Year: 2024
Type: article
Abstract: The exponential rise in the popularity of social networks in recent years has led to a corresponding surge in online data, creating a vast pool of information. This growing dataset has captivated the interest of researchers, compelling them to devise recommendation algorithms designed to navigate through this immense sea of data to establish meaningful friendships. Despite the success of CF, there is a notable gap in the related research, as deep learning’s potential to generate friendship recommendations in social networks using CF has been neglected. This paper seeks to fill this gap by introducing a novel, collaborative friendship recommendation system. The proposed system leverages the power of Neural Collaborative Filtering (NCF) to offer users a platform for discovering new friends who are highly likely to engage interactively. To underscore the effectiveness of our model, we present experimental results using a real dataset, showcasing the system’s capability to deliver accurate and valuable friend recommendations.
DOI: 10.59035/ilmo8300
Authors: Aadil Alshammari, Rakan Alanazi
Institution: Northern Border University
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
Funder: Northern Border University
OpenAlex ID: W4402013591