no code implementations • 21 May 2023 • Yue Xu, Hao Chen, Zefan Wang, Jianwen Yin, Qijie Shen, Dimin Wang, Feiran Huang, Lixiang Lai, Tao Zhuang, Junfeng Ge, Xia Hu
Feed recommendation systems, which recommend a sequence of items for users to browse and interact with, have gained significant popularity in practical applications.
no code implementations • 21 May 2023 • Yue Xu, Qijie Shen, Jianwen Yin, Zengde Deng, Dimin Wang, Hao Chen, Lixiang Lai, Tao Zhuang, Junfeng Ge
Integrated recommendation, which aims at jointly recommending heterogeneous items from different channels in a main feed, has been widely applied to various online platforms.
no code implementations • 1 Mar 2023 • Shanshan Lyu, Qiwei Chen, Tao Zhuang, Junfeng Ge
Although existing methods ESMM and ESM2 train with all impression samples over the entire space by modeling user behavior paths, SSB and DS problems still exist.
no code implementations • 19 Oct 2022 • Xu Yuan, Chen Xu, Qiwei Chen, Tao Zhuang, Hongjie Chen, Chao Li, Junfeng Ge
This paper proposes a Hierarchical Multi-Interest Co-Network (HCN) to capture users' diverse interests in the coarse-grained ranking stage.
no code implementations • 25 Sep 2022 • Qiwei Chen, Yue Xu, Changhua Pei, Shanshan Lv, Tao Zhuang, Junfeng Ge
The results verify that the proposed model outperforms existing CTR models considerably, in terms of both CTR prediction performance and online cost-efficiency.
1 code implementation • 10 Aug 2021 • Qiwei Chen, Changhua Pei, Shanshan Lv, Chao Li, Junfeng Ge, Wenwu Ou
Recently, researchers have found that the performance of CTR model can be improved greatly by taking user behavior sequence into consideration, especially long-term user behavior sequence.
no code implementations • 24 Feb 2021 • Yufei Feng, Yu Gong, Fei Sun, Junfeng Ge, Wenwu Ou
Afterwards, for the candidate list set, the PRank stage provides a unified permutation-wise ranking criterion named LR metric, which is calculated by the rating scores of elaborately designed permutation-wise model DPWN.
1 code implementation • 10 Jan 2021 • Yingqiang Ge, Shuchang Liu, Ruoyuan Gao, Yikun Xian, Yunqi Li, Xiangyu Zhao, Changhua Pei, Fei Sun, Junfeng Ge, Wenwu Ou, Yongfeng Zhang
We focus on the fairness of exposure of items in different groups, while the division of the groups is based on item popularity, which dynamically changes over time in the recommendation process.
1 code implementation • 28 Jun 2020 • Wenhui Yu, Xiao Lin, Junfeng Ge, Wenwu Ou, Zheng Qin
This causes two difficulties in designing effective algorithms: first, the majority of users only have a few interactions with the system and there is no enough data for learning; second, there are no negative samples in the implicit feedbacks and it is a common practice to perform negative sampling to generate negative samples.
no code implementations • 11 Jul 2019 • Chen Xu, Quan Li, Junfeng Ge, Jinyang Gao, Xiaoyong Yang, Changhua Pei, Fei Sun, Jian Wu, Hanxiao Sun, Wenwu Ou
To guarantee the consistency of off-line training and on-line serving, we usually utilize the same features that are both available.