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

In the previous post we talked about how Markov random fields (MRFs) can be used to model local structure in the recommendation data. Markov random fields for recommender systems II: Discovering latent space. 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. Local structures are powerful enough to make our MRF work, but they model At test time, we will introduce unseen items into the model assuming that the model won't change. In domains where the items consist of music or video However, collaborative filtering does introduce certain problems of its own: Early rater problem. Three specific problems can be distinguished for content-based filtering: Content description. In some domains generating a useful description of the content can be very difficult. The book is a very helpful introduction for all researcher that want to conduct research on personalization, learner support and knowledge management through recommender systems. Both content-based filtering and collaborative filtering have there strengths and weaknesses. For simplicity, assume that latent factors are binary.