Generic Online Ranking Service for Syndicated Content Prioritization
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Solution Overview
Problem
Many browse-and-search directories lack the traffic and resources to implement effective ranking capabilities, which are typically reserved for web search or advertisements, limiting their ability to present high-quality content to users.
Innovation Solution
A ranking service that allows multiple subscribers to exchange data using a predefined protocol, scoring and ranking resources based on user action data provided by each subscriber, without requiring access to subscriber-specific parameters beyond what is reported, enabling a generic service that can be syndicated across platforms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If dedicated computing resources are allocated for ranking capabilities in browse-and-search directories, then ranking quality and content presentation improve, but system cost and resource requirements increase
Solution Approach 1:
The ranking service is designed as a universal system that serves multiple subscribers and platforms through a common ranking infrastructure. Instead of each directory implementing its own ranking system, the service provides unified ranking capabilities across different content types and platforms, reducing overall resource requirements while maintaining ranking quality.
Solution Approach 2:
A centralized ranking service acts as an intermediary between content providers and users. The service receives user action data from various subscribers, processes ranking information through a common framework, and returns ranked results to requesting parties. This mediator approach eliminates the need for each directory to maintain dedicated ranking computing resources.
2Reliability
If subscriber-specific parameters are incorporated into ranking calculations, then ranking relevance to individual platforms improves, but system complexity and data processing requirements increase
Solution Approach 1:
The system allows each subscriber to provide parameters and weights that are specific to their local needs and content types. While the overall ranking framework is universal, each subscriber can customize their ranking criteria through locally-defined parameters, achieving relevant rankings for their specific platform without requiring the entire system to handle all possible parameter combinations.
Solution Approach 2:
The ranking system is segmented into a universal core that handles common ranking operations and subscriber-specific extensions that provide platform-customized parameters. This segmentation allows the system to maintain simplicity in the core processing while accommodating diversity in subscriber requirements through modular, plug-in style parameter definitions.
3Measurement precision
If comprehensive user action data is collected and processed for ranking, then content prioritization accuracy improves, but data processing time and computational cost increase
Solution Approach 1:
The system processes user action data in partial batches rather than requiring complete data collection before ranking. It can provide rankings based on available data and continuously update as new actions occur, reducing the time needed for initial processing while maintaining improving accuracy over time as more data becomes available.
Data Source
AI summary
Methods, systems, and apparatus, including computer program products, for providing ranking services. In one aspect, first signals are received from a first subscriber, and second signals are received from a second subscriber. The signals include information characterizing individual user actions relating to individual ones of first or second resources, respectively, values for one or more predefined parameters, and values for one or more first subscriber-specific parameters that are specific to the first or second subscriber, respectively. First ranking data is generated from the first signals, but not from values for any subscriber-specific parameter that is not a subscriber-specific parameter for the first subscriber. Second ranking data is generated from the second signals, but not from values for any subscriber-specific parameter that is not a subscriber-specific parameter for the second subscriber.