Recommendation systems, Slides of Computer science

Recommendation systems subject unit-5

Typology: Slides

2025/2026

Available from 06/30/2026

poojitha-chougani
poojitha-chougani 🇮🇳

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General Goals of Evaluation Design: Accuracy, Coverage,
Confidence and Trust, Novelty, Serendipity, Diversity, Scalability,
Segmenting the Ratings for Training and Testing,
Accuracy Metrics in Offline Evaluation.
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General Goals of Evaluation Design: Accuracy, Coverage,

Confidence and Trust, Novelty, Serendipity, Diversity, Scalability,

Segmenting the Ratings for Training and Testing,

Accuracy Metrics in Offline Evaluation.

Scalability

Measuring the Accuracy of Ratings Prediction

  • (^) Many recommender systems predict numerical ratings (e.g., Netflix ratings from 1- stars).
  • (^) The quality of these predictions is assessed using metrics like RMSE and MAE. RMSE versus MAE

Evaluating Ranking via Correlation

  • (^) Instead of just predicting ratings, recommendation systems should rank items in the correct order.
  • (^) Ranking correlation measures how similar the predicted ranking is to the actual user preferences.

Evaluating Ranking via Utility