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

In their early stage, recommender systems only focused on pure information filtering field. 13:00 – 13:30 – Opening and Introduction. See schedule below (detailed schedule here: http://cslinux0.comp.hkbu.edu.hk/~fwang/srs2013/?page_id=79. Nudging Serendipity – Guiding users toward discovery of unknown unknowns. As for the former perhaps the following would be more useful: http://paloalto.thlab.net/publications/80. 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. Under this circumstance, researchers introduced recommender systems in early 1990s. Recommendation systems: privacy and interactivity. 1- A moderator decides on what products to sell in the package, 2- You build a smart recommendation system that can do this job for the moderator. For these two options, smart mechanisms like the ones used for personalization are Thanks to this, products that are normally not advertised because of their unpopularity are introduced to buyers that might buy those products. Tags, comments, votes, and explicit people relationships, which can be used to enhance recommendations. On the other hand, recommender systems can significantly affect the success of social media websites, ensuring each user is presented with the most attractive and relevant content, on a personal basis. Original：http://alban.galland.free.fr/Documents/Enseignements/INF396/recommendersystems-slides.pdf Recommender Systems Alban Galland INRIA-Saclay 18 March 2010 A. We introduced recommender systems and compared them to relevant work in TEL like adaptive educational hypermedia, learning networks, educational data mining and learning analytics. The paper you link deals strictly with the latter.