Dual-Process Graph Neural Network for Diversified Recommendation
Author:
Affiliation:
1. Peking University, Beijing, China
2. Northwestern Polytechnical University, Xi'an, China
3. Huawei Noah's Ark Lab, Markham, Canada
4. Huawei Noah's Ark Lab, Beijing, China
Funder
This work was supported by the National Natural Science Foundation of China (Grant No.62276006).
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3583780.3614853
Reference51 articles.
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2. Rademacher and Gaussian complexities: Risk bounds and structural results;Bartlett Peter L;Journal of Machine Learning Research,2002
3. Rianne van den Berg , Thomas N Kipf , and Max Welling . 2017. Graph convolutional matrix completion. arXiv preprint arXiv:1706.02263 ( 2017 ). Rianne van den Berg, Thomas N Kipf, and Max Welling. 2017. Graph convolutional matrix completion. arXiv preprint arXiv:1706.02263 (2017).
4. Daniel E Berlyne . 1960. Conflict , arousal, and curiosity . McGraw-Hill Book Company . Daniel E Berlyne. 1960. Conflict, arousal, and curiosity. McGraw-Hill Book Company.
5. Derek Bridge and John Paul Kelly. 2006. Ways of Computing Diverse Collaborative Recommendations. In Adaptive Hypermedia and Adaptive Web-Based Systems Vincent P. Wade Helen Ashman and Barry Smyth (Eds.). Springer Berlin Heidelberg 41--50. Derek Bridge and John Paul Kelly. 2006. Ways of Computing Diverse Collaborative Recommendations. In Adaptive Hypermedia and Adaptive Web-Based Systems Vincent P. Wade Helen Ashman and Barry Smyth (Eds.). Springer Berlin Heidelberg 41--50.
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