Zobrazeno 1 - 10
of 19
pro vyhledávání: '"Dugang Liu"'
Publikováno v:
IEEE Access, Vol 6, Pp 36420-36427 (2018)
Social recommender is an active research area. Most previous social recommenders adopt existing social networks to augment recommendations which are based on user preferences. In this contribution, we propose to simultaneously infer the social influe
Externí odkaz:
https://doaj.org/article/3ffeeba4e487433da2e214b1754bc993
Autor:
Dugang Liu, Pengxiang Cheng, Zinan Lin, Xiaolian Zhang, Zhenhua Dong, Rui Zhang, Xiuqiang He, Weike Pan, Zhong Ming
Publikováno v:
ACM Transactions on Information Systems. 41:1-26
Debiased recommendation with a randomized dataset has shown very promising results in mitigating the system-induced biases. However, it still lacks more theoretical insights or an ideal optimization objective function compared with the other more wel
Publikováno v:
ACM Transactions on Recommender Systems. 1:1-27
How to effectively mitigate the bias of feedback in recommender systems is an important research topic. In this article, we first describe the generation process of the biased and unbiased feedback in recommender systems via two respective causal dia
Autor:
Dugang Liu, Pengxiang Cheng, Hong Zhu, Xing Tang, Yanyu Chen, Xiaoting Wang, Weike Pan, Zhong Ming, Xiuqiang He
Publikováno v:
Proceedings of the ACM Web Conference 2023.
Tabular data is one of the most common data storage formats behind many real-world web applications such as retail, banking, and e-commerce. The success of these web applications largely depends on the ability of the employed machine learning model t
Click-through prediction (CTR) models transform features into latent vectors and enumerate possible feature interactions to improve performance based on the input feature set. Therefore, when selecting an optimal feature set, we should consider the i
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::53c6b37fc3cea19567f35b2a3f6332bc
http://arxiv.org/abs/2301.10909
http://arxiv.org/abs/2301.10909
Publikováno v:
Database Systems for Advanced Applications ISBN: 9783031306778
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::444eeb512649aa1ff7be8b75f9ef2ca8
https://doi.org/10.1007/978-3-031-30678-5_44
https://doi.org/10.1007/978-3-031-30678-5_44
Publikováno v:
2022 IEEE 9th International Conference on Data Science and Advanced Analytics (DSAA).
Autor:
Dugang Liu, Mingkai He, Jinwei Luo, Jiangxu Lin, Meng Wang, Xiaolian Zhang, Weike Pan, Zhong Ming
Publikováno v:
Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining.
Publikováno v:
2022 International Joint Conference on Neural Networks (IJCNN).
Publikováno v:
RecSys
In a recommender system, a user’s interaction is often biased by the items’ displaying positions and popularity, as well as the user’s self-selection. Most existing recommendation models are built using such a biased user-system interaction dat