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

Both content-based filtering and collaborative filtering have there strengths and weaknesses. This hands-on course is suitable for software engineers, data analysts and statisticians. A model of a trust-based recommendation system on a social network. In domains where the items consist of music or video However, collaborative filtering does introduce certain problems of its own: Early rater problem. In some domains generating a useful description of the content can be very difficult. Introduction: For this blog assignment, I summarized an interesting academic paper I found using Google Scholar. Three specific problems can be distinguished for content-based filtering: Content description. Introduction to Data Science – Building Recommender Systems … January 29, 2013 | Filed under: Data Science. (Note the findings about the suitability of a particular algorithm and about user perspectives on lists of results). The main thrust of the talk had to do with the advantage gained by using multiple behaviors as the source of input data for building a recommendation engine. Nudging Serendipity – Guiding users toward discovery of unknown unknowns.