Biased autoencoder for collaborative filtering with temporal signals

2021
Recommendation systems are used in various types of online platforms and in e-commerce. Collaborative filtering (CF) is one of the most popular approaches for recommendation systems and has been widely studied in academia. In recent years, several models based on neural networks that can discover nonlinear relationships have been proposed and compared to traditional CF models. The results showed that they performed better in terms of their prediction accuracy. However, these models do not consider user bias and item bias together, and they do not include temporal signals. This paper proposes a biased autoencoder model (Biased AutoRec) for CF, which is built on the well-known AutoRec CF approach. Several approaches are also proposed to integrate temporal signals into the Biased AutoRec model to merge the power of nonlinearity and temporal signals. Experiments on several public datasets showed that the new models outperformed the AutoRec model, which outperformed the prediction accuracy of previous state-of-the-art CF models (i.e., biased matrix factorization, RBM-CF, LLORMA).
EXPERT SYSTEMS WITH APPLICATIONS
卷号:186
ISSN:0957-4174
来源机构
Tianjin University
收录类型
SSCI
发表日期
2021
学科领域
循证管理学
国家
中国
语种
英语
DOI
10.1016/j.eswa.2021.115775
其他关键词
TIME
EISSN
1873-6793
被引频次(WOS)
0
被引更新日期
2022-01
关键词
Collaborative filtering AutoRec Temporal dynamics Bias Autoencoder