CAERS-CF: Enhancing Convolutional Autoencoder Recommendations through Collaborative Filtering

Abstract

Recommendation systems are crucial in boosting companies' revenues by implementing various strategies to engage customers and encourage them to invest in products or services. Businesses constantly desire to enhance these systems through different approaches. One effective method involves using hybrid recommendation systems, known for their ability to create high-performance models. We introduce a hybrid recommendation system that leverages two types of recommendation systems: first, a novel deep learning-based recommendation system that utilizes users' and items' content data, and second, a traditional recommendation system that employs users' past behaviour data. We introduce a novel deep learning-based recommendation system called Convolutional Autoencoder Recommendation System (CAERS). It uses a Convolutional Autoencoder (CAE) to capture high-order meaningful relationships between users' and items' content information and decode them to predict ratings. Subsequently, we design a traditional model-based collaborative filtering recommendation system (CF) that leverages users' past behaviour data, utilizing Singular Value Decomposition (SVD). Finally, in the last step, we combine the two method's predictions with linear regression. We determine the optimal weight for each prediction generated by collaborative filtering and the deep learning-based recommendation system. Our main objective is to introduce a hybrid model called CAERS-CF that leverages the strengths of the two mentioned approaches. For experimental purposes, we utilize two movie datasets to showcase the performance of CAERS-CF. Our model outperforms each constituent model individually and other state-of-the-art deep learning or hybrid models. Specifically, the hybrid CAERS-CF model exhibits improvements across various metrics, highlighting the effectiveness of our approach in enhancing recommendation systems.