1 code implementation • Findings (ACL) 2022 • Jian Li, Jieming Zhu, Qiwei Bi, Guohao Cai, Lifeng Shang, Zhenhua Dong, Xin Jiang, Qun Liu
Accurately matching user’s interests and candidate news is the key to news recommendation.
no code implementations • 15 Apr 2024 • JunJie Huang, Guohao Cai, Jieming Zhu, Zhenhua Dong, Ruiming Tang, Weinan Zhang, Yong Yu
RAR consists of two key sub-modules, which synergistically gather information from a vast pool of look-alike users and recall items, resulting in enriched user representations.
1 code implementation • 16 Aug 2023 • Haiyuan Zhao, Lei Zhang, Jun Xu, Guohao Cai, Zhenhua Dong, Ji-Rong Wen
In the video recommendation, watch time is commonly adopted as an indicator of user interest.
1 code implementation • 15 Jun 2023 • Jieming Zhu, Guohao Cai, JunJie Huang, Zhenhua Dong, Ruiming Tang, Weinan Zhang
The error memory module is designed with fast access capabilities and undergoes continual refreshing with newly observed data samples during the model serving phase to support fast model adaptation.
4 code implementations • 3 Apr 2023 • Kelong Mao, Jieming Zhu, Liangcai Su, Guohao Cai, Yuru Li, Zhenhua Dong
As such, many two-stream interaction models (e. g., DeepFM and DCN) have been proposed by integrating an MLP network with another dedicated network for enhanced CTR prediction.
Ranked #2 on Click-Through Rate Prediction on MovieLens
5 code implementations • 19 May 2022 • Jieming Zhu, Quanyu Dai, Liangcai Su, Rong Ma, Jinyang Liu, Guohao Cai, Xi Xiao, Rui Zhang
Despite significant progress made in both research and practice of recommender systems, to date, there is a lack of a widely-recognized benchmarking standard in this field.
no code implementations • 24 Apr 2022 • Guohao Cai, Jieming Zhu, Quanyu Dai, Zhenhua Dong, Xiuqiang He, Ruiming Tang, Rui Zhang
Deep learning-based recommendation has become a widely adopted technique in various online applications.
no code implementations • 2 Apr 2022 • Haiyuan Zhao, Jun Xu, Xiao Zhang, Guohao Cai, Zhenhua Dong, Ji-Rong Wen
An extension to the pairwise neural ranking is also developed.
no code implementations • 23 Mar 2022 • Yi Li, Jieming Zhu, Weiwen Liu, Liangcai Su, Guohao Cai, Qi Zhang, Ruiming Tang, Xi Xiao, Xiuqiang He
Specifically, PEAR not only captures feature-level and item-level interactions, but also models item contexts from both the initial ranking list and the historical clicked item list.
no code implementations • 16 Jan 2022 • Mengyue Yang, Guohao Cai, Furui Liu, Zhenhua Dong, Xiuqiang He, Jianye Hao, Jun Wang, Xu Chen
To alleviate these problems, in this paper, we propose a novel debiased recommendation framework based on user feature balancing.
no code implementations • 5 Mar 2021 • Chang Liu, Xiaoguang Li, Guohao Cai, Zhenhua Dong, Hong Zhu, Lifeng Shang
It is still an open question to leverage various types of information under the BERT framework.