Recommender Systems: An Introduction . Dietmar Jannach, Markus Zanker, Alexander Felfernig, Gerhard Friedrich

Recommender Systems: An Introduction


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ISBN: 0521493366,9780521493369 | 353 pages | 9 Mb


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Recommender Systems: An Introduction Dietmar Jannach, Markus Zanker, Alexander Felfernig, Gerhard Friedrich
Publisher: Cambridge University Press




Playlist sequencing talk, Recommenders '06 Photo by davidjennings, cc licensed. This webinar provides an introduction to recommender systems, describing the different types of recommendation technologies available and how they are used in different applications today. This report presents a general introduction to the topic and discusses major emerging challenges. This is a youtube clip that gives you a simple introduction about how Netflix uses the collaborative filtering recommender system to improve their business. Title: An MDP-based Recommender System MDPs introduce two benefits: they take into account the long-term effects of each recommendation, and they take into account the expected value of each recommendation. In academic jargon this problem is known as Collaborative Filtering, and a lot of ink has been spilled on the matter. Talks that stood out most for me were Barry Smyth's introduction to the state-of-the-art on recommender systems and Pádraig Cunnigham's similar introduction to the Clique cluster's work on social network analysis. I spent Tuesday and Wednesday last week at a 'summer school' on recommender systems, hosted by MyStrands in Bilbao (thanks, sincerely, to them for their hospitality, and less sincerely to I recommend Juntae Kim's presentation as an introduction. In fact, recommendation systems are a billion-dollar industry, and growing. LN consist of participants and learning actions that are related to a certain domain (Koper and Sloep 2002). It conveys some simple ideas and is worth a look. EMusic, the second largest online music store after iTunes, introduced a new recommendation system on its site late last year. The tutorial started with an introduction on recommender system challenges by Domonkos Tikk, Andreas Hotho and Alan Said. 1.1: Learning Networks (LN) can facilitate self-organized, learner-centred lifelong learning. Most interesting to me was John Riedl's talk and subsequent discussion about the impact of recommender systems on community. We also illustrate specific computational models that have been proposed for mobile recommender systems and we close the paper by presenting some possible future developments and extension in this area. Providing sound way-finding support for lifelong learners in Learning Networks requires dedicated personalised recommender systems (PRS), that offer the learners customised advise on which learning actions or programs to study next. This blog entry introduces a state-of-the-art report written by Sirris on recommender systems. Share ebook Recommender Systems: An Introduction (repost).

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