26 citations · 104 across the 32 of their papers we have counts for
32 papers
From Understanding to Action: Feedback-Grounded Policy Discovery for Generative Recommendation
Zhi Chen, Minmao Wang, Xingchen Liu +8
Semantic-ID-based generative recommenders enable efficient next-item generation, but their item-level supervision mainly captures behavioral co-occurrence and local transitions. La…
From Trajectories to Prefixes: Reusing Teacher Trajectories via Replayed Prefixes and Online Continuation
Yihan Wang, Zhong Guan, Haoran Sun +3
Small language models are attractive backbones for interactive agents, but direct distillation from strong teacher trajectories often turns rich multi-turn behavior into one-shot i…
Robust Contrastive Graph Clustering with Adaptive Local-Global Integration
Lei Zhang, Fubo Sun, Haipeng Yang +2
Graph clustering is essential in graph analysis for revealing structural patterns and node communities. Despite recent advances in self-supervised contrastive learning that have im…
GCIB: Graph Contrastive Information Bottleneck for Multi-Behavior Recommendation
Likang Wu, Zihao Chen, Jianxin Zhang +4
With the rapid emergence of multi-behavior learning in recommender systems, leveraging auxiliary user behaviors has proven effective for mitigating target-behavior data sparsity. Y…
Missing Old Logits in Asynchronous Agentic RL: Semantic Mismatch and Repair Methods for Off-Policy Correction
Zhong Guan, Yongjian Guo, Haoran Sun +5
Asynchronous reinforcement learning improves rollout throughput for large language model agents by decoupling sample generation from policy optimization, but it also introduces a c…
RL-VLA: A Flexible and Asynchronous Reinforcement Learning Framework for VLA Training
Haoran Sun, Yongjian Guo, Zhong Guan +13
Reinforcement learning (RL) has emerged as a critical paradigm for post-training Vision-Language-Action (VLA) models, enabling embodied agents to adapt and improve through environm…
Reinventing Clinical Dialogue: Agentic Paradigms for LLM Enabled Healthcare Communication
Xiaoquan Zhi, Hongke Zhao, Likang Wu +2
Clinical dialogue represents a complex duality requiring both the empathetic fluency of natural conversation and the rigorous precision of evidence-based medicine. While Large Lang…
Hierarchical Semantic RL: Tackling the Problem of Dynamic Action Space for RL-based Recommendations
Minmao Wang, Xingchen Liu, Shijie Yi +5
Recommender Systems (RS) are fundamental to modern online services. While most existing approaches optimize for short-term engagement, recent work has begun to explore reinforcemen…
Recall-Extend Dynamics: Enhancing Small Language Models through Controlled Exploration and Refined Offline Integration
Zhong Guan, Likang Wu, Hongke Zhao +2
Many existing studies have achieved significant improvements in the reasoning capabilities of large language models (LLMs) through reinforcement learning with verifiable rewards (R…
Attention Mechanisms Perspective: Exploring LLM Processing of Graph-Structured Data
Zhong Guan, Likang Wu, Hongke Zhao +2
Attention mechanisms are critical to the success of large language models (LLMs), driving significant advancements in multiple fields. However, for graph-structured data, which req…
GANPrompt: Enhancing Robustness in LLM-Based Recommendations with GAN-Enhanced Diversity Prompts
Xinyu Li, Chuang Zhao, Hongke Zhao +2
In recent years, Large Language Models (LLMs) have demonstrated remarkable proficiency in comprehending and generating natural language, with a growing prevalence in the domain of…
LANE: Logic Alignment of Non-tuning Large Language Models and Online Recommendation Systems for Explainable Reason Generation
Hongke Zhao, Songming Zheng, Likang Wu +2
The explainability of recommendation systems is crucial for enhancing user trust and satisfaction. Leveraging large language models (LLMs) offers new opportunities for comprehensiv…
LangTopo: Aligning Language Descriptions of Graphs with Tokenized Topological Modeling
Zhong Guan, Hongke Zhao, Likang Wu +2
Recently, large language models (LLMs) have been widely researched in the field of graph machine learning due to their outstanding abilities in language comprehension and learning.…
Enhancing Collaborative Semantics of Language Model-Driven Recommendations via Graph-Aware Learning
Zhong Guan, Likang Wu, Hongke Zhao +2
Large Language Models (LLMs) are increasingly prominent in the recommendation systems domain. Existing studies usually utilize in-context learning or supervised fine-tuning on task…
From a Social Cognitive Perspective: Context-aware Visual Social Relationship Recognition
Shiwei Wu, Chao Zhang, Joya Chen +4
People's social relationships are often manifested through their surroundings, with certain objects or interactions acting as symbols for specific relationships, e.g., wedding ring…
Multi-View Empowered Structural Graph Wordification for Language Models
Zipeng Liu, Likang Wu, Ming He +3
Significant efforts have been dedicated to integrating the powerful Large Language Models (LLMs) with diverse modalities, particularly focusing on the fusion of language, vision an…
END4Rec: Efficient Noise-Decoupling for Multi-Behavior Sequential Recommendation
Yongqiang Han, Hao Wang, Kefan Wang +6
In recommendation systems, users frequently engage in multiple types of behaviors, such as clicking, adding to a cart, and purchasing. However, with diversified behavior data, user…
A Cross-View Hierarchical Graph Learning Hypernetwork for Skill Demand-Supply Joint Prediction
Wenshuo Chao, Zhaopeng Qiu, Likang Wu +4
The rapidly changing landscape of technology and industries leads to dynamic skill requirements, making it crucial for employees and employers to anticipate such shifts to maintain…
APGL4SR: A Generic Framework with Adaptive and Personalized Global Collaborative Information in Sequential Recommendation
Mingjia Yin, Hao Wang, Xiang Xu +7
The sequential recommendation system has been widely studied for its promising effectiveness in capturing dynamic preferences buried in users' sequential behaviors. Despite the con…
KMF: Knowledge-Aware Multi-Faceted Representation Learning for Zero-Shot Node Classification
Likang Wu, Junji Jiang, Hongke Zhao +4
Recently, Zero-Shot Node Classification (ZNC) has been an emerging and crucial task in graph data analysis. This task aims to predict nodes from unseen classes which are unobserved…
Generative Job Recommendations with Large Language Model
Zhi Zheng, Zhaopeng Qiu, Xiao Hu +3
The rapid development of online recruitment services has encouraged the utilization of recommender systems to streamline the job seeking process. Predominantly, current job recomme…
Exploring Large Language Model for Graph Data Understanding in Online Job Recommendations
Likang Wu, Zhaopeng Qiu, Zhi Zheng +2
Large Language Models (LLMs) have revolutionized natural language processing tasks, demonstrating their exceptional capabilities in various domains. However, their potential for be…
Recognizing Unseen Objects via Multimodal Intensive Knowledge Graph Propagation
Likang Wu, Zhi Li, Hongke Zhao +5
Zero-Shot Learning (ZSL), which aims at automatically recognizing unseen objects, is a promising learning paradigm to understand new real-world knowledge for machines continuously.…
A Survey on Large Language Models for Recommendation
Likang Wu, Zhi Zheng, Zhaopeng Qiu +9
Large Language Models (LLMs) have emerged as powerful tools in the field of Natural Language Processing (NLP) and have recently gained significant attention in the domain of Recomm…