Lighthouse Related Product, An Efficient Cross-Boundary Recommendation Engine On Qubole (Fanatics)

What You’ll Learn:

1. Introduction to Fanatics’ Challenge:
   • Understand the unique challenge Fanatics faced in creating a product recommendation system for their vast array of online sports merchandise shops.
2. Lighthouse Recommendation System:
   • Discover how the Lighthouse recommendation system utilizes API calls across various platforms to enhance user experience.
3. Significant Improvements and Results:
   • Learn about the improvements made in the Lighthouse system, leading to a 9% click-through rate, significantly outperforming third-party providers.
4. Data Processing and Model Training:
   • Get insights into the data processing techniques and the model training processes employed to create the recommendation system.
5. Key Components and Features:
   • Explore the key components and features of the system, including collaborative filtering, supervised learning models, and the utilization of clickstream data.
6. Engineering and Architecture Insights:
   • Gain a deeper understanding of the engineering and architecture behind the recommendation system, highlighting the role of Qubole in facilitating data processing and analysis.
7. Code Deployment and Scheduling:
   • Learn about the practical aspects of deploying models on Spark clusters and scheduling ETL jobs using Qubole’s API.
8. Future Enhancements and Flexibility:
   • Discover the potential for future enhancements and the flexibility of the system to incorporate new features and personalization options.