》 Research Fields
Recommender Systems, Machine Learning, Deep Learning, Data Science, Privacy-Preserving Computation, Interdisciplinary Intelligent Technology
》 Courses
Machine Learning; Mathematical Modeling; Frontiers in Computer Technology
》 Projects
1. PI, A Research on Co-evolutionary Cross-domain Recommender Systems via Multimodal Lifelong Learning, NSFC (62276190), 2023.1-2026.12
2. PI, A Research on Data Privacy Protection and Federated Learning for Crowdsourcing Mapping, SAITDF (2208), 2022.8-2023.8
》 Publications
1. Tangwei Ye, Liang Hu, Qi Zhang, Usman Naseem, Dora D.S Liu, Zhong Yuan Lai: Show Me The Best Outfit for A Certain Scene: A Scene-aware Fashion Recommender System, WWW 2023 (CCF A)
2. L. Hu, D. D. Liu, Q. Zhang, U. Naseem, Z. Y. Lai: Self-supervised Learning for Multilevel Skeleton-based Forgery Detection via Temporal-Causal Consistency of Actions, AAAI-2023 (CCF A)
3. D. D. Liu, L. Hu, Q. Zhang, U. Naseem, Z. Y. Lai: A Dynamics and Task Decoupled Reinforcement Learning Architecture for High-efficiency Dynamic Target Intercept, AAAI-2023 (CCF A)
4. Y. Jin, G. Hu, H. Chen, D. Miao, L. Hu, C. Zhao: Cross-Modal Distillation for Speaker Recognition, AAAI-2023 (CCF A)
5. Z Dai, J Yi, L Yan, Q Xu, L Hu, Q Zhang, J Li, G Wang: PFEMed: Few-shot medical image classification using prior guided feature enhancement. Pattern Recognition 134, 109108 (2023) (CCF B)
6. Y. Sui, S. Feng, H. Zhang, J. Cao, L. Hu, N. Zhu: Causality-aware Enhanced Model for Multi-hop Question Answering over Knowledge Graphs. Knowl. Based Syst. 250: 108943 (2022) (CCF B)
7. Q. Zhang, L. Hu, L. Cao, C. Shi, S. Wang, D. D. Liu: A Probabilistic Code Balance Constraint with Compactness and Informativeness Enhancement for Deep Supervised Hashing. IJCAI 2022: 1651-1657 (CCF A)
8. S. Wang, Q. Zhang, L. Hu, X. Zhang, Y. Wang, C. Aggarwal: Sequential/Session-based Recommendations: Challenges, Approaches, Applications and Opportunities. SIGIR 2022: 3425-3428 (CCF A)
9. X Jiang, Q Zhang, C Shi, K Jiang, L Hu, S Wang: An Ion Exchange Mechanism Inspired Story Ending Generator for Different Characters. ECML/PKDD 2022 (CCF B)
10. Wang, S., Hu, L., Wang, Y., He X., Sheng, Q.Z., Orgun, M., Cao, L, F. Rocci, and P. S. Yu. Graph Learning based Recommender Systems: A Review. In Proceedings of the 30th International Joint Conference on Artificial Intelligence , 2021. (CCF A)
11. Zhang, Q., Cao L., Shi C., Hu L. Tripartite Collaborative Filtering with Observability and Selection for Debiasing Rating Estimation on Missing-Not-at-Random Data. In AAAI 2021, 4671-4678 (CCF A)
12. Wang, S., Hu, L., Wang, Y., Sheng, Q.Z., Orgun, M., and Cao, L. Intention2Basket: A Neural Intention-driven Approach for Dynamic Next-basket Planning. In Proceedings of the 29th International Joint Conference on Artificial Intelligence , 2020. (CCF A)
13. Wang, S., Hu, L., Wang, Y., Sheng, Q.Z., Orgun, M., and Cao, L. Intention Nets: Psychology-Inspired User Choice Behavior. Modeling for Next-Basket Prediction. In Proceedings of the 34th AAAI Conference on Artificial Intelligence, 2020. (CCF A)
14. Wang, S., Cao, L, Hu, L Berkovsky, S , Huang, X , Xiao, L , Lu. W. Jointly Modeling Intra-and Inter-transaction Dependencies with Hierarchical Attentive Transaction Embeddings for Next-item Recommendation. IEEE Intelligent Systems, (2020)
15. Jian, S., Hu, L., Cao, L., and Lu, K. Representation Learning with Multiple Lipschitz-constrained Alignments on Partially-labeled Cross-domain Data. In Proceedings of the 34th AAAI Conference on Artificial Intelligence, 2020. (CCF A)
16. Hu, L., Jian, S., Cao, L., Gu, Z., Chen, Q., Amirbekyan, A.: HERS: Modeling Influential Contexts with Heterogeneous Relations for Sparse and Cold-start Recommendation. In AAAI-19, 2019 (CCF A)
17. Hu, L., Chen, Q., Cao, L., Jian, S., Zhao, H., and Cao, J. Evolving Coauthorship Modeling and Prediction via Time-Aware Paired Choice Analysis. IEEE Access 7, 98639-98651, 2019.
