Semi-supervised deep embedded clustering Y Ren, K Hu, X Dai, L Pan, SCH Hoi, Z Xu Neurocomputing 325, 121-130, 2019 | 177 | 2019 |
Interactive recommender system via knowledge graph-enhanced reinforcement learning S Zhou, X Dai, H Chen, W Zhang, K Ren, R Tang, X He, Y Yu Proceedings of the 43rd international ACM SIGIR conference on research and …, 2020 | 156 | 2020 |
Large-scale interactive recommendation with tree-structured policy gradient H Chen, X Dai, H Cai, W Zhang, X Wang, R Tang, Y Zhang, Y Yu Proceedings of the AAAI conference on artificial intelligence 33 (01), 3312-3320, 2019 | 143 | 2019 |
Yunjia Xi, Weiwen Liu, Bo Chen, Xiangyang Li, Chenxu Zhu, Huifeng Guo, Yong Yu, Ruiming Tang, et al. 2023. How Can Recommender Systems Benefit from Large Language Models: A Survey J Lin, X Dai arXiv preprint arXiv:2306.05817, 2023 | 36 | 2023 |
A graph-enhanced click model for web search J Lin, W Liu, X Dai, W Zhang, S Li, R Tang, X He, J Hao, Y Yu Proceedings of the 44th international ACM SIGIR conference on research and …, 2021 | 32 | 2021 |
An adversarial imitation click model for information retrieval X Dai, J Lin, W Zhang, S Li, W Liu, R Tang, X He, J Hao, J Wang, Y Yu Proceedings of the Web Conference 2021, 1809-1820, 2021 | 29 | 2021 |
How can recommender systems benefit from large language models: A survey J Lin, X Dai, Y Xi, W Liu, B Chen, X Li, C Zhu, H Guo, Y Yu, R Tang, ... arXiv preprint arXiv:2306.05817, 2023 | 20 | 2023 |
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A bird's-eye view of reranking: from list level to page level Y Xi, J Lin, W Liu, X Dai, W Zhang, R Zhang, R Tang, Y Yu Proceedings of the Sixteenth ACM International Conference on Web Search and …, 2023 | 15 | 2023 |
Multi-level interaction reranking with user behavior history Y Xi, W Liu, J Zhu, X Zhao, X Dai, R Tang, W Zhang, R Zhang, Y Yu Proceedings of the 45th International ACM SIGIR Conference on Research and …, 2022 | 12 | 2022 |
Towards return parity in markov decision processes J Chi, J Shen, X Dai, W Zhang, Y Tian, H Zhao arXiv preprint arXiv:2111.10476, 2021 | 12 | 2021 |
Context-aware reranking with utility maximization for recommendation Y Xi, W Liu, X Dai, R Tang, W Zhang, Q Liu, X He, Y Yu arXiv preprint arXiv:2110.09059, 2021 | 12 | 2021 |
MAP: A Model-agnostic Pretraining Framework for Click-through Rate Prediction J Lin, Y Qu, W Guo, X Dai, R Tang, Y Yu, W Zhang Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and …, 2023 | 11 | 2023 |
Yunjia Xi, Weiwen Liu, Bo Chen, Xiangyang Li, Chenxu Zhu, Huifeng Guo, Yong Yu, Ruiming Tang, et al. How can recommender systems benefit from large language models: A survey J Lin, X Dai arXiv preprint arXiv:2306.05817, 2023 | 9 | 2023 |
U-rank: Utility-oriented learning to rank with implicit feedback X Dai, J Hou, Q Liu, Y Xi, R Tang, W Zhang, X He, J Wang, Y Yu Proceedings of the 29th ACM international conference on information …, 2020 | 9 | 2020 |
Yunjia Xi, Ruiming Tang, Weinan Zhang, Xiuqiang He, Jun Wang, and Yong Yu. 2020. U-rank: Utility-oriented Learning to Rank with Implicit Feedback X Dai, J Hou, Q Liu Proceedings of the 29th ACM International Conference on Information …, 0 | 8 | |
Deep landscape forecasting in multi-slot real-time bidding W Ou, B Chen, Y Yang, X Dai, W Liu, W Zhang, R Tang, Y Yu Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and …, 2023 | 3 | 2023 |
Beyond relevance ranking: a general graph matching framework for utility-oriented learning to rank X Dai, Y Xi, W Zhang, Q Liu, R Tang, X He, J Hou, J Wang, Y Yu ACM Transactions on Information Systems (TOIS) 40 (2), 1-29, 2021 | 3 | 2021 |
Tired of Plugins? Large Language Models Can Be End-To-End Recommenders W Zhang, X Li, Y Wang, K Dong, Y Wang, X Dai, X Zhao, H Guo, R Tang arXiv preprint arXiv:2404.00702, 2024 | 1 | 2024 |
Understanding or Manipulation: Rethinking Online Performance Gains of Modern Recommender Systems Z Zhu, R Qin, J Huang, X Dai, Y Yu, Y Yu, W Zhang ACM Transactions on Information Systems 42 (4), 1-32, 2024 | 1 | 2024 |