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cs.IR2026
Auditing Semantic Gains in Sequential Recommendation: A Lightweight Recovery Test
Kong Wang, Zhongke He, Xiang Chen +4
Recent semantic and generative-retrieval recommenders report substantial improvements over ID-only sequential baselines, but it remains unclear whether these gains arise from langu…
cs.IR2025
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…