A recommender system is a subclass of information filtering system that seeks to predict the "rating" or "preference" a user would give to an item. These systems are present in a wide variety of applications and websites today. We can be aware of these recommendations when we are buying and articles similar to those we are looking for are suggested to us. However, they act in many other activities, such as in applications about restaurants and vacation trips. They also filter information from multimedia collections, such as Netflix or Amazon Prime. And furthermore, they are also present in browsers and they filter papers and books from repositories. They are subject to continuous research and improvement and the study of how these systems are being examined and evolve today is important because they literally filter the available information for us.

This bibliometric study analyses the present-day research front on recommender systems. The chosen data source is the Web of Science bibliographic database and the study is performed following quantitative methods, using bibliometric techniques together with a qualitative assessment and interpretation of the most relevant research articles.