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

The rapid development of information technology has made search engines the primary way people obtain information. Traditional video search ranking has relied on a multi‑stage cascade framework (recall → coarse ranking → fine ranking), where each stage is modeled independently on relevance, quality, freshness, and click‑through rate.

With the maturity of pre‑trained large models such as BERT, ERNIE, and GPT, an end‑to‑end solution for information retrieval has become feasible. At the same time, user demands for personalized, diverse, and deep information have increased, driving higher compute requirements.

Background : Over the past decade the dominant architecture was a tightly coupled multi‑stage cascade. This coupling caused high development cost, stability issues, and limited the speed of new product rollout.

Goal : Build a high‑performance, flexible video search ranking framework centered on large‑model technology, while reducing the entropy of the existing system and lowering long‑term operational costs.

Key Challenges :

Architectural coupling – ranking, recall, and summarization functions are intertwined, hindering efficiency and maintainability.

System efficiency – lack of a flexible parallel computing framework limits resource utilization, especially during low‑traffic periods.

End‑to‑end evolution – how to transition from a multi‑stage cascade to a single‑stage model‑driven architecture.

Proposed Solutions :

Decoupling core ranking functions : Separate the recall processing from the core ranking module, creating an independent recall component.

Flexible parallel framework : Adopt a graph‑engine‑based execution engine that supports serial, parallel, and data‑parallel modes, enabling both query‑level and item‑level parallelism.

Elastic compute allocation : Introduce a global elastic‑compute control center that dynamically adjusts resource distribution based on cluster metrics, improving utilization during traffic peaks and valleys.

End‑to‑end architecture upgrade : Consolidate coarse and fine ranking into a unified stage, expand the candidate set for the end‑to‑end model, and integrate the Rankflow framework to handle high‑concurrency candidate processing.

These measures lead to a more modular, scalable, and maintainable video ranking system. The new architecture provides richer result sets, supports personalized ranking modules, and enables efficient summarization of top‑N results.

Conclusion and Outlook : The evolved framework significantly improves performance, flexibility, and development efficiency while reducing long‑term operational costs. Future work includes exploring RAG‑enhanced search capabilities with large models and achieving full end‑to‑end integration between video and universal search.