3 papers
cs.IR2026
Time-Aware Diffusion based on Preference Disentanglement for Generative Recommendation
Bangguo Zhu, Peng Huo, Yuanbo Zhao +3
Recently, Generative Recommenders (GRs) have emerged as a transformative recommendation paradigm by replacing traditional item IDs with semantic indices (SIDs). Owing to the except…
cs.IR2026
Echoes in Filter Bubble: Diagnosing and Curing Popularity Bias in Generative Recommenders
Jun Yin, Bangguo Zhu, Peng Huo +5
Recently, Generative Recommenders (GRs), characterized by a unified end-to-end framework, have exhibited astonishing potential in transforming the recommendation paradigm. Despite…
cs.AI2026
Rel-MOSS: Towards Imbalanced Relational Deep Learning on Relational Databases
Jun Yin, Peng Huo, Bangguo Zhu +4
In recent advances, to enable a fully data-driven learning paradigm on relational databases (RDB), relational deep learning (RDL) is proposed to structure the RDB as a heterogeneou…