AI-Powered Product Recommendation (ShopEasy)
Session- and content-based recommenders on a retail slice, offline hit-rate evals, and cold-start limitations spelled out explicitly.
What was my role?
I was the sole implementer for this coursework module: scoped requirements, wrote the code and experiments for “AI-Powered Product Recommendation (ShopEasy),” and produced the write-up with metrics and limitations.
Situation
Course lab for “AI-Powered Product Recommendation (ShopEasy)”: short deadlines, public or synthetic data, and rubrics that reward reproducible notebooks and honest limitations.
Task
Produce a small credible artifact—clean repo or notebook—with baselines, evaluation, and a crisp story of what would change in production.
Action
Implemented end-to-end (Session- and content-based recommenders on a retail slice, offline hit-rate evals, and cold-start limitations spelled out explicitly.), logged experiments, compared alternatives, and documented dependencies plus failure cases.
Result
Submitted a runnable deliverable with metrics, repeatable setup commands, and a trade-off section suitable for extending to real systems.