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

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. Based on automated collaborative filtering, these recommender systems were introduced, refined, and commercialized by the team at GroupLens. In their early stage, recommender systems only focused on pure information filtering field. I am trying to build a recommender system which would recommend webpages to the user based on his actions(google search, clicks, he can also explicitly rate webpages). 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. The Recommender Stammtisch is a meetup for people who are interested in recommender systems, user behavior analytics, machine learning, AI and related topics. Recommender systems are fast becoming as standard a tool as search engines, helping users to discover content that interests them with very little effort. The paradox of choice; What is a Recommender System? Under this circumstance, researchers introduced recommender systems in early 1990s. Introduction to Recommender Systems. There are two major methods in designing a recommendation system: content-based method and collaborative filtering method. 9:30 Introductions – all participants introduce themselves. The recommender problem; General scheme of a RS; Tools of the trade.