Towards Improving Accuracy and Computation Cost Optimization of Recommendation Systems

Abstract

It is hard to avoid recommender systems (RS) these days which play a vital role in various domains, such as e-commerce, online streaming platforms, and personalized content delivery. These systems assist users in discovering relevant items based on their preferences and past interactions. A variety of methods are developed by RS communities, to address the accuracy issue. But, most of these methods are sequential and omit the item features and user side information which contains relevant and rich information that can increase the accuracy of these systems. However, enhancing the accuracy of recommendations often comes at the expense of increased computational costs. This PhD thesis aims to address the challenge of improving the accuracy of RS following a hybrid approach that allows leveraging user-item features based on some relevant state-of-the-art models and using deep learning techniques such as the contractive autoencoder (CAE) while optimizing the cost of computation using parallel/distributed paradigms.