137 citations · 239 across the 24 of their papers we have counts for
9 papers · 1 filter
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…
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…
Performative Debias with Fair-exposure Optimization Driven by Strategic Agents in Recommender Systems
Zhichen Xiang, Hongke Zhao, Chuang Zhao +2
Data bias, e.g., popularity impairs the dynamics of two-sided markets within recommender systems. This overshadows the less visible but potentially intriguing long-tail items that…
Cross-domain Transfer of Valence Preferences via a Meta-optimization Approach
Chuang Zhao, Hongke Zhao, Ming He +2
Cross-domain recommendation offers a potential avenue for alleviating data sparsity and cold-start problems. Embedding and mapping, as a classic cross-domain research genre, aims t…
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…