1 code implementation • 26 Mar 2024 • Youhua Li, Hanwen Du, Yongxin Ni, Yuanqi He, Junchen Fu, Xiangyan Liu, Qi Guo
Sequential Recommendation (SR) aims to predict future user-item interactions based on historical interactions.
1 code implementation • 15 Dec 2023 • Youhua Li, Hanwen Du, Yongxin Ni, Pengpeng Zhao, Qi Guo, Fajie Yuan, Xiaofang Zhou
To align the cross-modal item representations, we propose a novel next-item enhanced cross-modal contrastive learning objective, which is equipped with both inter- and intra-modality negative samples and explicitly incorporates the transition patterns of user behaviors into the item encoders.
1 code implementation • 28 Apr 2023 • Hanwen Du, Huanhuan Yuan, Pengpeng Zhao, Fuzhen Zhuang, Guanfeng Liu, Lei Zhao, Victor S. Sheng
Our framework adopts multiple parallel networks as an ensemble of sequence encoders and recommends items based on the output distributions of all these networks.
no code implementations • 10 Apr 2023 • Hanwen Du, Huanhuan Yuan, Zhen Huang, Pengpeng Zhao, Xiaofang Zhou
Generative models, such as Variational Auto-Encoder (VAE) and Generative Adversarial Network (GAN), have been successfully applied in sequential recommendation.
1 code implementation • 8 Aug 2022 • Hanwen Du, Hui Shi, Pengpeng Zhao, Deqing Wang, Victor S. Sheng, Yanchi Liu, Guanfeng Liu, Lei Zhao
Contrastive learning with Transformer-based sequence encoder has gained predominance for sequential recommendation.