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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: Introduction to Recommendation Systems
    • Loading Rating Matrix from Text File in Go
    • Adjusting Missing Data Ratio in Recommendation System Dataset
    • Implementing Missing Data Simulation for Recommendation System Testing
    • Calculating Missing Data Proportions in Explicit Ratings Dataset
  • Unit 2: Alternating Least Squares Recommendations
    • Matrix Initialization for ALS Algorithm
    • Implementing User Factor Updates in ALS Collaborative Filtering
    • ALS Algorithm Testing and Evaluation with RMSE Calculation
  • Unit 3: Implicit Feedback Matrices
    • Binary User-Item Interaction Matrix Calculator
    • Implementing Logarithmic Confidence Matrix Calculation
    • Collaborative Filtering Matrix Calculation Implementation
    • Normalizing Watch Time Data with Item Lengths in Go
    • Filtering User Interactions Based on Watch Time Threshold
  • Unit 4: Implementing IALS in Go
    • Implementing IALS Algorithm Preference and Confidence Matrices
    • Complete Matrix Factorization User Features Update
    • Implementing Personalized Recommendations with Top-K Item Selection
  • Unit 5: Mean Rank Evaluation
    • Optimizing Recommendation Rankings for Worse Mean Rank Performance
    • Calculate Normalized Rankings for Recommended Items
    • Mean Rank Calculation Implementation
    • Computing Mean Rank for Multiple Users in Recommendation Systems

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