| SVD-GCN: A simplified graph convolution paradigm for recommendation S Peng, K Sugiyama, T Mine Proceedings of the 31st ACM international conference on information …, 2022 | 79 | 2022 |
| Less is more: Reweighting important spectral graph features for recommendation S Peng, K Sugiyama, T Mine Proceedings of the 45th International ACM SIGIR Conference on Research and …, 2022 | 44 | 2022 |
| A robust hierarchical graph convolutional network model for collaborative filtering S Peng, T Mine arXiv preprint arXiv:2004.14734, 2020 | 11 | 2020 |
| Less is More: Removing Redundancy of Graph Convolutional Networks for Recommendation S Peng, K Sugiyama, T Mine ACM Transactions on Information Systems 42 (3), 1-26, 2024 | 6 | 2024 |
| Less is more S Peng, K Sugiyama, T Mine Proceedings of the 45th International ACM SIGIR Conference on Research and …, 2022 | 5 | 2022 |
| How powerful is graph filtering for recommendation S Peng, X Liu, K Sugiyama, T Mine Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and …, 2024 | 2 | 2024 |
| Vector representation based model considering randomness of user mobility for predicting potential users S Peng, X Xie, T Mine, C Su PRIMA 2018: Principles and Practice of Multi-Agent Systems: 21st …, 2018 | 2 | 2018 |
| Balancing Embedding Spectrum for Recommendation S Peng, K Sugiyama, X Liu, T Mine arXiv preprint arXiv:2406.12032, 2024 | | 2024 |
| Towards Effective and Efficient Personalized Recommendation from a Spectral Perspective S Peng Kyoto University, 2024 | | 2024 |
| SVD-GCN S Peng, K Sugiyama, T Mine Proceedings of the 31st ACM International Conference on Information …, 2022 | | 2022 |
| Mixture-preference bayesian matrix factorization for implicit feedback datasets S Peng, T Mine Proceedings of the 35th Annual ACM Symposium on Applied Computing, 1427-1434, 2020 | | 2020 |
| TSWNN+: Check-in Prediction Based on Deep Learning and Factorization Machine C Su, N Liu, X Xie, S Peng Big Data Innovations and Applications: 5th International Conference …, 2019 | | 2019 |
