High Accurate Multicriteria Cluster-Based Collaborative Filtering Recommender System

Abstract

A Recommender System (RS) is one of the appropriate answers by researchers for alleviating the problem of customers' overload with information from an internet source. In RS, users' evaluation of the items assists in quickly determining the user's preferences and removing the choosing overhead for the user the next time they search. However, using user ratings for an item based on multiple aspects has given researchers little attention to recommending an object accurately. This research proposes a model-based collaborative filtering movie recommendation system based on user ratings across various criteria. The proposed systems' model was established using clustering and classification methods, such as k-means, k-modes, and Multinomial logistic regression. The proposed work consists of two different steps; first, clustering mode; after clustering the dataset with k-means, k-modes were used to re-cluster it into many sub-clusters for search domain reduction purposes. The second is classification mode; the Multinomial Logistic Regression (MLR) model was created to predict the closest cluster class to newly active users. Since the MLR is one of the probabilistic models, it is more accurate than cluster methods in the prediction process. The proposed approach uses distance indicators, such as modified Mahalanobis distance and Euclidean distance, to measure the similarity between new active and in-group users. The evaluation of the proposed methodology was done using the MAE and Silhouette scores. Different values of Silhouette score were achieved in this study for a different number of features and clusters, with the best k-means score being 0.822. Based on multicriteria Yahoo!! Movie Dataset, the MAE result indicates that this study is better than the existing one.