How GenAI is Reshaping the Way We Build Recommendation Systems: A Developers Perspective 0 like December 21, 2024Share this postLast Updated on December 21, 2024 by Editorial TeamAuthor(s): Vikram Bhat Originally published on Towards AI. This member-only story is on us. Upgrade to access all of Medium.As someone whos worked on building recommendation systems for a few years, Ive witnessed the dramatic shift in tools, workflows, and paradigms firsthand. Back in 2019, building recommendation systems required a lot of manual effort, fragmented tools, and custom code. Fast forward to end of 2024, Generative AI and modern libraries have completely transformed the landscape, making development faster, more intuitive, and far more scalable.In 2019, building a recommendation system involved a lot of manual coding and iteration. Let me walk you through a typical workflow I followed back then:Data Collection & Cleaning: I relied heavily on Pandas and SQL for data cleaning, merging, and feature extraction. Tasks like splitting timestamps for session analysis or encoding categorical variables had to be scripted manually.Model Building: I would use Scikit-learn or XGBoost for collaborative filtering and content-based methods. Training involved long cycles of feature engineering everything from creating TF-IDF vectors for text features to manually generating embeddings. For deep learning, I used TensorFlow 1.x, which was powerful but complex to debug due to static graph definitions.Model Tuning: Hyperparameter tuning was slow and mostly manual. I used grid search or random Read the full blog for free on Medium.Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming asponsor. Published via Towards AITowards AI - Medium Share this post