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cs.LG2024
Do We Really Need Graph Convolution During Training? Light Post-Training Graph-ODE for Efficient Recommendation
Weizhi Zhang, Liangwei Yang, Zihe Song +4
The efficiency and scalability of graph convolution networks (GCNs) in training recommender systems (RecSys) have been persistent concerns, hindering their deployment in real-world…
cs.LG2023★ 1 cited
Conditional Denoising Diffusion for Sequential Recommendation
Yu Wang, Zhiwei Liu, Liangwei Yang +1
Generative models have attracted significant interest due to their ability to handle uncertainty by learning the inherent data distributions. However, two prominent generative mode…