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CodeSignal

Recommendation Systems Foundations using JS

via CodeSignal

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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: Introduction to Recommendation Systems and Baseline Prediction in JavaScript
    • Add User4 and Predict ItemB Rating with Global Average
    • Global Item Average Predictor for Missing Ratings
    • Classical Music Global Average Rating Predictor
  • Unit 2: Introduction to Pearson Correlation for Measuring Similarity in Recommendation Systems with JavaScript
    • Calculate Mean Ratings
    • Implement Difference Calculation in Pearson Correlation Function
    • Correct the Pearson Correlation Function
    • Compute Pairwise User Similarity with Pearson Correlation
    • Implement Pearson Correlation Function in JavaScript
  • Unit 3: Predicting User Ratings with Weighted Averages and Pearson Similarity in JavaScript
    • Counting Unique Users and Items
    • Weighted Rating Prediction Using Pearson Similarity
    • Calculate Pearson Correlation Between All User Pairs
    • Predict User3 Missing Ratings via Weighted Pearson Collaborative Filtering
  • Unit 4: Adjusted Weighted Averages for Bias-Reduced Recommendations in JavaScript
    • Average User Rating Calculator
    • Compute and Display Each User's Average Rating
    • Adjusted Weighted Average Rating Prediction
    • Compare Raw vs Weighted Rating Predictions

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