Recommender systems are now popular both commercially and in the research community, where many approaches have been suggested for providing recommendations. In many cases a system designer that wishes to employ a recommendation system must choose between a set of candidate approaches. A first step "Recommender Systems Handbook" The book "Recommender Systems Handbook" can be ordered at A Recommender System is a process that seeks to predict user preferences. This Specialization covers all the fundamental techniques in recommender systems, from non-personalized and project-association recommenders through content-based and collaborative filtering techniques, as well as advanced topics like matrix factorization, hybrid machine learning methods for recommender systems, and Recommender Systems: An Introduction [Dietmar Jannach, Markus Zanker, Alexander Felfernig, Gerhard Friedrich] on Amazon.com. *FREE* shipping on qualifying offers. In this age of information overload, people use a variety of strategies to make choices about what to buy recommender systems and discuss the major challenges. We compare and evaluate available algorithms and examine their roles in the future developments. In addition to algorithms, physical aspects are described to illustrate macroscopic behavior of recommender systems. Potential impacts and future directions are discussed. We emphasize that 3 Content-based Recommender Systems: State of the Art and Trends 77 does not require any active user involvement, in the sense that feedback is derived from monitoring and analyzing user's activities. Explicit evaluations indicate how relevant or interesting an item is to the user If you want to share your own teaching material on recommender systems, please send the material (preferably in editable form) or a link to the material to dietmar.jannach (at) udo.edu. Feel free to use the material from this page for your courses. Slides. Slides for Recommender Systems: An Introduction (UPDATED August October 2011) Recommender Systems Handbook illustrates how this technology can support the user in decision-making, planning and purchasing processes. It works for well known corporations such as Amazon, Google, Microsoft and AT&T. This handbook is suitable for researchers and advanced-level students in computer science as a reference. Recommender Systems Handbook / This second edition of a well-received text, with 20 new chapters, presents a coherent and unified repository of recommender systems' major concepts, theories, methodologies, trends, and challenges. A variety of real-world applications and detailed case studies are includedition In addition to whole Many different methods exist for constructing a recommender system such as naive approaches, in which the system calculates the average rating of an item as rated by different users, or calculates the average rating of the items by the same user, and then recommends an item that has a relatively high average rating. While a substantial amount of research has already been performed in the area of recommender systems, most existing approaches focus on recommending the most relevant items to users without taking into account any additional contextual information, such as time, location, or the company of other people (e.g., for watching movies or dining out). Evaluating Recommendation Systems 3 Often it is easiest to perform of?ine experiments using existin