Year: 2024
Type: article
Abstract: This paper discusses the design and evaluation of a Neural Collaborative Filtering (NCF) model for movie recommendations using the MovieLens dataset. It addresses the limitations of traditional recommendation systems, such as content-based filtering and collaborative filtering, which struggle with data sparsity and the cold start problem. By incorporating deep learning, the NCF model enhances the accuracy and personalization of recommendations by learning the latent features of users and items and capturing complex interactions.
Author: Zehang Li
Institution: Harbin Institute of Technology
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4404249492