A Dynamic Recommendation System Integrated Long Short-Term Memory (LSTM) and Matrix Factorization
Year: 2025
Type: article
Abstract: Matrix factorization (MF) technique has been widely utilized in recommendation systems due to the precise prediction of users’ interests. Prior MF-based methods adapt the overall rating to make the recommendation by extracting latent factors from users and items. However, in real applications, people’s preferences usually vary with time; the traditional MF-based methods could not properly capture the change of users’ interests. In this paper, by incorporating the recurrent neural network (RNN) into MF, we developed a novel recommendation system, M-RNN-F, to effectively describe the preference evolution of users over time. A learning model is proposed to capture the evolution pattern and predict the user preference in the future. The experimental results show that M-RNN-F performs better than other state-of-the-art recommendation algorithms. In addition, we conduct experiments on real world dataset to demonstrate the practicability.
Source: 電腦學刊
Institutions: Tamkang University, National Central University
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4411772642