4 citations · 6 across the 6 of their papers we have counts for
9 papers
How Do Graph Signals Affect Recommendation: Unveiling the Mystery of Low and High-Frequency Graph Signals
Feng Liu, Hao Cang, Huanhuan Yuan +5
Spectral graph neural networks (GNNs) are highly effective in modeling graph signals, with their success in recommendation often attributed to low-pass filtering. However, recent s…
Wavelet Enhanced Adaptive Frequency Filter for Sequential Recommendation
Huayang Xu, Huanhuan Yuan, Guanfeng Liu +3
Sequential recommendation has garnered significant attention for its ability to capture dynamic preferences by mining users' historical interaction data. Given that users' complex…
Intent Contrastive Learning with Cross Subsequences for Sequential Recommendation
Xiuyuan Qin, Huanhuan Yuan, Pengpeng Zhao +3
The user purchase behaviors are mainly influenced by their intentions (e.g., buying clothes for decoration, buying brushes for painting, etc.). Modeling a user's latent intention c…
Frequency Enhanced Hybrid Attention Network for Sequential Recommendation
Xinyu Du, Huanhuan Yuan, Pengpeng Zhao +4
The self-attention mechanism, which equips with a strong capability of modeling long-range dependencies, is one of the extensively used techniques in the sequential recommendation…
Ensemble Modeling with Contrastive Knowledge Distillation for Sequential Recommendation
Hanwen Du, Huanhuan Yuan, Pengpeng Zhao +4
Sequential recommendation aims to capture users' dynamic interest and predicts the next item of users' preference. Most sequential recommendation methods use a deep neural network…
Sequential Recommendation with Probabilistic Logical Reasoning
Huanhuan Yuan, Pengpeng Zhao, Xuefeng Xian +3
Deep learning and symbolic learning are two frequently employed methods in Sequential Recommendation (SR). Recent neural-symbolic SR models demonstrate their potential to enable SR…