Collaborative Filtering Based on a Variational Gaussian Mixture Model

FengLei Yang, Fei Liu and ShanShan Liu
Additional contact information
FengLei Yang: School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China
Fei Liu: School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China
ShanShan Liu: School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China

Future Internet, 2021, vol. 13, issue 2, 1-11

Abstract: Collaborative filtering (CF) is a widely used method in recommendation systems. Linear models are still the mainstream of collaborative filtering research methods, but non-linear probabilistic models are beyond the limit of linear model capacity. For example, variational autoencoders (VAEs) have been extensively used in CF, and have achieved excellent results. Aiming at the problem of the prior distribution for the latent codes of VAEs in traditional CF is too simple, which makes the implicit variable representations of users and items too poor. This paper proposes a variational autoencoder that uses a Gaussian mixture model for latent factors distribution for CF, GVAE-CF. On this basis, an optimization function suitable for GVAE-CF is proposed. In our experimental evaluation, we show that the recommendation performance of GVAE-CF outperforms the previously proposed VAE-based models on several popular benchmark datasets in terms of recall and normalized discounted cumulative gain (NDCG), thus proving the effectiveness of the algorithm.

Keywords: recommendation system; variational autoencoder; collaborative filtering; Gaussian mixture model (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1999-5903/13/2/37/pdf (application/pdf)
https://www.mdpi.com/1999-5903/13/2/37/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jftint:v:13:y:2021:i:2:p:37-:d:491268

Access Statistics for this article

Future Internet is currently edited by Ms. Grace You

More articles in Future Internet from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().