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

(ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). Chapter 01: Introduction to Recommender Systems. We introduced recommender systems and compared them to relevant work in TEL like adaptive educational hypermedia, learning networks, educational data mining and learning analytics. Nudging Serendipity – Guiding users toward discovery of unknown unknowns. Learn SQL from Stanfords Free Online “Introduction to Databases” Course. Online Controlled Experiments: Introduction, Learnings, and Humbling Statistics. The link for the online guide is available here. LN consist of participants and learning actions that are related to a certain domain (Koper and Sloep 2002). The tutorial started with an introduction on recommender system challenges by Domonkos Tikk, Andreas Hotho and Alan Said. 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. 1.1: Learning Networks (LN) can facilitate self-organized, learner-centred lifelong learning. 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. ACM Recommender System 2012: Most discussed and tweeted papers and presentations #RecSys2012. 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. Until recently, this literature suggests, research on recommendation systems has focused almost exclusively on accuracy, which led to systems that were likely to recommend only popular items, and hence suffered from a "popularity bias'' (Celma and Herrera 2008). 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. The authors then introduced a number of "item re-ranking methods that can generate substantially more diverse recommendations across all users while maintaining comparable levels of recommendation accuracy. Http://muricoca.github.com/recommendation-lectures/index.html.