Enhancing Recommender Systems: A New Approach Using Collaborative Filtering with Bayesian Optimization and Gaussian Processes

Recommendation systems frequently make use ofcollaborative filtering (CF). CF derives its strength from thefact that in order to profile its users, it does not require alot of knowledge about them, but it depends on earlier users’ratings in which they were choosing products to recommend.Although there has been progress in modeling users as wellas items, calibrating CF algorithms’ hyperparameters willcontinue to be a difficult task. This paper has come up witha new way of doing this by the use of Bayesian optimizationwith Gaussian processes throughout hyperparameter tuning.The method in question automatically works on hyperparametersto make two fundamental and straightforward CFalgorithms have solid results against three famous datasets:Netflix Prize, Movielens 1M as well as Movielens 10M. Thepractical implications here are that this method can helpprofessionals get better results from their recommendationswithout undertaking time-consuming manual adjustments.