Personalized Recommendation Systems: Integrating Deep Learning with Collaborative Filtering

Abstract

Aim: This study aims to investigate the effectiveness of deep learning with collaborative filtering, focusing on improving the accuracy and scalability of multiple domain name recommendations.

Methods: We use a combination of collaborative filtering algorithms and deep learning architectures to scale adaptive recommendation models. Various datasets from e-commerce movie recommendations and social media platforms, including records of object interactions with consumers and additional contextual features, are used. We evaluate the overall performance of our comprehensive approach using a joint filtering approach that evaluates accuracy by considering and averaging standard accuracy estimates. We explore deep knowledge architectures, including neural networks, convolutional neural networks, and recurrent neural networks, to study their contribution to recommendation accuracy. Scalability is classified based on allocated compute frames and parallel processing techniques.