Content-Based Collaborative Filtering with Hierarchical Agglomerative Clustering Using User/ Item based Ratings

Abstract

The recommender system (RS) plays the major role in online networks, online shopping, and online services etc. The conventional RSs are suffering with the inaccurate quality of experience to the users, so the improper content is recommending to customers. The content based collaborative filtering (CBCF) method is introduced to solve the issues presented in the RSs. However, the CBCF method is suffering with the cold start problem for new users and suffering with data accuracy, data sparsity, and scalable data in clustering process. Thus, to solve these problems, this article proposes hierarchical agglomerative clustering (HAC) based collaborative filtering (HAC-CF) for RSs. The proposed HAC-CF based RS functions by utilizing the incentivized/ penalized user (IPU) model with user-based and item-based ratings. To this end, users are divided into several clusters through single link graph partitioning through minimum distance criteria. Then, the final item ranking is computed using Pearson correlation coefficient (PCC) similarity of users. Hence, recommendation efficiency and accuracy are increased at the end user by combining user, item models. The simulation results show the performance enhancement of proposed method with respect to F1-score, recall, and precision as compared to the conventional approaches.