How GenAI is Reshaping the Way We Build Recommendation Systems: A Developer’s Perspective

Author(s): Vikram Bhat

Originally published on Towards AI.

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As someone who’s worked on building recommendation systems for a few years, I’ve 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.

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Published via Towards AI