1 citations · 1 across the 5 of their papers we have counts for
6 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…
CADRL: Category-aware Dual-agent Reinforcement Learning for Explainable Recommendations over Knowledge Graphs
Shangfei Zheng, Hongzhi Yin, Tong Chen +3
Knowledge graphs (KGs) have been widely adopted to mitigate data sparsity and address cold-start issues in recommender systems. While existing KGs-based recommendation methods can…
Multi-Modality is All You Need for Transferable Recommender Systems
Youhua Li, Hanwen Du, Yongxin Ni +4
ID-based Recommender Systems (RecSys), where each item is assigned a unique identifier and subsequently converted into an embedding vector, have dominated the designing of RecSys.…
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…
Meta-optimized Joint Generative and Contrastive Learning for Sequential Recommendation
Yongjing Hao, Pengpeng Zhao, Junhua Fang +5
Sequential Recommendation (SR) has received increasing attention due to its ability to capture user dynamic preferences. Recently, Contrastive Learning (CL) provides an effective a…