AI-Powered Product Recommendation Engine for E-Commerce
Real-world project · AICTE-aligned · AI-graded · Audit-ready certificate
About this project
Build a hybrid recommendation system for an e-commerce catalogue using collaborative filtering (user-item matrix factorization) and content-based filtering (product embeddings). Expose recommendations via FastAPI; A/B compare the strategies on a sample MovieLens or Amazon Reviews dataset.
Course Learning Outcomes (CLOs):
CLO1: Apply collaborative filtering using matrix factorization (SVD).
CLO2: Build content-based recommendations using sentence embeddings.
CLO3: Design a REST API that serves model predictions.
CLO4: Evaluate recommender quality using Precision@K and NDCG.
CLO5: Reason about hybrid strategies and cold-start problems.
Industry/societal relevance: Recommender systems power Flipkart, Myntra, Swiggy, Hotstar — directly placement-relevant.
Milestones
Skills you'll learn
Tools used
Prerequisites
Available mentors
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Be the first to mentorYou'll earn — Certificate (PDF)
AICTE-aligned Project Completion Certificate
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AICTE-aligned
Certificate of Project Completion
This is to certify that
has successfully completed the project
AI-Powered Product Recommendation Engine for E-Commerce
You'll earn — Digital Badge
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