1 citations · 1 across the 11 of their papers we have counts for
13 papers
From Retrieval to Recognition:Knowledge-Enhanced Post-training of Time Series Foundation Models for Classification
Zhen Liu, Yucheng Wang, Yingpeng Du +5
Time series foundation models (TSFMs) have achieved notable progress in classification tasks through large-scale pretraining. However, their post-training practice mainly focuses o…
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…
SG-LegalCite: A Principle-Augmented Benchmark for Legal Citation Retrieval in Singapore Law
Shannon Lee Yueh Ern, Kaidong Feng, Yingpeng Du +2
Legal citation in common-law systems depends not only on factual similarity, but also on the legal principle for which a precedent is invoked. However, existing benchmarks for lega…
Through Their Eyes: Fixation-aligned Tuning for Personalized User Emulation
Lingfeng Huang, Huizhong Guo, Tianjun Wei +2
Large language model (LLM) agents are increasingly deployed as scalable user simulators for recommender system evaluation. Yet existing simulators perceive recommendations through…
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-…
Uncertain Multi-Objective Recommendation via Orthogonal Meta-Learning Enhanced Bayesian Optimization
Hongxu Wang, Zhu Sun, Yingpeng Du +3
Recommender systems (RSs) play a crucial role in shaping our digital interactions, influencing how we access and engage with information across various domains. Traditional researc…
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…