Overview of Information Retrieval, Retrieval Models, Search and Filtering Techniques: Relevance Feedback, User Profiles, Recommender system functions, Matrix operations, covariance matrices, Understanding ratings, Applications of recommendation systems ,Issues with recommender system.

High level architecture of content-based systems, Advantages and drawbacks of content based filtering, Item profiles, Discovering features of documents, pre-processing and feature extraction, Obtaining item features from tags, Methods for learning user profiles, Similarity based retrieval, Classification algorithms

User-based recommendation, Item – based recommendation, Model based approaches, Matrix factorization, Attacks on collaborative recommender systems.

Opportunities for hybridization, Monolithic hybridization design : Feature combination, Feature augmentation, Parallelized hybridization design: Weighted, S witching, Mixed, Pipeline hybridization design: Cascade Meta-level, Limitations of hybridization strategies

Introduction, General properties of evaluation research, Evaluation designs: Accuracy, Coverage, confidence, novelty, diversity, scalability, serendipity, Evaluation on historical datasets, Offline evaluations.