26 citations · 104 across the 32 of their papers we have counts for
8 papers · 1 filter
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…