1 code implementation • 8 Jun 2023 • Sirui Chen, YuAn Wang, Zijing Wen, Zhiyu Li, Changshuo Zhang, Xiao Zhang, Quan Lin, Cheng Zhu, Jun Xu
In this paper, we propose a framework called controllable multi-objective re-ranking (CMR) which incorporates a hypernetwork to generate parameters for a re-ranking model according to different preference weights.
no code implementations • 11 Apr 2022 • Sirui Chen, Xiao Zhang, Xu Chen, Zhiyu Li, YuAn Wang, Quan Lin, Jun Xu
Then, it defines \emph{the MDP discrete time steps as the ranks in the initial ranking list, and the actions as the prediction of the user-item preference and the selection of the slots}.
no code implementations • 16 Oct 2019 • Wenhao Zhang, Wentian Bao, Xiao-Yang Liu, Keping Yang, Quan Lin, Hong Wen, Ramin Ramezani
In addition, our methods are based on the multi-task learning framework and mitigate the data sparsity issue.
no code implementations • 15 Oct 2019 • Hong Wen, Jing Zhang, Yu-An Wang, Fuyu Lv, Wentian Bao, Quan Lin, Keping Yang
Although existing methods, typically built on the user sequential behavior path ``impression$\to$click$\to$purchase'', is effective for dealing with SSB issue, they still struggle to address the DS issue due to rare purchase training samples.
2 code implementations • 1 Sep 2019 • Fuyu Lv, Taiwei Jin, Changlong Yu, Fei Sun, Quan Lin, Keping Yang, Wilfred Ng
In this paper, we propose a new sequential deep matching (SDM) model to capture users' dynamic preferences by combining short-term sessions and long-term behaviors.
no code implementations • 24 May 2018 • Hong Wen, Jing Zhang, Quan Lin, Keping Yang, Pipei Huang
The deep cascade structure and the combination rule enable the proposed \textit{ldcTree} to have a stronger distributed feature representation ability.