An efficient hybrid recommendation model based on collaborative filtering recommender systems

Abstract In recent years, collaborative filtering (CF) techniques have become one of the most popularly used techniques for providing personalized services to users. CF techniques collect users’ previous information about items such as books, music, movies, ideas, and so on. Memory‐based models are generally referred to as similarity‐based CF models, which are one of the most widely agreeable approaches for providing service recommendations. The memory‐based approach includes user‐based CF (UCF) and item‐based CF (ICF) algorithms. The UCF model recommends items by finding similar users, while the ICF model recommends items by finding similar items based on the user‐item rating matrix. However, consequent to the ingrained sparsity of the user‐item rating matrix, a large number of ratings are missing. This results in the availability of only a few ratings to make predictions for the unknown ratings. The result is the poor prediction quality of the CF model. A model to find the best algorithm is provided here, which gives the most accurate recommendation based on different similarity metrics. Here a hybrid recommendation model, namely ΓUICF, is proposed. The ΓUICF model integrates the UCF and ICF models with the Γ linear regression model to model the sparsity and scalability issue of the user‐item rating matrix. Detailed experimentation on two different real‐world datasets shows that the proposed model demonstrates substantial performance when compared with the existing methods.