2 citations · 3 across the 11 of their papers we have counts for
12 papers
Beyond a Single Story: Meta-Reviewing Sparse and Incomplete User-generated Contents for Recommendation
Hongren Wang, Tianjun Wei, Yingpeng Du +2
Data sparsity remains a long-standing challenge in recommender systems, and it becomes more severe for methods relying on user-generated content (UGC) such as textual reviews, whic…
PLAN: Parallel Liquid-Inspired Approximation Network for Efficient Representation Learning in Flexible Job Shop Scheduling
Dhivya Dharshini Kannan, Wei Zhang, Jieyi Bi +5
Deep reinforcement learning (DRL) approaches for flexible job shop scheduling (FJSP) heavily rely on attention-centric architectures to achieve state-of-the-art performance. Howeve…
LLMs Are Not a Silver Bullet: A Case Study on Software Fairness
Xinyue Li, Sixuan Li, Ying Xiao +4
Fairness is a critical requirement for human-related, high-stakes software systems, motivating extensive research on bias mitigation. Prior work has largely focused on tabular data…
SkillMOO: Multi-Objective Optimization of Agent Skills for Software Engineering
Jingzhi Gong, Ruizhen Gu, Zhiwei Fei +7
Agent skills are increasingly used to configure coding agents for software engineering (SE) tasks, yet current practice treats them as static, hand-crafted assets, or evolved on pa…
MMGRid: Navigating Temporal-aware and Cross-domain Generative Recommendation via Model Merging
Tianjun Wei, Enneng Yang, Yingpeng Du +3
Model merging (MM) offers an efficient mechanism for integrating multiple specialized models without access to original training data or costly retraining. While MM has demonstrate…
Think When Needed: Model-Aware Reasoning Routing for LLM-based Ranking
Huizhong Guo, Tianjun Wei, Dongxia Wang +4
Large language models (LLMs) are increasingly applied to ranking tasks in retrieval and recommendation. Although reasoning prompting can enhance ranking utility, our preliminary ex…
Mirroring Users: Towards Building Preference-aligned User Simulator with User Feedback in Recommendation
Tianjun Wei, Huizhong Guo, Yingpeng Du +4
User simulation is increasingly vital to develop and evaluate recommender systems (RSs). While Large Language Models (LLMs) offer promising avenues to simulate user behavior, they…
LLM-Driven Dual-Level Multi-Interest Modeling for Recommendation
Ziyan Wang, Yingpeng Du, Zhu Sun +4
Recently, much effort has been devoted to modeling users' multi-interests based on their behaviors or auxiliary signals. However, existing methods often rely on heuristic assumptio…
Reinforcement Speculative Decoding for Fast Ranking
Yingpeng Du, Tianjun Wei, Zhu Sun +1
Large Language Models (LLMs) have been widely adopted in ranking systems such as information retrieval (IR) systems and recommender systems (RSs). To alleviate the latency of auto-…
Active Large Language Model-based Knowledge Distillation for Session-based Recommendation
Yingpeng Du, Zhu Sun, Ziyan Wang +3
Large language models (LLMs) provide a promising way for accurate session-based recommendation (SBR), but they demand substantial computational time and memory. Knowledge distillat…
Re2LLM: Reflective Reinforcement Large Language Model for Session-based Recommendation
Ziyan Wang, Yingpeng Du, Zhu Sun +4
Large Language Models (LLMs) are emerging as promising approaches to enhance session-based recommendation (SBR), where both prompt-based and fine-tuning-based methods have been wid…
Large Language Model with Graph Convolution for Recommendation
Yingpeng Du, Ziyan Wang, Zhu Sun +6
In recent years, efforts have been made to use text information for better user profiling and item characterization in recommendations. However, text information can sometimes be o…