Članki z zahtevami za javni dostop - Ziwei ZhuVeč o tem
Na voljo nekje: 18
Fairness-aware tensor-based recommendation
Z Zhu, X Hu, J Caverlee
Proceedings of the 27th ACM international conference on information and …, 2018
Zahteve: US National Science Foundation, US Department of Defense
Popularity-opportunity bias in collaborative filtering
Z Zhu, Y He, X Zhao, Y Zhang, J Wang, J Caverlee
Proceedings of the 14th ACM international conference on web search and data …, 2021
Zahteve: US National Science Foundation
Measuring and mitigating item under-recommendation bias in personalized ranking systems
Z Zhu, J Wang, J Caverlee
Proceedings of the 43rd international ACM SIGIR conference on research and …, 2020
Zahteve: US National Science Foundation
Recommendation for new users and new items via randomized training and mixture-of-experts transformation
Z Zhu, S Sefati, P Saadatpanah, J Caverlee
Proceedings of the 43rd International ACM SIGIR conference on research and …, 2020
Zahteve: US National Science Foundation
Popularity bias in dynamic recommendation
Z Zhu, Y He, X Zhao, J Caverlee
Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data …, 2021
Zahteve: US National Science Foundation
Fairness among new items in cold start recommender systems
Z Zhu, J Kim, T Nguyen, A Fenton, J Caverlee
Proceedings of the 44th international ACM SIGIR conference on research and …, 2021
Zahteve: US National Science Foundation
Improving top-k recommendation via jointcollaborative autoencoders
Z Zhu, J Wang, J Caverlee
The World Wide Web Conference, 3483-3482, 2019
Zahteve: US National Science Foundation
Unbiased implicit recommendation and propensity estimation via combinational joint learning
Z Zhu, Y He, Y Zhang, J Caverlee
Proceedings of the 14th ACM conference on recommender systems, 551-556, 2020
Zahteve: US National Science Foundation, US Department of Defense
Quantifying and mitigating popularity bias in conversational recommender systems
A Lin, J Wang, Z Zhu, J Caverlee
Proceedings of the 31st ACM international conference on information …, 2022
Zahteve: US National Science Foundation
Content-collaborative disentanglement representation learning for enhanced recommendation
Y Zhang, Z Zhu, Y He, J Caverlee
Proceedings of the 14th ACM conference on recommender systems, 43-52, 2020
Zahteve: US National Science Foundation, US Department of Defense
Modeling and detecting student attention and interest level using wearable computers
Z Zhu, S Ober, R Jafari
2017 IEEE 14th international conference on wearable and implantable body …, 2017
Zahteve: US Department of Defense
Key opinion leaders in recommendation systems: Opinion elicitation and diffusion
J Wang, K Ding, Z Zhu, Y Zhang, J Caverlee
Proceedings of the 13th international conference on web search and data …, 2020
Zahteve: US National Science Foundation
End-to-end learning for fair ranking systems
J Kotary, F Fioretto, P Van Hentenryck, Z Zhu
Proceedings of the ACM Web Conference 2022, 3520-3530, 2022
Zahteve: US National Science Foundation
Improving the estimation of tail ratings in recommender system with multi-latent representations
X Zhao, Z Zhu, Y Zhang, J Caverlee
Proceedings of the 13th International Conference on Web Search and Data …, 2020
Zahteve: US Department of Defense
Fighting mainstream bias in recommender systems via local fine tuning
Z Zhu, J Caverlee
Proceedings of the Fifteenth ACM International Conference on Web Search and …, 2022
Zahteve: US National Science Foundation
User recommendation in content curation platforms
J Wang, Z Zhu, J Caverlee
Proceedings of the 13th International Conference on Web Search and Data …, 2020
Zahteve: US National Science Foundation
Evolution of filter bubbles and polarization in news recommendation
H Zhang, Z Zhu, J Caverlee
European Conference on Information Retrieval, 685-693, 2023
Zahteve: US National Science Foundation
Global gallery: The fine art of painting culture portraits through multilingual instruction tuning
A Mukherjee, A Caliskan, Z Zhu, A Anastasopoulos
Association for Computational Linguistics, 2024
Zahteve: US National Science Foundation
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