Universal Relevance Service Framework for Real-Time Ranking
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Solution Overview
Problem
Current relevance systems face inefficiencies due to separate code bases for batch and real-time systems, leading to duplicate efforts, over-diversification, high development and operational costs, and inability to scale gracefully with business growth, while also failing to support dynamic offerings with changing attributes in real-time.
Innovation Solution
A universal relevance service framework is implemented, providing a plug-in architecture that supports both real-time and batch processing, enabling seamless integration of evolving code, and allowing for horizontal scaling, with a continuous background computation model that decouples signal processing from servicing relevance computation requests, and uses attribute graphs and machine learning for personalized ranking and scoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If separate code bases are used for batch and real-time systems, then specific business use cases can be optimized, but development and operational costs increase and duplicate efforts occur
Solution Approach 1:
The patent merges batch and real-time processing capabilities into a single unified code base. The system uses a common processing framework that can handle both batch operations (for targeted electronic messaging) and real-time operations (for search queries) through the same code structure, eliminating duplicate code and reducing maintenance overhead while preserving optimization capabilities for different business use cases
Solution Approach 2:
The unified code base is designed to be universal and multi-functional, capable of performing both batch and real-time processing tasks. The system architecture allows a single code base to serve multiple business use cases through configurable processing modes and parameter settings, rather than requiring separate specialized code bases for each function
2Adaptability or versatility
If incremental improvements are made to each code base in response to short business focus, then specific business needs are met, but development and operational costs become high
Solution Approach 1:
By combining batch and real-time processing into one code base, incremental improvements can be made in a single location that benefit both processing modes simultaneously. This eliminates the need to make separate incremental improvements to multiple code bases, reducing development effort and costs while maintaining responsiveness to business needs
3Stability of the object's composition
If traditional relevance systems are used, then current operations are maintained, but the system cannot scale gracefully as business grows
Solution Approach 1:
The system employs dynamic architecture that can adapt and scale as business grows. The unified code base is designed with dynamic resource allocation and configurable processing parameters that allow the system to handle increasing workloads efficiently, maintaining operational stability while accommodating business growth through horizontal scaling capabilities
4Stability of the object's composition
If traditional relevance systems are used, then existing functionality is preserved, but dynamic offerings with changing attributes cannot be supported in real-time
Solution Approach 1:
The system uses dynamic processing capabilities that can handle real-time changes in offering attributes while maintaining stable core functionality. The unified code base processes dynamic data streams and updates relevance rankings in real-time based on changing attributes, preserving existing system stability while enabling real-time responsiveness to dynamic offerings
Data Source
AI summary
In general, embodiments of the present invention provide systems, methods and computer readable media for a universal relevance service framework for ranking and personalizing items.