Journal of Artificial Intelligence, Machine Learning and Soft Computing

Abstract

Recommender systems have become an integral part of our daily lives, assisting users in discovering personalized content such as movies, music, products, and more. These systems employ various techniques to provide recommendations and one of the emerging approaches is soft computing-based recommender systems. This paper explores the use of soft computing techniques, including fuzzy logic, neural networks, and evolutionary algorithms, in developing recommender systems that offer highly personalized content recommendations. We discuss the advantages, challenges, and future prospects of employing soft computing in recommendation engines, highlighting the potential for enhanced user experiences.

Keywords: Recommender Systems, Soft Computing, Fuzzy Logic, Neural Networks, Evolutionary Algorithms, Personalized Content Recommendation, User Preferences, Collaborative Filtering, Deep Learning, Context-aware Recommendations