Collaborative Filtering via Group-Structured Dictionary Learning

To handle this information overload and to help users in efficient decision making, recommender systems (RS) have been designed. The goal of RSs is to recommend personalized items for online users when they need to choose among several items. Typical problems include recommendations for which movie to watch, which jokes/books/news to read, which hotel to stay at, or which songs to listen to. One of the most popular approaches in the field of recommender systems is collaborative filtering (CF). The underlying idea of CF is very simple: Users generally express their tastes in an explicit way by rating the items. CF tries to estimate the users' preferences based on the ratings they have already made on items and based on the ratings of other, similar users. For a recent review on recommender systems and collaborative filtering, see e.g., [1]. Novel advances on CF show that dictionary learning based approaches can be efficient for making predictions about users' preferences [2]. The dictionary learning based approach assumes that (i) there is a latent, unstructured feature space (hidden representation) behind the users' ratings, and (ii) a rating of an item is equal to the product of the item and the user's feature.