no code implementations • 21 Jun 2023 • Wanda Li, Wenhao Zheng, Xuanji Xiao, Suhang Wang
Our experimental results using both public and industrial datasets demonstrate that the proposed model significantly improves multi-task prediction performance compared to state-of-the-art methods, highlighting the importance of considering user lifecycle stages in recommendation systems.
no code implementations • 12 May 2023 • Kai Ouyang, Chen Tang, Wenhao Zheng, Xiangjin Xie, Xuanji Xiao, Jian Dong, Hai-Tao Zheng, Zhi Wang
To address this issue, we propose using knowledge soft integration to balance the utilization of multimodal features and the curse of knowledge problem it brings about.
no code implementations • 3 Apr 2023 • Kai Ouyang, Wenhao Zheng, Chen Tang, Xuanji Xiao, Hai-Tao Zheng
To tackle this issue, we argue that a trade-off should be achieved between the introduction of large amounts of auxiliary information and the protection of valuable information related to CVR.
2 code implementations • 20 Feb 2023 • Xuanji Xiao, Huaqiang Dai, Qian Dong, Shuzi Niu, Yuzhen Liu, Pei Liu
Despite recommender systems play a key role in network content platforms, mining the user's interests is still a significant challenge.
no code implementations • 16 Feb 2023 • Xuanji Xiao, Ziyu He
Rank models play a key role in industrial recommender systems, advertising, and search engines.
no code implementations • 13 Feb 2023 • Guoxi Zhang, Xing Yao, Xuanji Xiao
An ultimate goal of recommender systems (RS) is to improve user engagement.
no code implementations • 22 Aug 2020 • Xuanji Xiao, Hua-Bin Chen, Yuzhen Liu, Xing Yao, Pei Liu, Chaosheng Fan, Nian Ji, Xirong Jiang
To address this sharing&conflict problem, we propose a novel multi-task CVR modeling scheme with neuron-connection level sharing named NCS4CVR, which can automatically and flexibly learn which neuron weights are shared or not shared without artificial experience.