Syllabus

Introduction to Recommender Systems, Eliciting Ratings and other Feedback Contributions, Implicit Ratings ,Linear Algebra notation: Matrix addition, multiplication, transposition, and inverses covariance matrices, Taxonomy of Recommender Systems, Non-Personalized Recommenders Content-Based Recommenders, Collaborative Filtering- User-User Collaborative Filtering, Evaluation Item Based Collaborative Filtering, Evaluation, Dimensionality Reduction, Advanced Topics: Matrix Factorization, Diversity and Accuracy trade-off, Factorizing Machines.

• To provide students with basic concepts and its application in various domain.

• To make the students understand different techniques that a data scientist needs to know for analyzing big data.

• To design and build a complete machine learning solution in many application domains.

• Aware of various issues related to Personalization and Recommendations.
• Design and implement a set of well-known Recommender System approaches used in E-commerce and Tourism industry.
• Develop new Recommender Systems for a number of domains especially, Education, Health-care.

1. Francesco Ricci , Lior Rokach , Bracha Shapira, 1. Francesco Ricci , Lior Rokach , Bracha Shapira, Springer 2011 edition

2. Dietmar Jannach, Markus Zanker, Alexander Felfernig, Recommender Systems: An Introduction, , Cambridge University Press 1 edition (September 30, 2010)

3. M.D. Ekstrand, J.T. Riedl, J.A. Konstan, Collaborative filtering recommender systems, xxxxx

X. Su, T.M. Khoshgoftaar, A survey of collaborative filtering techniques, Adv. Artif. Intell., 2009 (2009), p. 4:2

2. Y. Koren, Factorization meets the neighborhood: a multifaceted collaborative filtering model, in: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2008, pp. 426–434.