no code implementations • 6 Apr 2024 • Yabin Zhang, Wenhui Yu, Erhan Zhang, Xu Chen, Lantao Hu, Peng Jiang, Kun Gai
For the model part, we adopt Generative Pre-training Transformer (GPT) as the sequential recommendation model and design a user modular to capture personalized information.
1 code implementation • 28 Jun 2020 • Wenhui Yu, Zheng Qin
We predict users' preferences with the model and learn it by maximizing likelihood of observed data labels, i. e., a user prefers her positive samples and has no interests in her unvoted samples.
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.
2 code implementations • ICML 2020 • Wenhui Yu, Zheng Qin
\textbf{G}raph \textbf{C}onvolutional \textbf{N}etwork (\textbf{GCN}) is widely used in graph data learning tasks such as recommendation.
no code implementations • 2 May 2019 • Wenhui Yu, Xiangnan He, Jian Pei, Xu Chen, Li Xiong, Jinfei Liu, Zheng Qin
While recent developments on visually-aware recommender systems have taken the product image into account, none of them has considered the aesthetic aspect.
no code implementations • 2 May 2019 • Wenhui Yu, Zheng Qin
However, there are some demerits of side information: (1) the extra data is not always available in all recommendation tasks; (2) it is only for items, there is seldom high-level feature describing users.
no code implementations • 16 Sep 2018 • Wenhui Yu, Huidi Zhang, Xiangnan He, Xu Chen, Li Xiong, Zheng Qin
Considering that the aesthetic preference varies significantly from user to user and by time, we then propose a new tensor factorization model to incorporate the aesthetic features in a personalized manner.