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

In fact, recommendation systems are a billion-dollar industry, and growing. Within the second round of the personalized recommender system, Ciapple has achieved 50x response speed improvement by re-engineering the whole system which satisfied the web application 40x response time over all improvement.Ciapple is now planing for introducing a set of new intelligent features that would enhance the Choozer's shopping experience and thus increase the conversion rate of ChoozOn. Today we introduce UnSuggester, “the worst recommendation system ever devised™.” UnSuggester is a brand new idea in recommender technology. Skip to content Introduction to Recommender System (Brief Introduction). The introduction of the first approach is based on the article Matrix Factorization Techniques for Recommender Systems by Koren, Bell and Volinsky. Xlvector – Recommender System. Related Work (Recommender Systems Taxonomies). Ŧ�果翻墙，可以更好的浏览这个blog. Enhancements to the web application in the end of January 2012. 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. The Author introduced 5 papers, which offered different taxonomies. Let's talk about bad recommendations. Brief introduction of recommender system. 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. Recommender systems recommend objects regardless of potential adverse effects of their overcrowding. Original：http://alban.galland.free.fr/Documents/Enseignements/INF396/recommendersystems-slides.pdf Recommender Systems Alban Galland INRIA-Saclay 18 March 2010 A. 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. In academic jargon this problem is known as Collaborative Filtering, and a lot of ink has been spilled on the matter. Homepage, where users can explicitly rate movies they have seen. Based on automated collaborative filtering, these recommender systems were introduced, refined, and commercialized by the team at GroupLens.