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
Publisher: Cambridge University Press
Page: 353
Format: pdf
ISBN: 0521493366, 9780521493369

Actual one at Facebook) The main disadvantage with recommendation engines based on collaborative filtering is when users instead of providing their personal preference try to guess the global preference and they introduce bias in the recommendation algorithm. See schedule below (detailed schedule here: http://cslinux0.comp.hkbu.edu.hk/~fwang/srs2013/?page_id=79. This report presents a general introduction to the topic and discusses major emerging challenges. Online Controlled Experiments: Introduction, Learnings, and Humbling Statistics. Tags, comments, votes, and explicit people relationships, which can be used to enhance recommendations. In particular, we introduce a design principle by focusing on the dynamic relationship between the recommender sys- tem's performance and the number of new training samples the system requires. 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. However, today's recommender system approaches almost exclusively focus on code reuse and do not consider modeling tasks in model-driven development. 13:00 – 13:30 – Opening and Introduction. ACM Recommender System 2012: Most discussed and tweeted papers and presentations #RecSys2012. This blog entry introduces a state-of-the-art report written by Sirris on recommender systems.