This talk explores how the integration of Large Language Model reasoning capabilities is fundamentally transforming industry-scale recommendation systems, shifting from traditional pattern-matching approaches to cognitive frameworks that deliver unprecedented personalization and transparency.

Recent research demonstrates that LLM reasoning, particularly through Chain-of-Thought (CoT) prompting, significantly enhances recommendation quality by addressing the inherent subjectivity and personalization challenges of recommendation tasks. Rather than simply matching users to items based on statistical correlations, LLMs now employ sophisticated reasoning processes to assess user preferences and generate appropriately ranked recommendations. They also provide transparent explanations that elucidate their suggestions in natural language, dramatically improving system clarity.

The presentation will delve into recent industry implementations and research developments from early 2025 and look at how they have leveraged LLM-powered reasoning to enhance recommendation quality through techniques such as query understanding, metadata enrichment, and innovative evaluation frameworks. We'll examine how multi-agent LLM orchestration enables collaborative reasoning processes where models share insights to generate more accurate conclusions, and explore key paradigms emerging in current research.

The talk will conclude with insights on how LLM reasoning is opening new frontiers for personalization without requiring curated gold references or human raters, pointing toward a future where recommendation systems don't just predict what users want but understand why they want it—ultimately creating more meaningful and effective user experiences.