| Quantifying and Mitigating Popularity Bias in Conversational Recommender Systems A Lin, J Wang, Z Zhu, J Caverlee CIKM '22: Proceedings of the 31st ACM International Conference on …, 2022 | 43 | 2022 |
| Enhancing User Personalization in Conversational Recommenders A Lin, Z Zhu, J Wang, J Caverlee WWW '23: Proceedings of the ACM Web Conference 2023, 770 - 778, 2023 | 13 | 2023 |
| Towards Fair Conversational Recommender Systems A Lin, Z Zhu, J Wang, J Caverlee RecSys '22: 16th ACM Conference on Recommender Systems - FAccTRec Workshop, 2022 | 5 | 2022 |
| Countering mainstream bias via end-to-end adaptive local learning J Pan, Z Zhu, J Wang, A Lin, J Caverlee European Conference on Information Retrieval, 75-89, 2024 | 3 | 2024 |
| Cold-Start Recommendation towards the Era of Large Language Models (LLMs): A Comprehensive Survey and Roadmap W Zhang, Y Bei, L Yang, HP Zou, P Zhou, A Liu, Y Li, H Chen, J Wang, ... arXiv preprint arXiv:2501.01945, 2025 | 2 | 2025 |
| Federated Conversational Recommender Systems A Lin, J Wang, Z Zhu, J Caverlee European Conference on Information Retrieval, 50-65, 2024 | | 2024 |
| Howdy Y’all: An Alexa TaskBot M Alfifi, X Dong, T Feldman, A Lin, K Madanagopal, A Pethe, M Teleki, ... | | |
James CaverleeProfessor, Computer Science and Engineering, Texas A&M UniversityPreverjeni e-poštni naslov na cse.tamu.edu