Dear Colleagues,

Recommender Systems (RS) have become an essential tool for society. The advent of social networks and the Internet of things have sharpened the information overload problem. RS are the main solution to this problem. RS are intelligent systems that learn the preferences of users and provide them a set of items that fit with those preferences. In other words, RS acts as a filter that allows the passage of relevant items to users and block irrelevant ones. RS have been applied to a wide variety of conexts, such as movies, books, e-commerce, social networks, e-learning, and so on.

RS can be classified according to the type of information that they use to compute recommendations: Content Based Filtering (CBF) provides recommendations based on the features that describes the users and/or items belonging to the RS; Collaborative Filtering (CF) uses the ratings of users regarding items to provide a set of recommendations based on the assumtion that, if users shared the same interest in the past, they will also share the same interest in the future; and Hybrid Filtering (HF) combining CBF and CF to provide better recommendations.

We solicit original submission that improve RS on any of the following topics:

• collaborative filtering: Similarity metrics, quality measures, matrix factorization, cold start
• content based filtering: Topic modeling, folksonomies, geo-based recommendations
• social media data: Recommendations to group of users, followers, time-based recommendations

Dr. Fernando Ortega
Guest Editor

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• Collaborative Filtering
• Content Based Filtering
• Recommender Systems
• Matrix Factorization