activity
20242026
most citedA Survey on Diffusion Models for Recommender Systems

5 citations · 5 across the 7 of their papers we have counts for

collaborators

7 papers

cs.MA2026

MoRSE: Task-Oriented Multi-Agent System with Mixture of Role-Subtask Experts

Peiwen Li, Shiyang Zhang, Yangtian Zhang +3

Large language model-based multi-agent systems have recently shown strong potential for complex, long-horizon tasks. However, existing methods mainly rely on coarse prompt-level di…

cs.IR2026

CoPersona: Collaborative Persona Graphs for Robust LLM Personalization

Yangtian Zhang, Leyao Wang, Hiren Madhu +3

Real-world LLM personalization is often constrained by sparse and skewed user histories: most users provide only a handful of interactions, while even frequent users' logs capture…

cs.LG2026

Variational Learning for Insertion-based Generation

Yangtian Zhang, Zhe Wang, Arthur Gretton +4

Non-monotonic sequence generation methods, such as masked diffusion models, provide a flexible alternative to left-to-right autoregressive modeling by allowing tokens to be generat…

cs.LG2026

Learning Permutation Distributions via Reflected Diffusion on Ranks

Sizhuang He, Yangtian Zhang, Shiyang Zhang +1

The finite symmetric group S_n provides a natural domain for permutations, yet learning probability distributions on S_n is challenging due to its factorially growing size and disc…

cs.CE2026

STRIDE: Post-Training LLMs to Reason and Refine Bio-Sequences via Edit Trajectories

Daiheng Zhang, Shiyang Zhang, Sizhuang He +3

Discrete biological sequence optimization often requires goal-directed, parser-valid edits to an existing protein or molecule. Diffusion models support iterative refinement but do…

cs.IR2025

Flow Matching for Collaborative Filtering

Chengkai Liu, Yangtian Zhang, Jianling Wang +2

Generative models have shown great promise in collaborative filtering by capturing the underlying distribution of user interests and preferences. However, existing approaches strug…

cs.IR2024★ 5 cited

A Survey on Diffusion Models for Recommender Systems

Jianghao Lin, Jiaqi Liu, Jiachen Zhu +5

While traditional recommendation techniques have made significant strides in the past decades, they still suffer from limited generalization performance caused by factors like inad…