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CodeSignal

Collaborative Filtering Techniques with ALS using JS

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 and Displaying Rating Matrix from File Using JavaScript
    • Adjusting Missing Data Ratio in Dataset Preparation
    • Mark Ten Percent of Ratings as Missing in User-Item Matrix
    • Calculating Proportions of Missing and Non-Missing Data
  • Unit 2: Alternating Least Squares Collaborative Filtering
    • Matrix Initialization for ALS Algorithm
    • Updating User Factors in ALS Algorithm
    • Testing and Evaluation of ALS Algorithm Implementation
  • Unit 3: Implicit Feedback Matrices
    • Calculate Interaction Matrix from JSON Data
    • Logarithmic Scaling for Confidence Matrix Calculation
    • Matrix Calculation and Confidence Analysis Task
    • Normalizing Watch Time for Confidence Calculation
    • Refining Interaction Matrix with Normalized Watch Times
  • Unit 4: Implementing Implicit ALS
    • Enhancing IALS Algorithm with Preference and Confidence Matrices
    • Matrix Operations for User Feature Update
    • Personalized Recommendation System Enhancement
  • Unit 5: Evaluating Recommendation Quality
    • Adjusting Recommendation Rankings for Mean Rank Analysis
    • Calculating Normalized Rankings for Recommended Items
    • Mean Rank Calculation Implementation
    • Calculating Mean Rank for Multiple Users

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