Modifiable Video Search and Ranking in Multimedia Messaging

The search and ranking system is segmented into distinct functional modules: a search module that handles user queries, a ranking module that calculates relevance scores, and a video selection module that presents results. This modular architecture reduces overall system complexity by allowing each component to be developed and maintained independently while still providing efficient video search functionality.

A video database serves as an intermediary layer between the search interface and the video content. The database stores modifiable videos with associated metadata and handles the complex operations of searching, filtering, and ranking, thereby simplifying the user interface and reducing the complexity burden on the front-end system.

The ranking algorithm uses multiple adjustable parameters (relevance weight, popularity weight, freshness weight) that can be modified to change the ranking behavior. By parameterizing the complexity rather than hardcoding complex logic, the system achieves high video content relevance while keeping the algorithm structure relatively simple and maintainable.

The system pre-calculates and stores ranking-related metadata (such as popularity metrics and freshness timestamps) in the video database before actual search operations. This preliminary preparation reduces the computational complexity during real-time ranking by avoiding repeated complex calculations, thus achieving precise video content ranking efficiently.

Machine learning models are trained offline in advance to learn patterns in video content and user behavior. The trained models generate pre-computed features and predictions that are stored in the video database. During actual search operations, the system only needs to retrieve and combine these pre-computed results, significantly reducing computational processing time while maintaining high search accuracy.

The patent replaces complex real-time machine learning inference with simpler rule-based systems that use pre-computed features. Instead of running heavy computational models during each search, the system substitutes them with efficient queries and comparisons against pre-processed data, thereby reducing computational resource consumption and processing time while preserving search accuracy.