A Dynamic Recommendation System Integrated Long Short-Term Memory (LSTM) and Matrix Factorization
學年 113
學期 2
出版（發表）日期 2025-06-01
作品名稱 A Dynamic Recommendation System Integrated Long Short-Term Memory (LSTM) and Matrix Factorization
作品名稱（其他語言）
著者 Ying-Hong Wang; Yi-Cheng Chen; Lin Hui; Yen-Lung Chu
單位
出版者
著錄名稱、卷期、頁數 Journal of Computers 36(3), p. 115-139
摘要 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.
關鍵字 social network; matrix factorization; stochastic gradient descent (SGD); deep learning; Long Short-Term Memory (LSTM); recommendation system
語言 en
ISSN 1991-1599
期刊性質 國外
收錄於 EI Scopus
產學合作
通訊作者 Ying-Hong Wang
審稿制度 是
國別 TWN
公開徵稿
出版型式 ,電子版
相關連結

機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/128531 )