Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Fan, Chaosheng"'
Autor:
Yang, Chen, Chen, Jin, Yu, Qian, Wu, Xiangdong, Ma, Kui, Zhao, Zihao, Fang, Zhiwei, Chen, Wenlong, Fan, Chaosheng, He, Jie, Peng, Changping, Lin, Zhangang, Shao, Jingping
Online recommenders have attained growing interest and created great revenue for businesses. Given numerous users and items, incremental update becomes a mainstream paradigm for learning large-scale models in industrial scenarios, where only newly ar
Externí odkaz:
http://arxiv.org/abs/2312.15903
Autor:
Song, Jinbo, Huang, Ruoran, Wang, Xinyang, Huang, Wei, Yu, Qian, Chen, Mingming, Yao, Yafei, Fan, Chaosheng, Peng, Changping, Lin, Zhangang, Hu, Jinghe, Shao, Jingping
Publikováno v:
Proceedings of the 31st ACM International Conference on Information & Knowledge Management. 2022: 4495-4499
Industrial systems such as recommender systems and online advertising, have been widely equipped with multi-stage architectures, which are divided into several cascaded modules, including matching, pre-ranking, ranking and re-ranking. As a critical b
Externí odkaz:
http://arxiv.org/abs/2310.08039
Autor:
Xu, Xiaoxiao, Fang, Zhiwei, Yu, Qian, Huang, Ruoran, Fan, \\Chaosheng, Li, Yong, He, Yang, Peng, Changping, Lin, Zhangang, Shao, Jingping
The exposure sequence is being actively studied for user interest modeling in Click-Through Rate (CTR) prediction. However, the existing methods for exposure sequence modeling bring extensive computational burden and neglect noise problems, resulting
Externí odkaz:
http://arxiv.org/abs/2204.14069
Autor:
Xu, Xiaoxiao, Yang, Chen, Yu, Qian, Fang, Zhiwei, Wang, Jiaxing, Fan, Chaosheng, He, Yang, Peng, Changping, Lin, Zhangang, Shao, Jingping
We propose a general Variational Embedding Learning Framework (VELF) for alleviating the severe cold-start problem in CTR prediction. VELF addresses the cold start problem via alleviating over-fits caused by data-sparsity in two ways: learning probab
Externí odkaz:
http://arxiv.org/abs/2201.10980
Autor:
Xiao, Xuanji, Chen, Huabin, Liu, Yuzhen, Yao, Xing, Liu, Pei, Fan, Chaosheng, Ji, Nian, Jiang, Xirong
Click-through rate (CTR) and post-click conversion rate (CVR) predictions are two fundamental modules in industrial ranking systems such as recommender systems, advertising, and search engines. Since CVR involves much fewer samples than CTR (known as
Externí odkaz:
http://arxiv.org/abs/2008.09872
Publikováno v:
Proceedings of the 36th International ACM Sigir Conference Research & Development in Information Retrieval; 7/28/2013, p1045-1048, 4p
Publikováno v:
Proceedings of the 36th International ACM Sigir Conference Research & Development in Information Retrieval; 7/28/2013, p949-953, 5p
Autor:
Fan, Chaosheng, Lin, Zuoquan
Publikováno v:
Web Technologies & Applications; 2013, p770-781, 12p