| Heterogeneous similarity graph neural network on electronic health records Z Liu, X Li, H Peng, L He, SY Philip 2020 IEEE international conference on big data (big data), 1196-1205, 2020 | 67 | 2020 |
| Dynamic Graph Collaborative Filtering X Li, M Zhang, S Wu, Z Liu, L Wang, SY Philip 2020 IEEE international conference on data mining (ICDM), 322-331, 2020 | 64 | 2020 |
| Blood pressure prediction via recurrent models with contextual layer X Li, S Wu, L Wang Proceedings of the 26th International Conference on World Wide Web, 685-693, 2017 | 58 | 2017 |
| Basket recommendation with multi-intent translation graph neural network Z Liu, X Li, Z Fan, S Guo, K Achan, SY Philip 2020 IEEE International Conference on Big Data (Big Data), 728-737, 2020 | 53 | 2020 |
| Pre-training recommender systems via reinforced attentive multi-relational graph neural network X Li, Z Liu, S Guo, Z Liu, H Peng, SY Philip, K Achan 2021 IEEE International Conference on Big Data (Big Data), 457-468, 2021 | 16 | 2021 |
| Medical triage chatbot diagnosis improvement via multi-relational hyperbolic graph neural network Z Liu, X Li, Z You, T Yang, W Fan, P Yu Proceedings of the 44th International ACM SIGIR conference on research and …, 2021 | 15 | 2021 |
| Mitigating health disparities in ehr via deconfounder Z Liu, X Li, P Yu Proceedings of the 13th ACM International Conference on Bioinformatics …, 2022 | 10 | 2022 |
| Time-aware Hyperbolic Graph Attention Network for Session-based Recommendation X Li, Y Liu, Z Liu, PS Yu 2022 IEEE International Conference on Big Data (Big Data), 626-635, 2023 | 8 | 2023 |
| Knowledge graph completion models are few-shot learners: An empirical study of relation labeling in e-commerce with llms J Chen, L Ma, X Li, N Thakurdesai, J Xu, JHD Cho, K Nag, E Korpeoglu, ... arXiv preprint arXiv:2305.09858, 2023 | 7 | 2023 |
| Mitigating Frequency Bias in Next-Basket Recommendation via Deconfounders X Li, Z Liu, L Ma, K Nag, S Guo, P Yu, K Achan 2022 IEEE International Conference on Big Data (Big Data), 616-625, 2022 | 2 | 2022 |
| LLM-Ensemble: Optimal Large Language Model Ensemble Method for E-commerce Product Attribute Value Extraction C Fang, X Li, Z Fan, J Xu, K Nag, E Korpeoglu, S Kumar, K Achan SIGIR 2024, 2024 | 1 | 2024 |
| Group-Aware Interest Disentangled Dual-Training for Personalized Recommendation X Liu, L Yang, Z Liu, X Li, M Yang, C Wang, SY Philip 2023 IEEE International Conference on Big Data (BigData), 393-402, 2023 | 1 | 2023 |
| A Counterfactual Fair Model for Longitudinal Electronic Health Records via Deconfounder Z Liu, X Li, SY Philip 2023 IEEE International Conference on Data Mining (ICDM), 1175-1180, 2023 | 1 | 2023 |
| Graph Learning in Recommender Systems: Toward Structures and Causality X Li University of Illinois at Chicago, 2023 | 1 | 2023 |
| Prompt Optimizer of Text-to-Image Diffusion Models for Abstract Concept Understanding Z Fan, X Li, C Fang, T Biswas, K Nag, J Xu, K Achan Companion Proceedings of the ACM Web Conference 2024, 2024 | | 2024 |
Philip S. YuProfessor of Computer Science, University of Illinons at Chicagocs.uic.edu의 이메일 확인됨
