AI RESEARCH
Diagnosing LLM-based Rerankers in Cold-Start Recommender Systems: Coverage, Exposure and Practical Mitigations
arXiv CS.CL
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ArXi:2604.16318v1 Announce Type: cross Large language models (LLMs) and cross-encoder rerankers have gained attention for improving recommender systems, particularly in cold-start scenarios where user interaction history is limited. However, practical deployment reveals significant performance gaps between LLM-based approaches and simple baselines. This paper presents a systematic diagnostic study of cross-encoder rerankers in cold-start movie recommendation using the Serendipity-2018 dataset.