| Neural graph collaborative filtering X Wang, X He, M Wang, F Feng, TS Chua Proceedings of the 42nd international ACM SIGIR conference on Research and …, 2019 | 3404 | 2019 |
| 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 | 1249 | 2021 |
| Bias and debias in recommender system: A survey and future directions J Chen, H Dong, X Wang, F Feng, M Wang, X He ACM Transactions on Information Systems 41 (3), 1-39, 2023 | 934 | 2023 |
| Temporal relational ranking for stock prediction F Feng, X He, X Wang, C Luo, Y Liu, TS Chua ACM Transactions on Information Systems (TOIS) 37 (2), 1-30, 2019 | 443 | 2019 |
| Depression detection via harvesting social media: A multimodal dictionary learning solution. G Shen, J Jia, L Nie, F Feng, C Zhang, T Hu, TS Chua, W Zhu IJCAI, 3838-3844, 2017 | 432 | 2017 |
| Causal intervention for leveraging popularity bias in recommendation Y Zhang, F Feng, X He, T Wei, C Song, G Ling, Y Zhang Proceedings of the 44th international ACM SIGIR conference on research and …, 2021 | 428 | 2021 |
| Tallrec: An effective and efficient tuning framework to align large language model with recommendation K Bao, J Zhang, Y Zhang, W Wang, F Feng, X He Proceedings of the 17th ACM Conference on Recommender Systems, 1007-1014, 2023 | 352 | 2023 |
| Model-agnostic counterfactual reasoning for eliminating popularity bias in recommender system T Wei, F Feng, J Chen, Z Wu, J Yi, X He Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data …, 2021 | 309 | 2021 |
| Enhancing stock movement prediction with adversarial training F Feng, H Chen, X He, J Ding, M Sun, TS Chua arXiv preprint arXiv:1810.09936, 2018 | 293 | 2018 |
| Tem: Tree-enhanced embedding model for explainable recommendation X Wang, X He, F Feng, L Nie, TS Chua Proceedings of the 2018 world wide web conference, 1543-1552, 2018 | 280 | 2018 |
| Graph adversarial training: Dynamically regularizing based on graph structure F Feng, X He, J Tang, TS Chua IEEE Transactions on Knowledge and Data Engineering 33 (6), 2493-2504, 2019 | 253 | 2019 |
| Denoising implicit feedback for recommendation W Wang, F Feng, X He, L Nie, TS Chua Proceedings of the 14th ACM international conference on web search and data …, 2021 | 247 | 2021 |
| TAT-QA: A question answering benchmark on a hybrid of tabular and textual content in finance F Zhu, W Lei, Y Huang, C Wang, S Zhang, J Lv, F Feng, TS Chua arXiv preprint arXiv:2105.07624, 2021 | 241 | 2021 |
| Neural multi-task recommendation from multi-behavior data C Gao, X He, D Gan, X Chen, F Feng, Y Li, TS Chua, D Jin 2019 IEEE 35th international conference on data engineering (ICDE), 1554-1557, 2019 | 235 | 2019 |
| Neurostylist: Neural compatibility modeling for clothing matching X Song, F Feng, J Liu, Z Li, L Nie, J Ma Proceedings of the 25th ACM international conference on Multimedia, 753-761, 2017 | 201 | 2017 |
| Deconfounded video moment retrieval with causal intervention X Yang, F Feng, W Ji, M Wang, TS Chua Proceedings of the 44th international ACM SIGIR conference on research and …, 2021 | 195 | 2021 |
| Clicks can be cheating: Counterfactual recommendation for mitigating clickbait issue W Wang, F Feng, X He, H Zhang, TS Chua Proceedings of the 44th International ACM SIGIR Conference on Research and …, 2021 | 187 | 2021 |
| Deconfounded recommendation for alleviating bias amplification W Wang, F Feng, X He, X Wang, TS Chua Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data …, 2021 | 179 | 2021 |
| Is chatgpt fair for recommendation? evaluating fairness in large language model recommendation J Zhang, K Bao, Y Zhang, W Wang, F Feng, X He Proceedings of the 17th ACM Conference on Recommender Systems, 993-999, 2023 | 165 | 2023 |
| Neural compatibility modeling with attentive knowledge distillation X Song, F Feng, X Han, X Yang, W Liu, L Nie The 41st International ACM SIGIR conference on research & development in …, 2018 | 153 | 2018 |
Xiangnan HeUniversity of Science and Technology of China, School of Data Science在 ustc.edu.cn 的电子邮件经过验证