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cs.LG2025
CORE: Contrastive Masked Feature Reconstruction on Graphs
Jianyuan Bo, Yuan Fang
In the rapidly evolving field of self-supervised learning on graphs, generative and contrastive methodologies have emerged as two dominant approaches. Our study focuses on masked f…
cs.LG2025
A Probabilistic Framework for Temporal Distribution Generalization in Industry-Scale Recommender Systems
Yuxuan Zhu, Cong Fu, Yabo Ni +2
Temporal distribution shift (TDS) erodes the long-term accuracy of recommender systems, yet industrial practice still relies on periodic incremental training, which struggles to ca…
cs.IR2025
A Contrastive Framework with User, Item and Review Alignment for Recommendation
Hoang V. Dong, Yuan Fang, Hady W. Lauw
Learning effective latent representations for users and items is the cornerstone of recommender systems. Traditional approaches rely on user-item interaction data to map users and…