| Self-supervised graph learning for recommendation J Wu, X Wang, F Feng, X He, L Chen, J Lian, X Xie Proceedings of the 44th international ACM SIGIR conference on research and …, 2021 | 751 | 2021 |
| Causal attention for interpretable and generalizable graph classification Y Sui, X Wang, J Wu, M Lin, X He, TS Chua Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and …, 2022 | 87 | 2022 |
| Graph convolution machine for context-aware recommender system J Wu, X He, X Wang, Q Wang, W Chen, J Lian, X Xie Frontiers of Computer Science 16 (6), 166614, 2022 | 60 | 2022 |
| On the effectiveness of sampled softmax loss for item recommendation J Wu, X Wang, X Gao, J Chen, H Fu, T Qiu ACM Transactions on Information Systems 42 (4), 1-26, 2024 | 38 | 2024 |
| Cross pairwise ranking for unbiased item recommendation Q Wan, X He, X Wang, J Wu, W Guo, R Tang Proceedings of the ACM Web Conference 2022, 2370-2378, 2022 | 26 | 2022 |
| Large language model can interpret latent space of sequential recommender Z Yang, J Wu, Y Luo, J Zhang, Y Yuan, A Zhang, X Wang, X He arXiv preprint arXiv:2310.20487, 2023 | 16 | 2023 |
| Gif: A general graph unlearning strategy via influence function J Wu, Y Yang, Y Qian, Y Sui, X Wang, X He Proceedings of the ACM Web Conference 2023, 651-661, 2023 | 14 | 2023 |
| A generic learning framework for sequential recommendation with distribution shifts Z Yang, X He, J Zhang, J Wu, X Xin, J Chen, X Wang Proceedings of the 46th International ACM SIGIR Conference on Research and …, 2023 | 12 | 2023 |
| Adap-τ: Adaptively modulating embedding magnitude for recommendation J Chen, J Wu, J Wu, X Cao, S Zhou, X He Proceedings of the ACM Web Conference 2023, 1085-1096, 2023 | 11 | 2023 |
| Unleashing the power of graph data augmentation on covariate distribution shift Y Sui, Q Wu, J Wu, Q Cui, L Li, J Zhou, X Wang, X He Advances in Neural Information Processing Systems 36, 2024 | 9 | 2024 |
| Llara: Aligning large language models with sequential recommenders J Liao, S Li, Z Yang, J Wu, Y Yuan, X Wang, X He arXiv preprint arXiv:2312.02445, 2023 | 9 | 2023 |
| Adversarial causal augmentation for graph covariate shift Y Sui, X Wang, J Wu, A Zhang, X He arXiv preprint arXiv:2211.02843, 2022 | 9 | 2022 |
| Knowledge-enhanced causal reinforcement learning model for interactive recommendation W Nie, X Wen, J Liu, J Chen, J Wu, G Jin, J Lu, AA Liu IEEE Transactions on Multimedia, 2023 | 8 | 2023 |
| Recommendation unlearning via influence function Y Zhang, Z Hu, Y Bai, F Feng, J Wu, Q Wang, X He arXiv preprint arXiv:2307.02147, 2023 | 7 | 2023 |
| How graph convolutions amplify popularity bias for recommendation? J Chen, J Wu, J Chen, X Xin, Y Li, X He Frontiers of Computer Science 18 (5), 185603, 2024 | 6 | 2024 |
| Understanding contrastive learning via distributionally robust optimization J Wu, J Chen, J Wu, W Shi, X Wang, X He Advances in Neural Information Processing Systems 36, 2024 | 6 | 2024 |
| Query and response augmentation cannot help out-of-domain math reasoning generalization C Li, Z Yuan, G Dong, K Lu, J Wu, C Tan, X Wang, C Zhou arXiv preprint arXiv:2310.05506, 2023 | 6 | 2023 |
| Generate What You Prefer: Reshaping Sequential Recommendation via Guided Diffusion Z Yang, J Wu, Z Wang, X Wang, Y Yuan, X He Advances in Neural Information Processing Systems 36, 2024 | 3 | 2024 |
| Deconfounded training for graph neural networks Y Sui, X Wang, J Wu, X He, TS Chua arXiv preprint arXiv:2112.15089, 2021 | 3 | 2021 |
| Dynamic Sparse Learning: A Novel Paradigm for Efficient Recommendation S Wang, Y Sui, J Wu, Z Zheng, H Xiong arXiv preprint arXiv:2402.02855, 2024 | 1 | 2024 |
Xiangnan HeUniversity of Science and Technology of China, School of Data ScienceVerified email at ustc.edu.cn
