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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 a Rating Matrix from a Text File
    • Adjusting the Missing Data Ratio and Printing Missing Indices
    • Marking Ten Percent of Ratings as Missing for Testing
    • Calculating Proportions of Missing and Non-Missing Data in a Recommendation Dataset
  • Unit 2: Alternating Least Squares Fundamentals
    • Matrix Initialization and Shape Verification for ALS
    • Implementing User Factor Updates in ALS
    • Testing and Evaluating ALS with Held-Out Ratings
  • Unit 3: Implicit Feedback Matrices
    • Building a Binary Interaction Matrix from JSON Data
    • Implement Logarithmic Scaling for Certainty Matrix Calculation
    • Calculate Interaction and Certainty Matrices from User Data
    • Normalizing Watch Time Using Item Lengths
    • Filtering Interactions Based on Normalized Watch Time
  • Unit 4: Implementing Implicit ALS
    • Constructing Preference and Confidence Matrices for IALS
    • Implement User Feature Update for Implicit Feedback Recommendation System
    • Generating Personalized Recommendations from the Prediction Matrix
  • Unit 5: Evaluating Recommendation Quality
    • Make the Mean Rank Worse by Adjusting Recommendations
    • Calculating Normalized Rankings for Recommended Items
    • Implementing Mean Rank Calculation for Recommendations
    • Calculating Mean Rank for Multiple Users in a Recommendation System

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