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CodeSignal

Recommendation Systems Foundations with Go

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
    • Expanding User-Item Rating Matrix with Missing Value Prediction
    • Implementing Item Average Rating Prediction System
    • Classical Music Recommendation System Item Average Prediction
  • Unit 2: Pearson Correlation in Go
    • Calculate Mean Ratings for Pearson Correlation
    • Calculate Rating Differences from Mean in Pearson Correlation
    • Debug Pearson Correlation Formula Implementation
    • Calculating User Similarity with Pearson Correlation
    • Implementing Pearson Correlation Function in Go
  • Unit 3: Weighted Recommendations in Go
    • User-Item Matrix Unique Count Enhancement
    • Adjusting User Ratings to Impact Prediction Outcomes
    • Implementing Pearson Similarity-Based Weighted Rating Prediction
    • Pearson Correlation Coefficient Calculation for User Pairs
    • Collaborative Filtering Video Rating Prediction
  • Unit 4: Adjusted Weighted Recommendations
    • Calculate User Average Rating from Matrix
    • Calculate User Average Ratings for Bias Analysis
    • Weighted Rating Prediction Implementation
    • Collaborative Filtering Rating Prediction Comparison

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