RECOMMENDATION SYSTEMS
(MR22-1CS0158)
UNIT-1 :
Recommender Systems Function, Recommendation Techniques, Recommender Systems as a
Multi-Disciplinary Field, Challenges.
UNIT-2:
Basic Components of Content –Based Systems, Preprocessing and Feature Extraction, Learning
User Profiles and Filtering, Nearest Neighbor Classification.
UNIT-3:
User-Based collaborative filtering, Similarity Function Variants, Variants of the Prediction
Function, Item-Based Collaborative filtering, Comparing User-Based and Item-Based
Methods, Strengths and Weaknesses of Neighborhood-Based Methods.
UNIT-4:
Rule-Based Collaborative Filtering, Association Rules, Naive Bayes Collaborative Filtering,
Neural Network, Singular Value Decomposition, Stochastic Gradient Descent, Regularization.
UNIT-5:
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.
COURSE STRUCTURE
