The Use of Metaheuristics in the Evolution of Collaborative Filtering Recommender Systems: A Review

As digitalization spreads across the globe, the amount of information available is increasing exponentially and users are suffering from information overload. Recommender systems present a feasible and effective means to guide and expose users to products and items which align with their preferences. Specifically with the boom of social networks, collaborative filtering recommender systems offer a means to suggest highly relevant items to a user based on their shared interests with other users in the system. Despite major advancements through the integration of machine learning and hybrid systems, collaborative filtering algorithms struggle to handle large and sparse datasets which hampers the system’s ability to provide accurate recommendations. Metaheuristic techniques have been successful in improving collaborative filtering recommender systems despite data size and sparsity. This study presents a review of different attempts to optimize collaborative filtering recommender systems inclusive of metaheuristic techniques in this evolution which highlights an evident gap in standardized evaluation metrics of recommender systems.