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Traditional recommender systems assume the availability of explicit ratings of items from users. However, in many applications this is not the case and only binary, positive-only user feedback is available in the form of likes on Facebook, items bought on Amazon, videos watched on Netflix, adds/links clicked on Google, tags assigned to a photo in Flickr etc. Recently, the number of publications on designing recommender systems that handle binary, positive-only feedback, is growing very fast. In this tutorial we discuss why collaborative filtering with binary, positive-only feedback is fundamentally different from collaborative filtering with rating data. We give an overview of the algorithms suitable for this task with an emphasis on surprising commonalities and key differences. We show, for example, that also the nearest-neighbors method can be elegantly described in the matrix factorization framework.