Machine Learning
38 learners
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
4 lessons
16 practices
3 hours
Model Validation and Selection
Lessons and practices
Increasing Recommendation Diversity
Implement Coverage Function from Scratch
Visualize Model Coverage Effectively
Calculate Coverage with XGBoost
Boost Your Recommendation Novelty
Calculate Novelty for Multiple Users
Calculating Average Novelty Scores
Calculate Novelty with Item Popularity
Calculate Cosine Similarities Easily
Calculate Cosine Similarity Efficiently
Calculate Recommendation Diversity Score
Decrease Diversity in Recommendations
Enhance Dataset for Better Diversity
Boost Your Recommendation Skills
Complete the Serendipity Function
Calculating Serendipity for Multiple Users
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