15 papers
Is ChatGPT Fair for Recommendation? Evaluating Fairness in Large Language Model Recommendation
Jizhi Zhang, Keqin Bao, Yang Zhang +3
The remarkable achievements of Large Language Models (LLMs) have led to the emergence of a novel recommendation paradigm -- Recommendation via LLM (RecLLM). Nevertheless, it is imp…
PAFO: Pareto Fairness Optimization for Personalized Reward Modeling
Xiaoyan Zhao, Haoting Ni, Yang Zhang +3
Large language models (LLMs) increasingly rely on reward models to align their outputs with diverse user preferences. While personalized reward models aim to capture such heterogen…
TRACER: Token ReAssignment for Concept ERasure in Generative Recommendation
Ziheng Chen, Jiali Cheng, Zezhong Fan +4
Generative recommendation formulates next-item prediction as autoregressive generation over semantic ID (SID) sequences derived from users' historical interactions, making modern r…
AlpsBench: An LLM Personalization Benchmark for Real-Dialogue Memorization and Preference Alignment
Jianfei Xiao, Xiang Yu, Chengbing Wang +8
As Large Language Models (LLMs) evolve into lifelong AI assistants, LLM personalization has become a critical frontier. However, progress is currently bottlenecked by the absence o…
Reinforced Latent Reasoning for LLM-based Recommendation
Yang Zhang, Wenxin Xu, Xiaoyan Zhao +4
Large Language Models (LLMs) have demonstrated impressive reasoning capabilities in complex problem-solving tasks, sparking growing interest in their application to preference reas…
Generative Multi-Target Cross-Domain Recommendation
Jinqiu Jin, Yang Zhang, Fuli Feng +1
Recently, there has been a surge of interest in Multi-Target Cross-Domain Recommendation (MTCDR), which aims to enhance recommendation performance across multiple domains simultane…