Media — Named Entity Extraction for Content Personalization

Overview

The purpose of this DAG is to extract named entities from multimedia content, which is crucial for personalizing user experiences in media applications. The data sources include content databases and various APIs that provide multimedia content. The ingestion pipeline starts with data retrieval, followed by named entity extraction, normalization of the extracted entities, and finally storing the results in a data warehouse. Quality control measures are implemented at each stage to ensure data in

The purpose of this DAG is to extract named entities from multimedia content, which is crucial for personalizing user experiences in media applications. The data sources include content databases and various APIs that provide multimedia content. The ingestion pipeline starts with data retrieval, followed by named entity extraction, normalization of the extracted entities, and finally storing the results in a data warehouse. Quality control measures are implemented at each stage to ensure data integrity, including validation checks and error logging. The outputs are exposed through a dedicated API, facilitating seamless integration with recommendation systems. Key performance indicators (KPIs) such as precision and recall are monitored to assess the effectiveness of the entity extraction process. The business value lies in enhanced content personalization, leading to improved user engagement and satisfaction, ultimately driving revenue growth.

Part of the SOPs & Playbooks solution for the Media industry.

Use cases

• Improves content relevance through personalized recommendations.
• Enhances user engagement by tailoring experiences.
• Increases operational efficiency with automated entity extraction.
• Supports data-driven decision-making for content strategies.
• Drives revenue growth through targeted advertising opportunities.