AI-Powered Recommendation Engine
A collaborative-filtering recommendation engine that generates personalized item recommendations from user behavior data.
Problem
Generic, non-personalized item ordering leaves relevant items buried, so recommendations need to be ranked per-user to be useful.
Why I built it
A hands-on project to learn the machine learning side of building recommendation systems, from data preprocessing through to evaluating recommendation quality.
Solution
A Python pipeline that preprocesses user behavior data, computes item similarity, and ranks recommendations per user, evaluated against offline metrics.
How it’s built
- Data preprocessing, similarity computation, and recommendation ranking using Python and scientific computing libraries.
- Evaluated recommendation quality using offline metrics and optimized the recommendation pipeline for faster inference.
Source is on GitHub.
Tech stack
Published June 1, 2024
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