arXiv:2601.22925v3 Announce Type: replace-cross Abstract: Recent years have seen a rapid surge in research leveraging Large Language Models (LLMs) for recommendation. These methods typically employ supervised fine-tuning (SFT) to adapt LLMs to recommendation scenarios, and utilize beam search during inference to efficiently retrieve $B$ top-ranked recommended items. However, we identify a critical training-inference inconsistency: while SFT optimizes the overall probability of positive items, it does not guarantee that such items will be retrieved by beam search even if they possess high overa
The rapid ascent of LLMs in recommendation systems has highlighted practical inference challenges, making research into optimizing their real-world performance timely.
Improving the efficiency and effectiveness of LLM-based recommendation systems directly impacts user experience and the profitability of digital platforms.
This research could lead to more accurate and efficient LLM-powered recommendation systems, bridging a critical gap between training optimization and inference retrieval mechanisms.
- · E-commerce platforms
- · Social media companies
- · AI/ML researchers
- · Cloud providers
- · Legacy recommendation systems
- · High-latency content providers
More relevant and personalized user experiences across various digital platforms.
Increased user engagement and potential for higher conversion rates due to improved recommendations.
Further entrenchment of AI as a core component of digital commerce and content consumption, potentially leading to new business models built around hyper-personalization.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG