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

Recommender Systems: An Introduction Dietmar Jannach, Markus Zanker, Alexander Felfernig, Gerhard Friedrich ebook
Format: pdf
ISBN: 0521493366, 9780521493369
Page: 353
Publisher: Cambridge University Press

Recommender systems recommend objects regardless of potential adverse effects of their overcrowding. One of the most common types of recommendation engine, Collaborative Filtering is a behavior based system that functions solely on the assumption that people with similar interests share common preferences. Let's talk about bad recommendations. This is a youtube clip that gives you a simple introduction about how Netflix uses the collaborative filtering recommender system to improve their business. The paper you link deals strictly with the latter. It conveys some simple ideas and is worth a look. Recommendation systems: privacy and interactivity. For our purposes we can broadly group most techniques into three primary types of recommendation engines: Collaborative Filtering, Content-Based and Data Mining. (Note the findings about the suitability of a particular algorithm and about user perspectives on lists of results). Techniques for delivering recommendations. Based on automated collaborative filtering, these recommender systems were introduced, refined, and commercialized by the team at GroupLens. Today we introduce UnSuggester, “the worst recommendation system ever devised™.” UnSuggester is a brand new idea in recommender technology. As for the former perhaps the following would be more useful: http://paloalto.thlab.net/publications/80. This method, introduced by the same author and others from MSR as “Matchbox” is now used in different settings. 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. The book is a very helpful introduction for all researcher that want to conduct research on personalization, learner support and knowledge management through recommender systems. Introducing Docear's Research Paper Recommender System. We will briefly introduce each below. 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 this demo paper we present Docear's research paper recommender system.