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

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. We introduced recommender systems and compared them to relevant work in TEL like adaptive educational hypermedia, learning networks, educational data mining and learning analytics. Original：http://alban.galland.free.fr/Documents/Enseignements/INF396/recommendersystems-slides.pdf Recommender Systems Alban Galland INRIA-Saclay 18 March 2010 A. Techniques for delivering recommendations. 1.1: Learning Networks (LN) can facilitate self-organized, learner-centred lifelong learning. 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. In fact, recommendation systems are a billion-dollar industry, and growing. Most of this music will generally fit into personal tastes of that user, and it is all based on the “recommender systems” that have been introduced by these internet radio outlets. In academic jargon this problem is known as Collaborative Filtering, and a lot of ink has been spilled on the matter. For our purposes we can broadly group most techniques into three primary types of recommendation engines: Collaborative Filtering, Content-Based and Data Mining. Howdy, since the introduction of collecting ecommerce data (logging of purchased products) it would be great, to build something like product recommendations via the API. The fourth and final speaker was Sean Owen, founder at Myrrix, a startup that is building complete, real-time, scalable recommender system, built on Apache Mahout. 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. LN consist of participants and learning actions that are related to a certain domain (Koper and Sloep 2002). 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. 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. Recommender systems recommend objects regardless of potential adverse effects of their overcrowding. We will briefly introduce each below. Trust Networks for Recommender Systems (Atlantis Computational Intelligence Systems) by Patricia Victor, Chris Cornelis and Martine De Cock English | 2011 | ISBN: 9491216074 , 9789491216077 | 202 pages | PDF | 3,2 MB.