Machine Learning Specialization · Unsupervised Learning, Recommenders, Reinforcement Learning

Collaborative Filtering Recommender System

A movie recommender that learns latent movie and user vectors from a rating matrix, then predicts personalized preferences.

Matrix factorizationLatent vectorsRegularized cost functionTensorFlow GradientTapeMovie recommendations

GitHub

What this project is about

This project models recommendation as a latent-factor learning problem. Users and movies are represented by learned vectors, and the dot product plus bias predicts how much a user may like a movie. The interesting part is that the model can learn taste structure from sparse rating patterns.

Implementation focus

The lab made recommender systems feel like optimization: write the collaborative-filtering cost, add regularization, vectorize the computation, then use TensorFlow to learn the user/movie parameters.

Key results and artifacts

Ratings matrix: 4,778 movies × 443 users
Cost: 13.67; regularized cost: 28.09
Training loss fell from ~2,321,191 to ~2,902

What I learned

The important idea was that useful recommendations can emerge from shared rating patterns even before hand-designed content features are used.

Milestone deep dive

What this project proves in my learning path.

A movie recommender that learns latent movie and user vectors from a rating matrix, then predicts personalized preferences. The milestone is included because it shows a complete learning unit: a problem, a data representation, an implementation path, measurable outputs, and a lesson that later connects to larger AI systems.

Input layerusers, items, ratings, and feature vectors

Model layerlatent vectors or neural embedding towers

Evaluation layertests, metrics, plots, or qualitative output

Artifact layerpersonalized predictions and similarity scores

Problem framing

What I was trying to solve

This page is not just a lab description. It explains how I transformed the assignment idea into an engineering story: what the model receives, what it optimizes, what output it should produce, and how the result can be inspected.

Implementation

What I coded or assembled

The implementation focus was the mechanism behind the result: functions, model blocks, training logic, preprocessing, evaluation, and the small technical details that make the notebook work rather than only look correct.

Concepts

Core concepts

Matrix factorizationLatent vectorsRegularized cost functionTensorFlow GradientTapeMovie recommendations

These tags show the technical ideas this milestone added to my toolkit.

Learning result

What changed after it

The important idea was that useful recommendations can emerge from shared rating patterns even before hand-designed content features are used.

Important outputs and evidence

Ratings matrix: 4,778 movies × 443 users
Cost: 13.67; regularized cost: 28.09
Training loss fell from ~2,321,191 to ~2,902

How I would explain it in an interview

I would describe this milestone by starting from the data representation, then explain the model or algorithm, then show what was measured. The important part is not only the final number; it is the ability to reason about why the method works, what assumptions it makes, and what limitations should be improved next.