Evolution of Video Search Ranking Architecture Towards an End‑to‑End Large‑Model Framework

With the rapid development of information technology, search engines have become the primary way people obtain information. The underlying ranking architecture has been evolving, especially under the demand of large‑model technologies such as BERT, ERNIE and GPT. This article describes the major changes in video search ranking frameworks in recent years, focusing on the transition from traditional multi‑stage cascade architectures to more efficient and flexible end‑to‑end ranking frameworks.

Background : Over the past decade, the mainstream framework for search engines has been a multi‑stage cascade consisting of recall, coarse ranking, and fine ranking. Each stage models relevance, quality, timeliness and click‑through rate independently, and the results are merged and truncated. The rise of pre‑trained large models makes an end‑to‑end solution increasingly feasible, while user demands for differentiated, diverse and deep information have driven up compute requirements.

Goal : Build a high‑performance, extensible video search ranking framework driven by large‑model technology, reduce the entropy of the existing system, and lower long‑term operation and maintenance costs.

Key Challenges :

Architectural coupling: the ranking architecture has become tightly coupled with strategy, product logic and infrastructure, hurting development efficiency and stability.

System efficiency: the core ranking module lacks a flexible parallel‑computing framework, leading to low resource utilization during traffic troughs.

End‑to‑end evolution: how to migrate the traditional multi‑stage cascade to a single‑stage model‑centric architecture.

Overall Approach :

Decouple core ranking functions by extracting recall and summary computation from the ranking module, creating a layered system.

Introduce a flexible framework that supports serial, parallel and data‑parallel execution, enabling fine‑grained parallelism for list‑wise and item‑wise sorting.

Build a global elastic‑compute control center that dynamically allocates resources based on cluster metrics, improving utilization during low‑traffic periods and preventing overload during peaks.

Upgrade the architecture to an end‑to‑end model‑centric pipeline (Rankflow), merging coarse and fine ranking into a unified stage and leveraging large‑model inference for final ranking.

Specific measures include:

Separate the recall stage into an independent module and migrate it to a new video‑recall service.

Upgrade the graph engine to support full parallel query execution, covering request construction, cache reads and result parsing.

Componentize and plugin‑ize common strategy functions for easier understanding and maintenance.

Introduce a personalized ranking module that receives more results from the core ranking and performs additional re‑ranking.

Design a data‑parallel mode that can handle multi‑list, multi‑item sorting scenarios, which traditional serial or simple parallel modes cannot satisfy.

Implement an elastic‑compute allocation center that periodically selects optimal strategy combinations based on machine load.

The redesigned framework, named Rankflow, provides high concurrency handling of candidate result sets, supports mixed list‑item sorting, and integrates elastic resource reuse. Visual diagrams (omitted here) illustrate the new pipeline and the elastic‑compute subsystem.

Summary and Outlook : The layered optimization, Rankflow integration and elastic resource reuse significantly improve the performance, flexibility and maintainability of the video search ranking system. Future research directions include extending RAG (retrieval‑augmented generation) capabilities for large‑model‑enhanced search and achieving seamless end‑to‑end integration between video and universal search.