| How Can Recommender Systems Benefit from Large Language Models: A Survey J Lin, X Dai, Y Xi, W Liu, B Chen, H Zhang, Y Liu, C Wu, X Li, C Zhu, ... arXiv preprint arXiv:2306.05817, 2023 | 100* | 2023 |
| Towards open-world recommendation with knowledge augmentation from large language models Y Xi, W Liu, J Lin, J Zhu, B Chen, R Tang, W Zhang, R Zhang, Y Yu arXiv preprint arXiv:2306.10933, 2023 | 67 | 2023 |
| ReLLa: Retrieval-enhanced Large Language Models for Lifelong Sequential Behavior Comprehension in Recommendation J Lin, R Shan, C Zhu, K Du, B Chen, S Quan, R Tang, Y Yu, W Zhang Proceedings of the ACM on Web Conference 2024, 3497–3508, 2024 | 37* | 2024 |
| 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 | 33 | 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 |
| 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 | 17 | 2023 |
| 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 | 17 | 2023 |
| ClickPrompt: CTR Models are Strong Prompt Generators for Adapting Language Models to CTR Prediction J Lin, B Chen, H Wang, Y Xi, Y Qu, X Dai, K Zhang, R Tang, Y Yu, ... Proceedings of the Web Conference 2024, 3319–3330, 2024 | 12* | 2024 |
| An f-shape click model for information retrieval on multi-block mobile pages L Fu*, J Lin*, W Liu, R Tang, W Zhang, R Zhang, Y Yu Proceedings of the Sixteenth ACM International Conference on Web Search and …, 2023 | 12 | 2023 |
| Mamba4rec: Towards efficient sequential recommendation with selective state space models C Liu, J Lin, J Wang, H Liu, J Caverlee arXiv preprint arXiv:2403.03900, 2024 | 10 | 2024 |
| Learning ball-balancing robot through deep reinforcement learning Y Zhou, J Lin, S Wang, C Zhang 2021 International Conference on Computer, Control and Robotics (ICCCR), 1-8, 2021 | 10 | 2021 |
| Codeapex: A bilingual programming evaluation benchmark for large language models L Fu, H Chai, S Luo, K Du, W Zhang, L Fan, J Lei, R Rui, J Lin, Y Fang, ... arXiv preprint arXiv:2309.01940, 2023 | 5 | 2023 |
| Towards Efficient and Effective Unlearning of Large Language Models for Recommendation H Wang*, J Lin*, B Chen, Y Yang, R Tang, W Zhang, Y Yu arXiv preprint arXiv:2403.03536, 2024 | 4 | 2024 |
| FLIP: Towards Fine-grained Alignment between ID-based Models and Pretrained Language Models for CTR Prediction H Wang*, J Lin*, X Li, B Chen, C Zhu, R Tang, W Zhang, Y Yu arXiv preprint arXiv:2310.19453, 2023 | 3* | 2023 |
| SINKT: A Structure-Aware Inductive Knowledge Tracing Model with Large Language Model L Fu, H Guan, K Du, J Lin, W Xia, W Zhang, R Tang, Y Wang, Y Yu arXiv preprint arXiv:2407.01245, 2024 | | 2024 |
| ELCoRec: Enhance Language Understanding with Co-Propagation of Numerical and Categorical Features for Recommendation J Chen, K Du, J Lin, B Chen, R Tang, W Zhang arXiv preprint arXiv:2406.18825, 2024 | | 2024 |
| Large Language Models Make Sample-Efficient Recommender Systems J Lin, X Dai, R Shan, B Chen, R Tang, Y Yu, W Zhang arXiv preprint arXiv:2406.02368, 2024 | | 2024 |
| DisCo: Towards Harmonious Disentanglement and Collaboration between Tabular and Semantic Space for Recommendation K Du, J Chen, J Lin, Y Xi, H Wang, X Dai, B Chen, R Tang, W Zhang arXiv preprint arXiv:2406.00011, 2024 | | 2024 |
| Extracting Essential and Disentangled Knowledge for Recommendation Enhancement K Du, J Chen, J Lin, M Zhu, B Chen, S Li, R Tang arXiv preprint arXiv:2406.00012, 2024 | | 2024 |
| M-scan: A Multi-Scenario Causal-driven Adaptive Network for Recommendation J Zhu, Y Wang, J Lin, J Qin, R Tang, W Zhang, Y Yu Proceedings of the ACM on Web Conference 2024, 3844-3853, 2024 | | 2024 |
Ruiming TangHuawei在 huawei.com 的电子邮件经过验证
