1 code implementation • 22 Oct 2023 • Xiuyuan Qin, Huanhuan Yuan, Pengpeng Zhao, Guanfeng Liu, Fuzhen Zhuang, Victor S. Sheng
In this paper, Intent contrastive learning with Cross Subsequences for sequential Recommendation (ICSRec) is proposed to model users' latent intentions.
1 code implementation • 7 May 2023 • Xinyu Du, Huanhuan Yuan, Pengpeng Zhao, Junhua Fang, Guanfeng Liu, Yanchi Liu, Victor S. Sheng, Xiaofang Zhou
Sequential recommendation (SR) aims to model user preferences by capturing behavior patterns from their item historical interaction data.
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.
1 code implementation • 22 Apr 2023 • Huanhuan Yuan, Pengpeng Zhao, Xuefeng Xian, Guanfeng Liu, Victor S. Sheng, Lei Zhao
To better capture the uncertainty and evolution of user tastes, SR-PLR embeds users and items with a probabilistic method and conducts probabilistic logical reasoning on users' interaction patterns.
1 code implementation • 18 Apr 2023 • Xinyu Du, Huanhuan Yuan, Pengpeng Zhao, Jianfeng Qu, Fuzhen Zhuang, Guanfeng Liu, Victor S. Sheng
However, many recent studies represent that current self-attention based models are low-pass filters and are inadequate to capture high-frequency information.
1 code implementation • 16 Apr 2023 • Xiuyuan Qin, Huanhuan Yuan, Pengpeng Zhao, Junhua Fang, Fuzhen Zhuang, Guanfeng Liu, Victor Sheng
By applying both data augmentation and learnable model augmentation operations, this work innovates the standard CL framework by contrasting data and model augmented views for adaptively capturing the informative features hidden in stochastic data augmentation.
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.