Content-Based Collaborative Filtering With Predictive Error Reduction-Based CNN Using IPU Model

Abstract

Recommender systems (RS) are strong tools for addressing the internet networking overload problems by considering past user ratings on multiple items with auxiliary data and suggests the better item to the end user. Traditional collaborative filtering (CF) and content-based methods were identified the interaction or correlation between users and the items. But they have failed to identify the join user-item interactions and suffering from incomplete cold start (ICS) and complete cold start (CCS) issues. To address the deficiencies of CF-based approaches, this article offers a novel deep learning based error predictions method along with CF based user-item interactions. Initially, incentivized/penalized user-based content-based collaborative filtering (IPU-CBCF) method is introduced for learning low-dimensional vectors of users and items, separately. The simulation results shows that IPU-CBCF using PER-CNN resulted in better performance as compared to the conventional approaches for all performance metrics like F1-score, recall, and precision, respectively.