Article Content Matching Using Domain Vectors and Temporal Relevance
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Solution Overview
Problem
Existing systems fail to provide multimedia content directly linked to the topic of an article, leading to poor user engagement as recommended videos often discuss the same domain but not the specific event mentioned in the article.
Innovation Solution
A method and system that uses domain-specific language models to identify and embed additional multimedia content related to the event in the article, utilizing smart tags and relevance models to ensure temporal proximity and relevance, thereby enhancing user engagement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If recommended videos are served to users based on high engagement or market sector, then video views increase, but user engagement with the article topic decreases
Solution Approach 1:
The patent introduces an intermediary system that uses natural language processing and domain-specific language models to match articles with relevant multimedia content. This intermediary layer analyzes the semantic content of articles and connects them to appropriate videos or images, ensuring that recommended content is directly related to the article topic rather than just based on broad market sectors or engagement metrics alone.
Solution Approach 2:
The system changes the parameters used for content recommendation from simple engagement metrics and market sector categorization to a more sophisticated approach using domain-specific language models, temporal proximity analysis, and semantic relevance scoring. This parameter transformation enables more precise matching between articles and multimedia content.
2Reliability
If multimedia content is manually curated for articles, then content relevance improves, but time and resource consumption increases
Solution Approach 1:
The patent implements a self-service system where multimedia content is automatically matched to articles through domain-specific language models and semantic analysis. The system autonomously processes articles, identifies relevant topics and entities, queries databases for matching content, and ranks results without human intervention, enabling scalable automated content curation while maintaining high relevance.
Solution Approach 2:
The manual mechanical process of content curation is replaced with an automated computational system using natural language processing, domain-specific language models, and database querying. This substitution eliminates the need for manual review and selection while maintaining or improving content matching accuracy through systematic algorithmic analysis.
3Reliability
If visual content search is performed across the entire corpus, then comprehensive results are obtained, but search efficiency decreases
Solution Approach 1:
The patent segments the search process into distinct stages: domain identification, article type classification using smart tags, semantic content analysis, and targeted database querying. By dividing the search into these segments, the system can apply appropriate filtering and analysis methods at each stage, reducing the overall search space while maintaining comprehensive results.
Solution Approach 2:
The system performs preliminary actions by first identifying the domain and article type before conducting the full content search. Domain-specific language models are pre-trained and ready, and smart tags are pre-established for different article types. This preliminary preparation enables faster, more targeted searches without sacrificing comprehensiveness.
Data Source
AI summary
The present disclosure relates to systems, devices, and methods for identifying additional content for an article. The systems, devices, and methods may identify a domain for the articles and content and may use machine learning models to classify the articles and the content into categories using smart tags for the domain. The systems, devices, and methods may convert the articles and the content into document vectors using a pre-trained domain specific language model and generate a relevance score for the articles and the content using the document vectors. The systems, devices, and methods may generate a list of predicted matches that includes content that is similar to the article based on the relevance score. The systems, devices, and methods may filter the list of predicted matches based on a temporal proximity to generate a list of additional content for the article.