Course Details
Subject {L-T-P / C} : CS6430 : Recommender Systems { 3-0-0 / 3}
Subject Nature : Theory
Coordinator : Prof. Bibhudatta Sahoo
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.
Course Objectives
- • 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.
Course Outcomes
• Aware of various issues related to Personalization and Recommendations. <br />• Design and implement a set of well-known Recommender System approaches used in E-commerce and Tourism industry. <br />• Develop new Recommender Systems for a number of domains especially, Education, Health-care.
Essential Reading
- 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)
Supplementary Reading
- 3. M.D. Ekstrand, J.T. Riedl, J.A. Konstan, Collaborative filtering recommender systems, xxxxx
- xxxxx, xxxx, xxxxx
Journal and Conferences
- 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.