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CodeSignal

Diving Deep into Collaborative Filtering Techniques with ALS

via CodeSignal

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Overview

This course explores collaborative filtering techniques, which are central to modern recommendation systems. It covers both user-based and item-based collaborative filtering methods, as well as matrix factorization and the powerful Alternating Least Squares algorithm.

Syllabus

  • Unit 1: Exploring User-Item Explicit Rating Matrix
    • Loading Rating Matrix with NumPy
    • Adjust Missing Ratings Ratio
    • Handling Missing Ratings Randomly
    • Calculating Missing Data Proportions
  • Unit 2: Implementing the Alternating Least Squares Algorithm
    • Initialize and Verify Factor Matrices
    • Update User Factors with ALS
    • Test and Evaluate Your ALS Predictions
  • Unit 3: Understanding Implicit Feedback in Recommendation Systems
    • Building a Binary Interaction Matrix
    • Update Confidence with Logarithmic Scaling
    • Matrix Initialization from JSON Data
    • Normalize Watch Time for Certainty
    • Interpreting User Engagement Data
  • Unit 4: Implementing Implicit Alternating Least Squares (IALS)
    • Create Preference and Confidence Matrices
    • Completing the Matrix Update Function
    • Top 5 Recommended Items
  • Unit 5: Evaluating IALS Predictions Quality
    • Adjust Recommendations for Worse Metric
    • Create Normalized Item Rankings
    • Complete the Mean Rank Calculation
    • Evaluating Two User Recommendations

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