18. Wang, S., Hu, L., Wang, Y., Sheng, Q.Z., Orgun, M.A., and Cao, L. Modeling Multi-Purpose Sessions for Next-Item Recommendations via Mixture-Channel Purpose Routing Networks. In Proceedings of the 28th International Joint Conference on Artificial Intelligence, 3771-3777, 2019. (CCF A)
19. Wang, S., Hu, L., Wang, Y., Cao, L., Sheng, Q.Z., and Orgun, M. Sequential Recommender Systems: Challenges, Progress and Prospects. In Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 6332-6338, 2019. (CCF A)
20. Jian, S., Hu, L., Cao, L., Lu, K., Gao, H.: Evolutionarily Learning Multi-aspect Interactions and Influences from Network Structure and Node Content. In AAAI-19, 2019. (CCF A)
21. Hu, L., Chen, Q., Zhao, H., Jian, S., Cao, L., Cao, J.: Neural Cross-Session Filtering: Next-Item Prediction Under Intra- and Inter-Session Context. IEEE Intelligent Systems 33, 57-67 (2018)
22. Hu, L., Jian S., Cao L., and Chen C.. Interpretable Recommendation via Attraction Modeling: Learning Multilevel Attractiveness over Multimodal Movie Contents, In IJCAI 2018, 2018 (CCF A)
23. Jian, S., Hu, L., Cao, L., Lu, K.: Metric-Based Auto-Instructor for Learning Mixed Data Representation. In AAAI-18, 2018. (CCF A)
24. Yang, D., Guo, J., Wang, Z.-J., Wang, Y., Zhang, J., Hu, L., Yin, J., and Cao, J. FastPM: An approach to pattern matching via distributed stream processing. Information Sciences 453, 263-280, 2018. (CCF B)
25. Wang, S., Hu, L., Cao, L., Huang, X., Lian, D., Liu, W.: Attention-based Transactional Context Embedding for Next-Item Recommendation. In Proceedings of Thirty-Second AAAI Conference on Artificial Intelligence, 2018. (CCF A)
26. Hu, L., Cao, L., Wang, S., Xu, G., Cao, J., and Gu, Z. Diversifying Personalized Recommendation with User-session Context. In Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, IJCAI-17, 1858--1864, 2017. (CCF A)
27. Hu, L., Cao, L., Cao, J., Gu, Z., Xu, G., and Wang, J. Improving the Quality of Recommendations for Users and Items in the Tail of Distribution. ACM Trans. Inf. Syst. 35, 3, 1-37, 2017. (CCF A)
28. Wang, S., Hu, L., Cao, L., and Huang, X. Perceiving the Next Choice with Comprehensive Transaction Embeddings for Online Recommendation. In Machine Learning and Knowledge Discovery in Databases: European Conference, ECML/PKDD 2017 Proceedings, 2017. (CCF B)
29. Hu, L., Cao, L., Cao, J., Gu, Z., Xu, G., and Yang, D. Learning Informative Priors from Heterogeneous Domains to Improve Recommendation in Cold-Start User Domains. ACM Trans. Inf. Syst. 35, 2, 1-37, 2016. (CCF A)
30. Cao, W., Hu, L., and Cao, L. Deep Modeling Complex Couplings within Financial Markets. In Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, 2518-2524, 2015. (CCF A)
31. Chen, Q., Hu, L., Xu, J., Liu, W., and Cao, L. Document similarity analysis via involving both explicit and implicit semantic couplings. In Data Science and Advanced Analytics (DSAA), 2015. 36678 2015. IEEE International Conference on, 1-10, 2015. (CCF C)
32. Hu, L., Cao, J., Xu, G., Cao, L., Gu, Z., and Cao, W. Deep modeling of group preferences for group-based recommendation. In Twenty-Eighth AAAI Conference on Artificial Intelligence, 2014. (CCF A)
33. Hu, L., Cao, W., Cao, J., Xu, G., Cao, L., and Gu, Z. Bayesian Heteroskedastic Choice Modeling on Non-identically Distributed Linkages. In Data Mining (ICDM), 2014 IEEE International Conference on, 851-856, 2014. (CCF B)
34. Hu, L., Cao, J., Xu, G., Wang, J., Gu, Z., and Cao, L. Cross-domain collaborative filtering via bilinear multilevel analysis. In Proceedings of the Twenty-Third international joint conference on Artificial Intelligence, 2626-2632, 2013. (CCF A)
35. Hu, L., Cao, J., Xu, G., Cao, L., Gu, Z., and Zhu, C. Personalized recommendation via cross-domain triadic factorization. In Proceedings of the 22nd international conference on World Wide Web, 595-606, 2013. (CCF A)
36. Xu, W., Cao, J., Hu, L., Wang, J., and Li, M. A Social-Aware Service Recommendation Approach for Mashup Creation. In Web Services (ICWS), 2013 IEEE 20th International Conference on, 107-114, 2013. (CCF B)
37. Cao, J., Xu, W., Hu, L., Wang, J., and Li, M. A Social-Aware Service Recommendation Approach for Mashup Creation. Int. J. Web Serv. Res. 10, 1, 53-72, 2013.
38. You, Y., Huang, G., Cao, J., Chen, E., He, J., Zhang, Y., and Hu, L. GEAM: A General and Event-Related Aspects Model for Twitter Event Detection. In Web Information Systems Engineering – WISE 2013, 319-332, 2013. (CCF C)
