In this course we elaborate on the role of recommenders as a primary means of improving a user's or customer's experience, while increasing company revenue. The course covers learning methods for the recommender core, approaches for the design and evaluation of recommenders, and specific application areas of recommenders.
This course is a followup of the bachelor course CRM/RecSys, which contains an introduction to Customer Relationship Management and to Recommendation Engines, including two core methods for model learning (collaborative filtering and content-based modeling) and basics on evaluating recommenders. In this course, we consider advanced learning methods and advanced instruments for guaranteeing the goodness of the recommender core.
F. Ricci, L. Rokach, B. Shapira (eds). Recommender Systems Handbook. Springer 2011, esp:
Learning methods and applications
Ch4: A Comprehensive Survey of Neighborhood-based Recommendation Methods
Ch5: Advances in Collaborative Filtering
Ch19: Social Tagging Recommender Systems
Ch22: Aggregation of Preferences in Recommender Systems
Recommender design and evaluation
Ch11: Matching Recommendation Technologies and Domains
Ch14: Creating More Credible and Persuasive Recommender Systems: The Influence of Source Characteristics on Recommender System Evaluations
Ch15: Designing and Evaluating Explanations for RecommenderSystems
2) Scientific articles, mainly from the ACM conferences:
International Conf. on Recommender Systems (RecSys)
International Conf. on Information & Knowledge Management (CIKM)
Background in data mining is of advantage. This course is also appropriate for students who have heard the CRM/RecSys bachelor course.