Fairness is an important field in the Recommender Systems area since collaborative filtering datasets tend to be demographically biased. This paper proposes a parameterized model and provides an open repository of code to generate synthetic datasets containing demographic information. The model parameters can be set to hold different numbers of minority and nonminority users, distributions, and their overlapping. A neural network model has also been used to test the accuracy obtained in different scenarios, by setting the number of minority users and the overlapping between minority and nonminority distributions of votes. The results show how minority users receive unfair recommendations, particularly when their number decreases and when the distributions of minority versus nonminority users partially overlap. This research can be easily extended by designing more sophisticated models and modifying the provided framework; in this sense, a specific future work has been proposed.

In collaborative filtering recommender systems recommendations can be made to groups of users. There are four basic stages in the collaborative filtering algorithms where the group's users' data can be aggregated to the data of the group of users: similarity metric, establishing the neighborhood, prediction phase, determination of recommended items. In this paper we perform aggregation experiments in each of the four stages and two fundamental conclusions are reached: (1) the system accuracy does not vary significantly according to the stage where the aggregation is performed, (2) the system performance improves notably when the aggregation is performed in an earlier stage of the collaborative filtering process. This paper provides a group recommendation similarity metric and demonstrates the convenience of tackling the aggregation of the group's users in the actual similarity metric of the collaborative filtering process.

Recommender systems have developed in parallel with the web. They were initially based on demographic, content-based and collaborative filtering. Currently, these systems are incorporating social information. In the future, they will use implicit, local and personal information from the Internet of things. This article provides an overview of recommender systems as well as collaborative filtering methods and algorithms; it also explains their evolution, provides an original classification for these systems, identifies areas of future implementation and develops certain areas selected for past, present or future importance.