no code implementations • 15 May 2024 • Yi Zhang, Lei Sang, Yiwen Zhang
To counter the sparsity of implicit feedback, the feature distributions of users and items are encoded via a Gaussian-based graph generation strategy, and we implement the recommendation process through bilateral intent-guided graph reconstruction re-sampling.
no code implementations • 8 May 2024 • Wenjie Chen, Yi Zhang, Honghao Li, Lei Sang, Yiwen Zhang
The embedding-space collaborative denoising module devotes to resisting the noise cross-domain diffusion problem through contrastive learning with dual-domain embedding collaborative perturbation.
1 code implementation • 6 May 2024 • Honghao Li, Yiwen Zhang, Yi Zhang, Lei Sang, Yun Yang
Specifically, the framework employs the SSEM at the bottom of the model to differentiate between samples, thereby assigning a more suitable encoder for each sample.
Ranked #1 on Click-Through Rate Prediction on Frappe
no code implementations • 3 Apr 2024 • Yu Wang, Lei Sang, Yi Zhang, Yiwen Zhang
3) A hierarchical contrastive learning strategy for capturing local and global information.
no code implementations • 6 Mar 2024 • Li Wang, Lei Sang, Quangui Zhang, Qiang Wu, Min Xu
Furthermore, we introduce a privacy-preserving decoder to mitigate user privacy leakage during knowledge transfer.
1 code implementation • 15 Dec 2023 • Honghao Li, Lei Sang, Yi Zhang, Xuyun Zhang, Yiwen Zhang
Click-through rate (CTR) Prediction is a crucial task in personalized information retrievals, such as industrial recommender systems, online advertising, and web search.
Ranked #4 on Click-Through Rate Prediction on Avazu
no code implementations • 24 Mar 2018 • Lei Sang, Min Xu, Shengsheng Qian, Xindong Wu
Existing methods usually adopt a "one-size-fits-all" approach when concerning multi-scale structure information, such as first- and second-order proximity of nodes, ignoring the fact that different scales play different roles in the embedding learning.