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Consumer Products — Feature Preparation for Personalized Recommendation Systems

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This DAG prepares features from customer and product data to enhance recommendation model performance. It ensures data quality and accessibility through a structured ETL process.

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Overview

The purpose of this DAG is to prepare features essential for personalized recommendation systems in the consumer products industry. It ingests data from various sources, including customer interaction logs, product catalogs, and sales data. The ingestion pipeline consists of extracting relevant data, transforming it into a usable format, and loading it into a feature store for model training. Key processing steps include data cleaning, feature engineering, and normalization to ensure that the da

The purpose of this DAG is to prepare features essential for personalized recommendation systems in the consumer products industry. It ingests data from various sources, including customer interaction logs, product catalogs, and sales data. The ingestion pipeline consists of extracting relevant data, transforming it into a usable format, and loading it into a feature store for model training. Key processing steps include data cleaning, feature engineering, and normalization to ensure that the data is consistent and reliable. Quality control measures are implemented throughout the pipeline to validate the accuracy and completeness of the data, ensuring it meets the necessary standards for model training. The final outputs are exposed via a RESTful API, allowing seamless access for downstream applications. Monitoring key performance indicators (KPIs) such as data freshness, feature distribution, and model performance metrics is crucial for maintaining the efficiency of the recommendation system. Ultimately, this DAG adds significant business value by improving the relevance of product recommendations, enhancing customer satisfaction, and driving sales growth.

Part of the Recommendations solution for the Consumer Products industry.

Use cases

  • Enhanced customer engagement through personalized recommendations.
  • Increased sales conversion rates driven by relevant suggestions.
  • Improved data-driven decision-making across marketing strategies.
  • Faster time-to-market for new product recommendations.
  • Higher customer retention rates due to tailored shopping experiences.

Technical Specifications

Inputs

  • • Customer interaction logs
  • • Product catalog data
  • • Sales transaction records
  • • Customer demographic information
  • • Website analytics data

Outputs

  • • Processed feature sets for recommendation models
  • • Quality assessment reports
  • • API endpoints for feature access
  • • Data freshness metrics
  • • Feature distribution summaries

Processing Steps

  1. 1. Extract customer interaction logs and product data.
  2. 2. Transform raw data through cleaning and normalization.
  3. 3. Engineer features relevant to recommendation algorithms.
  4. 4. Conduct data quality checks and validation.
  5. 5. Load processed features into the feature store.
  6. 6. Expose features via a RESTful API for access.
  7. 7. Monitor KPIs to ensure data quality and model performance.

Additional Information

DAG ID

WK-0578

Last Updated

2025-12-12

Downloads

27

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