Recommender System - Binary Ratings without explicit dislike I'm currently looking into developing a system in which media from various sources is collected, with the notion that the media collected for each user is media they 'like'. This results in a set of n users, each with their own respective set of m liked items. 

Most papers on recommender systems I find either require explicit ratings, or work within a binary domain with likes and dislikes.

How would I go about making recommendations based mainly on what is in a users preferred / liked set, without information on what they dislike? 

Thanks.
You can use one-class classification, in which the learner is given only positive examples, but must later on classify new instance into positive and negative. An example is intrusion detection – the learner is given the network in its working state, and tries to identify outlying behavior.