Media — User Interaction Data Feature Engineering Pipeline

Overview

The purpose of this DAG is to create robust feature engineering pipelines from user interaction data, which is critical for enhancing recommendation systems in the media industry. The data sources include user activity logs, content engagement metrics, and demographic information. The ingestion pipeline begins with extracting raw data from these sources, followed by a series of transformation steps that clean and enrich the data. This includes normalization, encoding categorical variables, and a

The purpose of this DAG is to create robust feature engineering pipelines from user interaction data, which is critical for enhancing recommendation systems in the media industry. The data sources include user activity logs, content engagement metrics, and demographic information. The ingestion pipeline begins with extracting raw data from these sources, followed by a series of transformation steps that clean and enrich the data. This includes normalization, encoding categorical variables, and aggregating user interactions to create meaningful features. Quality control measures are implemented at various stages to ensure the relevance and reliability of the features, such as statistical checks and validation against predefined thresholds. The final outputs of this pipeline are structured datasets ready for machine learning model training, including feature sets that enhance content recommendation algorithms. Monitoring key performance indicators (KPIs) such as feature relevance scores and model performance metrics ensures continuous improvement of the pipeline. The business value lies in the ability to deliver personalized content recommendations, thereby increasing user engagement and satisfaction, ultimately driving revenue growth for media companies.

Part of the AI Assistants & Contact Center solution for the Media industry.

Use cases

• Increased user engagement through personalized content recommendations
• Enhanced efficiency in data processing and feature engineering
• Improved accuracy of recommendation algorithms
• Faster response to changing user preferences
• Higher revenue potential through targeted content delivery