AT A GLANCE
A robust recommendation system was developed to enhance user engagement by generating personalized, real-time article suggestions using advanced data pipelines, machine learning models, and automated updates, improving click-through rates and session durations.
Client information
CHALLENGE
The client required a robust article recommendation system to suggest relevant content based on user behavior, aiming to increase website engagement, grow their user base, and retain long-term subscribers. With millions of users generating data daily, leveraging big data tools was essential to process raw clickstream data and deliver personalized, timely recommendations.
SOLUTION
Raw user clickstream data was processed with Python and Spark to develop a suite of recommendation models, including traditional content-based filtering, collaborative filtering, and advanced deep learning approaches using BERT. The generated recommendations were seamlessly written to a NoSQL database for real-time access and automated through Databricks’ built-in job scheduler to ensure timely updates. Comprehensive A/B testing was conducted to evaluate and validate the improvements these models offered over the existing system, ensuring enhanced accuracy and user engagement.
IMPACT
TOOLS
Ready to achieve measurable results? Tell us about your challenges, and we’ll show you how we can help
