sampling. arXiv preprint arXiv:1801.10247 (2018). Jie Chen Tengfei Ma and Cao Xiao. 2018. FastGCN: fast learning with graph convolutional networks via importance sampling. arXiv preprint arXiv:1801.10247 (2018).
3. Wide & Deep Learning for Recommender Systems
4. Hanjun Dai Yichen Wang Rakshit Trivedi and Le Song. 2016. Deep coevolutionary network: Embedding user and item features for recommendation. arXiv preprint arXiv:1609.03675 (2016). Hanjun Dai Yichen Wang Rakshit Trivedi and Le Song. 2016. Deep coevolutionary network: Embedding user and item features for recommendation. arXiv preprint arXiv:1609.03675 (2016).
5. Aditya Grover and Jure Leskovec. 2016. node2vec: Scalable feature learning for networks. In SIGKDD. ACM. Aditya Grover and Jure Leskovec. 2016. node2vec: Scalable feature learning for networks. In SIGKDD. ACM.
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