Class Central is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

CodeSignal

Recommendation Systems Foundations

via CodeSignal

Go to class Write review

Overview

This course introduces foundational algorithms and concepts that form the backbone of recommendation systems. You'll start with simple baseline prediction models and gradually advance to similarity measures and more sophisticated prediction models. Mastering these fundamentals is essential for developing robust and efficient recommendation tools.

Syllabus

  • Unit 1: Baseline Prediction Using Global Average
    • Adding a New User for Analysis
    • Complete the Matrix Prediction Code
    • Predicting Ratings for Classical Music
  • Unit 2: Similarity Measures: Pearson Correlation
    • Calculating Mean Ratings with Numpy
    • Calculate Differences for User Ratings
    • Fix Pearson Correlation Calculations
    • Calculate User Similarity in Python
    • Create Pearson Correlation Function
  • Unit 3: Rating Prediction Using Weighted Average and Pearson Similarity
    • Adding Unique Counts to Data Loader
    • Influence Ratings through User Similarity
    • Enhance Rating Predictions
    • Refine Your Pearson Correlation Algorithm
    • Predict User Ratings with Confidence
  • Unit 4: Improved Prediction Using Adjusted Weighted Average
    • Compute User's Average Rating
    • Uncover User Rating Biases
    • Predicting User Ratings with Precision
    • Comparing Prediction Approaches

Reviews

Start your review of Recommendation Systems Foundations

Go to class

Never Stop Learning.

Get personalized course recommendations, track subjects and courses with reminders, and more.

Sign up for free
Someone learning on their laptop while sitting on the floor.