AI RESEARCH
What Makes LLMs Effective Sequential Recommenders? A Study on Preference Intensity and Temporal Context
arXiv CS.LG
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ArXi:2506.02261v3 Announce Type: replace-cross What enables large language models (LLMs) to effectively model user preferences in sequential recommendation? Our investigation reveals that existing preference-alignment approaches largely rely on binary pairwise comparisons, overlooking two critical factors: preference intensity (the structured strength of affinity or aversion) and temporal context (the extent to which recent interactions better reflect a user's current intent