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

Recommender Systems: An Introduction



Download Recommender Systems: An Introduction




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


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. We will briefly introduce each below. The Author introduced 5 papers, which offered different taxonomies. An attack against a collaborative filtering recommender system consists of a set of attack profiles, each contained biased rating data associated with a fictitious user identity, and including a target item, the item that the attacker wishes that item- based collaborative filtering might provide significant robustness compared to the user-based algorithm, but, as this paper shows, the item-based algorithm also is still vulnerable in the face of some of the attacks we introduced. 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. Techniques for delivering recommendations. Recommender systems recommend objects regardless of potential adverse effects of their overcrowding. Related Work (Recommender Systems Taxonomies). For our purposes we can broadly group most techniques into three primary types of recommendation engines: Collaborative Filtering, Content-Based and Data Mining. This method, introduced by the same author and others from MSR as “Matchbox” is now used in different settings. Earlier this month, Netflix (an American provider of on-demand Internet streaming media) offered some details about the working of its recommendation system. (Note the findings about the suitability of a particular algorithm and about user perspectives on lists of results). 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.