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

Recommender Systems: An Introduction. We will briefly introduce each below. EMusic, the second largest online music store after iTunes, introduced a new recommendation system on its site late last year. 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. 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. This is a youtube clip that gives you a simple introduction about how Netflix uses the collaborative filtering recommender system to improve their business. It conveys some simple ideas and is worth a look. Earlier this month, Netflix (an American provider of on-demand Internet streaming media) offered some details about the working of its recommendation system. Until recently, this literature suggests, research on recommendation systems has focused almost exclusively on accuracy, which led to systems that were likely to recommend only popular items, and hence suffered from a "popularity bias'' (Celma and Herrera 2008). Techniques for delivering recommendations. Let's begin another article's series. SRS == Social Recommender Systems. For our purposes we can broadly group most techniques into three primary types of recommendation engines: Collaborative Filtering, Content-Based and Data Mining. The authors then introduced a number of "item re-ranking methods that can generate substantially more diverse recommendations across all users while maintaining comparable levels of recommendation accuracy. Recommender Systems: An Introduction by Dietmar Jannach, Markus Zanker, Alexander Felfernig, Gerhard Friedrich. Research on SRS using relationship information in early phases with inconclusive results, modest accuracy improvement in limited sets of cases. Now i will talk about recommendation systems and how we can implement some simple recommendation algorithms using information filtering with functional examples. Introduce classification of SRS.