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cs.IR2026
Learning Robust Personalized Prompts for LLM-Driven Sequential Recommendation
Xiaolin Zheng, Qiyong Zhong, Jiajie Su +1
LLM-driven sequential recommendation formulates next-item prediction as autoregressive generation conditioned on natural-language prompts. However, minor wording changes in semanti…
cs.IR2026
Is Serving-Time Language-Model Inference Necessary for Recommendation? Evidence from Generative and Agentic Settings
Kong Wang, Zhongke He, Xiang Chen +4
Language models (LMs) are increasingly used in recommender systems to generate recommendations or coordinate recommendation tools. However, does their strong performance imply that…
cs.IR2024
X-Reflect: Cross-Reflection Prompting for Multimodal Recommendation
Hanjia Lyu, Ryan Rossi, Xiang Chen +4
Large Language Models (LLMs) have been shown to enhance the effectiveness of enriching item descriptions, thereby improving the accuracy of recommendation systems. However, most ex…