| A bi-step grounding paradigm for large language models in recommendation systems K Bao, J Zhang, W Wang, Y Zhang, Z Yang, Y Luo, F Feng, X He, Q Tian arXiv preprint arXiv:2308.08434, 2023 | 38 | 2023 |
| 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 | 17 | 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 | 15 | 2023 |
| 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 | 14 | 2023 |
| Generate What You Prefer: Reshaping Sequential Recommendation via Guided Diffusion Z Yang, J Wu, Z Wang, X Wang, Y Yuan, X He NeurIPS 2023, 2023 | 7 | 2023 |
| Model-enhanced Contrastive Reinforcement Learning for Sequential Recommendation C Li, Z Yang, J Zhang, J Wu, D Wang, X He, X Wang arXiv preprint arXiv:2310.16566, 2023 | | 2023 |
Xiangnan HeUniversity of Science and Technology of China, School of Data ScienceVerified email at ustc.edu.cn
