Zobrazeno 1 - 10
of 16
pro vyhledávání: '"Changhua Pei"'
Autor:
Qingyang Yu, Changhua Pei, Bowen Hao, Mingjie Li, Zeyan Li, Shenglin Zhang, Xianglin Lu, Rui Wang, Jiaqi Li, Zhenyu Wu, Dan Pei
Publikováno v:
Proceedings of the ACM Web Conference 2023.
Autor:
Ruohan Zhan, Changhua Pei, Qiang Su, Jianfeng Wen, Xueliang Wang, Guanyu Mu, Dong Zheng, Peng Jiang, Kun Gai
Publikováno v:
Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining.
Publikováno v:
WWW
Slate recommendation generates a list of items as a whole instead of ranking each item individually, so as to better model the intra-list positional biases and item relations. In order to deal with the enormous combinatorial space of slates, recent w
Autor:
Shuchang Liu, Ruoyuan Gao, Junfeng Ge, Fei Sun, Yingqiang Ge, Changhua Pei, Yunqi Li, Wenwu Ou, Xiangyu Zhao, Yikun Xian, Yongfeng Zhang
Publikováno v:
Proceedings of the 14th ACM International Conference on Web Search and Data Mining.
As Recommender Systems (RS) influence more and more people in their daily life, the issue of fairness in recommendation is becoming more and more important. Most of the prior approaches to fairness-aware recommendation have been situated in a static
Publikováno v:
SIGIR
Personalized recommendation benefits users in accessing contents of interests effectively. Current research on recommender systems mostly focuses on matching users with proper items based on user interests. However, significant efforts are missing to
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c8f5f34345df7347026d1928aad3b3d7
Publikováno v:
CIKM
Modeling users' dynamic and evolving preferences from their historical behaviors is challenging and crucial for recommendation systems. Previous methods employ sequential neural networks (e.g., Recurrent Neural Network) to encode users' historical in
Autor:
Jian Wu, Junfeng Ge, Fei Sun, Peng Jiang, Yongfeng Zhang, Dan Pei, Wenwu Ou, Yi Zhang, Xiao Lin, Changhua Pei, Hanxiao Sun
Publikováno v:
RecSys
Ranking is a core task in recommender systems, which aims at providing an ordered list of items to users. Typically, a ranking function is learned from the labeled dataset to optimize the global performance, which produces a ranking score for each in
Autor:
Xiao Lin, Fei Sun, Wenwu Ou, Hongjie Chen, Xuanji Xiao, Yongfeng Zhang, Changhua Pei, Peng Jiang, Hanxiao Sun
Publikováno v:
RecSys
Recommendation with multiple objectives is an important but difficult problem, where the coherent difficulty lies in the possible conflicts between objectives. In this case, multi-objective optimization is expected to be Pareto efficient, where no si
Autor:
Changhua Pei, Hanxiao Sun, Jian Wu, Chen Xu, Fei Sun, Jinyang Gao, Junfeng Ge, Wenwu Ou, Quan Li, Xiaoyong Yang
Publikováno v:
KDD
Features play an important role in the prediction tasks of e-commerce recommendations. To guarantee the consistency of off-line training and on-line serving, we usually utilize the same features that are both available. However, the consistency in tu
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e433b27dc34d4b71979b49a7f306f1e0
http://arxiv.org/abs/1907.05171
http://arxiv.org/abs/1907.05171
Publikováno v:
WWW
Existing recommendation algorithms mostly focus on optimizing traditional recommendation measures, such as the accuracy of rating prediction in terms of RMSE or the quality of top-k recommendation lists in terms of precision, recall, MAP, etc. Howeve