1 citations · 1 across the 6 of their papers we have counts for
6 papers
TopoGR: Revealing and Preserving Latent Structure of Semantic ID in Generative Recommendation
Ziyu Zheng, Zhengshun Du, Yaming Yang +5
Semantic ID-based generative recommendation tokenizes each item into a sequence of discrete semantic IDs and predicts the next item by generating semantic IDs. However, existing me…
Can Generative Recommendation Reach Cold Items? A Temporal Perspective on Semantic-ID Generation
Jie Peng, Yanping Zheng, Zhewei Zhe +3
Semantic-ID-based generative recommendation represents items as sequences of shared semantic tokens, enabling token recombination beyond isolated item IDs. However, closed-world re…
Cross-Head Attention Uplift Network with Inverse Propensity Score under Unobserved Confounding
Haoran Zhang, Chuanpu Li, Yuxin Fu +4
Uplift modeling, crucial for estimating individual treatment effects (ITE), faces dual challenges: flexibly leveraging inter-group similarity to enhance discriminative power and de…
From Leakage to Fidelity: Reliable Benchmarking for Temporal Cascade Prediction
Jie Peng, Rui Wang, Qiang Wang +4
Temporal cascade prediction is widely studied, yet its empirical foundations remain fragile. Most existing works report results under random cascade splits that mix past and future…
GRAPHIA: Harnessing Social Graph Data to Enhance LLM-Based Social Simulation
Jiarui Ji, Zehua Zhang, Zhewei Wei +3
Large language models (LLMs) have shown promise in simulating human-like social behaviors. Social graphs provide high-quality supervision signals that encode both local interaction…
Crafting Efficient Fine-Tuning Strategies for Large Language Models
Michael Oliver, Guan Wang
This paper addresses the challenges of efficiently fine-tuning large language models (LLMs) by exploring data efficiency and hyperparameter optimization. We investigate the minimum…