2 citations · 3 across the 15 of their papers we have counts for
16 papers
Scaling World-Model Reinforcement Learning Through Diffusion Policy Optimization
Xiaoyuan Cheng, Wenxuan Yuan, Zhancun Mu +5
Model-based reinforcement learning (RL) can be effectively supported at scale through the use of world models. However, in practice, scaling such approaches remains fundamentally l…
Route What Remains: A Meta-Modal Agent for Missing-Modality Candidate Reranking in Recommender Systems
Jinze Wang, Yangchen Zeng, Tiehua Zhang +5
Missing-modality recommenders usually reconstruct absent representations, although the observed evidence may not determine the missing content. We formulate candidate reranking as…
Interdomain Attention: Beyond Token-Level Key-Value Memory
Naoki Kiyohara, Harrison Bo Hua Zhu, Riccardo El Hassanin +4
Softmax attention-based transformers and recurrent memory-based models (e.g., state space models (SSMs)) form the foundation of modern sequence models. Hybrid architectures aim to…
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…
FashionStylist: An Expert Knowledge-enhanced Multimodal Dataset for Fashion Understanding
Kaidong Feng, Zhuoxuan Huang, Huizhong Guo +7
Fashion understanding requires both visual perception and expert-level reasoning about style, occasion, compatibility, and outfit rationale. However, existing fashion datasets rema…
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…
Fusion and Alignment Enhancement with Large Language Models for Tail-item Sequential Recommendation
Zhifu Wei, Yizhou Dang, Guibing Guo +2
Sequential Recommendation (SR) learns user preferences from their historical interaction sequences and provides personalized suggestions. In real-world scenarios, most items exhibi…
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-…
Enhancing New-item Fairness in Dynamic Recommender Systems
Huizhong Guo, Zhu Sun, Dongxia Wang +3
New-items play a crucial role in recommender systems (RSs) for delivering fresh and engaging user experiences. However, traditional methods struggle to effectively recommend new-it…
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…