Biased autoencoder for collaborative filtering with temporal signals. (30th December 2021)

• Record Type: Journal Article
• Authors: Dou, Runliang; Arslan, Oguzhan; Zhang, Ce
• Is Part Of: Expert systems with applications. Volume 186(2021)
• Publication Date: 2021-12-30
• Subjects: Collaborative filtering -- AutoRec -- Temporal dynamics -- Bias -- Autoencoder; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2021.115775
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: Autoencoder can provide more accurate results in Collaborative Filtering. Neural networks give satisfying results in rating prediction due to non-linearity. Autoencoder has research potential in Collaborative Filtering and rating prediction. The usage of Autoencoder and temporal signals jointly give remarkable RMSE scores. With the advent of GPUs, neural networks become popular in Collaborative Filtering. Abstract: 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).