Machine Learning
Recommendation Systems Quality Evaluation
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.
Python
See path
4 lessons
16 practices
3 hours
Badge for Model Validation and Selection,
Model Validation and Selection
Lessons and practices
Lesson 1: Understanding and Calculating Coverage in Recommendation Systems
Increasing Recommendation Diversity
Implement Coverage Function from Scratch
Visualize Model Coverage Effectively
Calculate Coverage with XGBoost
Lesson 2: Novelty in Recommendation Systems
Boost Your Recommendation Novelty
Calculate Novelty for Multiple Users
Calculating Average Novelty Scores
Calculate Novelty with Item Popularity
Lesson 3: Diversity in Recommendation Systems
Calculate Cosine Similarities Easily
Calculate Cosine Similarity Efficiently
Calculate Recommendation Diversity Score
Decrease Diversity in Recommendations
Enhance Dataset for Better Diversity
Lesson 4: Serendipity Calculation in Recommendation Systems
Boost Your Recommendation Skills
Complete the Serendipity Function
Calculating Serendipity for Multiple Users
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