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.
Reference Book:
1. JannachD., Zanker M. And FelFering A., Recommender Systems: An Introduction, Cambridge University Press (2011), 1stedition 2. CharuC. Aggarwal, Springer(2016), 1stedition. Recommender Systems: TheTextbook, 3. RicciF., RokachL., ShapiraD., KantorB.P., Recommender Systems Handbook, Springer(2011), 1 edition 4. Manouselis N., Drachsler H. Verbert K., Duval E., Recommender Systems For Learning, Springer(2013), 1st edition
Text Book:
1. JannachD., Zanker M. And FelFering A., Recommender Systems: An Introduction, Cambridge University Press (2011), 1stedition 2. CharuC. Aggarwal, Springer(2016), 1stedition. Recommender Systems: TheTextbook, 3. RicciF., RokachL., ShapiraD., KantorB.P., Recommender Systems Handbook, Springer(2011), 1 edition
