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CodeSignal

Recommendation Systems Quality Evaluation using JS

via CodeSignal

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Overview

This course focuses on metrics specific to recommendation systems, crucial for evaluating and optimizing model performance. You'll delve into recommendation-specific metrics such as Coverage, Serendipity, Novelty, and Diversity. Each metric is presented with theoretical insights and practical coding examples to illustrate their application.

Syllabus

  • Unit 1: Coverage in Recommendation Systems
    • Enhancing Recommendation Diversity
    • Implementing a Coverage Function for Recommendation Systems
    • Model Coverage Calculation Task
    • Calculate Coverage for Recommendation System
  • Unit 2: Understanding Novelty Metrics
    • Increase Novelty Score by Modifying Predicted Items List
    • Calculating Novelty Scores for Multiple Users
    • Calculating Average Novelty Score
    • Item Popularity and Novelty Calculation Task
  • Unit 3: Diversity in Recommendation Systems
    • Cosine Similarity Calculation for Item Recommendations
    • Calculating Cosine Similarity for Recommended Items
    • Implementing Diversity Function in Recommendation Systems
    • Reducing Diversity in Recommendations
    • Enhancing Diversity in Item Recommendations
  • Unit 4: Serendipity in Recommendations
    • Enhancing Serendipity in Recommendation Systems
    • Serendipity Score Calculation Task
    • Serendipity Score Calculation for Multiple Users

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