New Trends in Artificial Intelligence for Recommender Systems and Collaborative Filtering

Abstract

In recent times, recommender systems (RSs) have been attracting a lot of attention from the research community because of their groundbreaking applications. This has led to software-intensive companies like Amazon, Netflix, Spotify, or Google relying on RSs to organize their huge catalogue of products and to offer highly attractive items to their users. In a highly connected society, consumers are exposed to a wide offering of products to be consumed, a large number of advertisements to attract new purchases, and a huge amount of data about the setup of these purchased items. Furthermore, this overload of information is even more overwhelming if we also consider multi-source data to which we are exposed to daily, like traffic information, financial trading information, or news, among others. Moreover, the inclusion of social networks in our lives has opened a new landscape for offering data, since social network users are intensive consumers of ever-changing new content. For this reason, it is crucial to provide intelligent systems capable of managing this large amount of data, sorting it according to the preferences and likes of the users, and offering a small portion of highly relevant content to consumers. For this purpose, RSs were developed with the aim of addressing this information overload problem. To address these problems, in recent years, the RS community has proposed new, astonishing, and very innovative solutions. Currently, the area is experiencing an exciting revolution of traditional Collaborative Filtering (CF) methods, based on Matrix Factorization and K-Nearest Neighbors, with the incorporation of cutting-edge technologies. Neural Networks, Deep Learning, Model Explainability, and Fair Prediction, among others, are making their way into the realm of RSs, importing techniques from other areas of artificial intelligence to provide novel approaches. In this Special Issue, we aim to widen the boundary of knowledge in CF-based RSs with new proposals incorporating cutting edge trends in artificial intelligence. In addition, we have collected exciting novel applications of RS techniques to address new challenges in real-world problems.