Third-Party Feature Vector Scoring for Content Ranking
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Solution Overview
Problem
Online systems lack the ability to effectively utilize third-party data to rank and select content for users, as they do not have access to user interaction data from third-party systems, which limits their ability to determine which users respond best to specific content.
Innovation Solution
The online system generates scores for content items and users based on feature vectors received from third-party systems, combining these scores using functions like dot product or Euclidean distance to estimate the value of presenting content to users, and iteratively improves these scores by coordinating with the third-party system to refine accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the online system uses only its own internal data to rank content, then it maintains data privacy and security, but it cannot effectively utilize third-party user interaction data to improve content ranking accuracy
Solution Approach 1:
The patent introduces feature vectors as an intermediary representation that captures user characteristics and content attributes without requiring direct access to raw third-party data. The online system receives pre-computed feature vectors from third-party systems, which serve as a mediator that translates third-party interaction data into a format usable by the online system's ranking algorithms, thus improving ranking accuracy while maintaining data boundaries
Solution Approach 2:
The patent segments the data processing into distinct components: third-party systems compute user feature vectors based on their own data, while the online system computes content feature vectors based on its own data. These segmented computations are then combined through dot product operations to generate ranking scores, allowing each system to operate within its data boundaries while achieving collaborative ranking improvement
2Adaptability or versatility
If the online system integrates third-party data sources to improve user profiling, then it enhances the ability to determine user content preferences, but it increases system complexity and data integration challenges
Solution Approach 1:
The patent transforms complex third-party user interaction data into simplified numerical feature vectors with fixed dimensions. Each user is represented by a vector of numerical features that capture their characteristics, and each content item is represented by a similar vector. This parameter transformation allows the system to handle diverse data sources through uniform mathematical operations (dot products), significantly reducing integration complexity while maintaining adaptability to different user preferences
Solution Approach 2:
The patent creates a universal feature vector representation that can accommodate multiple data sources and user characteristics. The same dot product-based scoring mechanism works across different third-party systems and content types, providing a multi-functional framework that handles diverse data integration scenarios through a single unified approach
Data Source
AI summary
An online system receives third party source data from a third party system including content feature vector entries and user feature vector entries, each content feature vector entry describing an corresponding user of the third party system, each component in each user feature vector related to a characteristic of the corresponding user. The online system generates a combination score for a target user and a selected content item by computing a combination of the content feature vector entry associated with the selected content item and the user feature vector entry associated with the target user using a combining function, the combination score indicating an estimated increase in value for the third party system when the target user is presented with the selected content item.