arXiv:2605.27429v1 Announce Type: cross Abstract: Industrial video-on-demand (VOD) recommenders need richer content understanding, but LLM-as-reranker designs repeat prompt construction, token generation, model invocation, output parsing, and fallback handling for each request. In high-volume latency-sensitive services, these request-time operations complicate throughput planning, tail-latency control, capacity isolation, and predictable operation. This paper presents Ocean4Rec, a reranking layer that uses an LLM only offline to materialize item OCEAN profiles from content metadata. Items are
The proliferation of LLMs in various applications necessitates solutions for their efficient deployment, especially in latency-sensitive services like VOD recommendations.
This development addresses a critical challenge in scaling LLM applications by decoupling intensive computations from real-time requests, enabling broader and more cost-effective integration.
The method shifts LLM processing for VOD reranking from real-time to offline, significantly improving throughput, latency, and operational predictability for high-volume services.
- · Video-on-demand platforms
- · Cloud service providers
- · LLM application developers
- · Legacy recommendation systems
- · Inefficient real-time LLM inference architectures
Reduced operational costs and improved user experience for VOD platforms leveraging LLMs.
Accelerated adoption of LLM-powered features in other high-volume, latency-sensitive consumer applications.
Enhanced competition among recommendation system providers, leading to further innovations in offline-first AI deployments.
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Read at arXiv cs.AI