Recommender Systems and Collaborative Filtering
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Electrical, Electronics and Communications Engineering".
Deadline for manuscript submissions: closed (15 July 2020) | Viewed by 54193
Special Issue Editors
Interests: artificial intelligence; machine learning; recommender systems
Special Issues, Collections and Topics in MDPI journals
Interests: recommender systems; deep learning; generative adversarial networks; algebraic geometry and topology
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The Internet has become the most powerful tool in today's society. People's lives revolve around Internet connectivity and the Internet is present in all the actions that we develop on a daily basis. To check the state of the traffic, to manage our finances or to choose the television program that we want to watch are only a few examples of what is done daily through the Internet. Every day, millions of new electronic resources, such as apps, blogs’ posts, social network’s publications or TV shows, are created to meet people's needs. However, the enormous amount of electronic resources available is so immense that finding the most suitable resource for each person becomes a challenge. This phenomenon is known as information overload problem.
Recommender Systems are intelligent systems capable of alleviating the information overload problem. They act as filters that learn the preferences of the users, allowing to pass the information that is relevant to them and blocking the one that is not. The most popular implementation of Recommender Systems is Collaborative Filtering. They constitute a wide family of algorithms that elaborate new recommendations to users by means of predictions based on large datasets of previous collective ratings (both explicit or implicit) of the available items or services.
The high performance of Collaborative Filtering algorithms has focused the interest of the scientific community and it has become a very active research area. Methods such as k-Nearest Neighbors, Matrix Factorization or Deep Learning have improved the quality of both predictions and recommendations provided by Recommender Systems over the last decade. In this Special Issue we seek to advance the knowledge in this matter with innovative contributions focused on Collaborative Filtering based Recommender Systems.
Prof. Dr. Fernando Ortega
Dr. Ángel González-Prieto
Guest Editors
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Keywords
- recommender systems
- collaborative filtering
- hybrid filtering
- k-nearest neighbors
- matrix factorization
- deep learning
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