Entity Annotation System for Automatic Content Matching
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Solution Overview
Problem
Content providers face challenges in manually linking advertisements with relevant additional content items, such as videos or social network information, to enhance user experience and increase conversion rates, due to difficulties in automatically matching and annotating these items based on their entities.
Innovation Solution
A computer-implemented method and system that automatically annotates first and second content items with entities, compares their annotations, and displays the annotated first content item alongside the second content item to users, facilitating the integration of relevant additional content with advertisements based on entity matching.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If content providers manually link advertisements with relevant additional content items, then user experience and conversion rates are enhanced, but the process becomes time-consuming and labor-intensive
Solution Approach 1:
The system enables automatic content matching through self-service mechanisms where the computer automatically annotates content items with entities, compares annotations, and links relevant content without human intervention. The processor autonomously performs entity extraction, annotation comparison, and content matching, eliminating the need for manual content pairing while maintaining high relevance quality.
Solution Approach 2:
The patent replaces the mechanical manual linking process with an automated computational system. Instead of human operators manually identifying and linking relevant content, the system uses entity annotation and comparison algorithms to automatically match advertisements with relevant content items, substituting human cognitive work with machine-based semantic analysis.
2Ease of operation
If automated entity matching is implemented to link content items, then manual effort is reduced, but the complexity of the annotation and comparison system increases
Solution Approach 1:
The complex content matching task is segmented into distinct operational phases: entity annotation phase where content items are tagged with entities, comparison phase where annotations are evaluated for similarity, and selection phase where matching content is identified. This segmentation breaks down the complex automated process into manageable, modular steps that can be independently optimized and maintained.
Solution Approach 2:
Entity annotations serve as an intermediary layer between the raw content items and the matching logic. Instead of directly comparing content items, the system first transforms them into standardized entity annotations, which then facilitate efficient comparison and matching. This intermediary representation simplifies the overall system architecture by providing a common framework for diverse content types.
Data Source
AI summary
Methods and systems for providing content for display including receiving a first content item, receiving a second content item, automatically annotating the first content item and the second content item with an entity, comparing the entity annotation of the first content item and the entity annotation of the second content item, and, based on the comparison, displaying the annotated first content item with the second content item to a user.