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, and Issues with a recommender system.

High-level architecture of content-based systems, Advantages, and drawbacks of content-based filtering, Itemprofiles, 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 HOD Board Chairman approaches, Matrix factorization, Attackson collaborative recommender systems.

Opportunities for hybridization, Monolithichybridization design: Feature combination, Feature augmentation, Parallelized hybridization design: Weighted, Switching, Mixed, Pipeline dhybridization design: Cascade Meta-level, Limitations of hybridization strategies

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