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CodeSignal

Recommendation Systems Foundations in C++

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
    • Adding a New User for Analysis
    • Complete the Matrix Prediction Code
    • Predicting Ratings for Classical Music
  • Unit 2: Pearson Correlation in Recommendation Systems
    • Calculating Mean User Ratings
    • Calculate Differences for User Ratings
    • Debug the Pearson Correlation Calculation
    • Calculate User Similarity
    • Create Pearson Correlation Function
  • Unit 3: Weighted Recommendations with 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

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