# Variational Autoencoders-Based Algorithm for Multi-Criteria Recommendation Systems
## 2 Background and Related Work
### Variational Autoencoder-Based Recommender Systems  
Among deep learning-based models, autoencoders and VAEs are enormously popular because they serve daily purposes in multiple domains and applications of deep learning-based algorithms in various tasks of unsupervised learning [14]. VAEs, which are improved-upon versions of autoencoders, work through nonlinear techniques alongside probabilistic modeling of latent variables to build compact representations of data. These probabilistic models let VAEs infer latent user preferences and intrinsic semantics of items without the need for explicit labels. The uncertainty captured by VAEs in user-item interactions allows them to make more refined recommendations [15; 16; 17; 18; 19; 20; 21; 22]. Consequently, several researchers have leveraged autoencoder and VAE architectures to further improve the effectiveness of their recommendation systems. However, these works reveal several gaps and opportunities for improvement in terms of architectural design, multi-criteria handling, and the integration of probabilistic modeling. [PERSON] and [PERSON] in [22] proposed a multi-criteria CF algorithm (AE-MCCF) that is based on autoencoders. This algorithm is designed to improve the accuracy of the recommendations. This occurs as AE-MCCF uncovers latent, complex, and nonlinear relationships between user and item profiles. Initially, an autoencoder is developed for the user-item data associated with specific criteria. Then, a feedforward neural network is utilized to model the nonlinear relationships between the criteria and the overall user evaluations. Finally, an aggregation function is employed to aggregate criterion-based predictions to generate the overall predictions. Empirical results showed that AE-MCCF improved the accuracy of the predictions generated in comparison with the baseline recommendation methods. [PERSON] [21] proposed a multi-criteria recommendation algorithm based on deep autoencoders (AEMC) to capture the intricate, non-linear, and latent relations among users with respect to multiple criteria preferences to produce more relevant recommendations. The AEMC model supports the encoding of complex abstractions as data representations in the encoder layers while integrating users' multi-criteria preferences. This model will therefore be able to understand and extract fine-grained interactions between users and items. This is validated by experimental results on multi-criteria datasets, TripAdvisor and Yahoo! Movies, which demonstrated better performance of the AEMC model in comparison with various benchmark recommendation models. Although AEMCCF and AEMC demonstrated enhanced performance compared with baseline methods, their reliance on conventional autoencoders prevented them from exploiting the uncertainty modeling capabilities inherent in VAEs. [PERSON] et al. [19] presented a variational autoencoder-based recommender system, referred to as RecVAE. It includes multiple improvements for the foundational Multi-VAE model [23], such as an updated encoder architecture, a novel composite prior distribution for the latent codes, a novel method to set the hyperparameter \(\beta\), and an innovative method for RecVAE training through interchanging updates of the encoder and decoder. Experimental results on the Netflix Prize, MovieLens-20M, and Million Songs datasets showed that RecVAE significantly outperforms other autoencoder-based recommendation models. While RecVAE demonstrated significant performance improvements on the Netflix Prize, MovieLens-20M, and Million Songs datasets, it was limited to single-criterion recommendations. [PERSON] et al. [15] introduced the Joint Variational Autoencoder (JoVA) model, an ensemble of two VAEs, for Top-K recommendations with implicit feedback. JoVA predicts user preferences by extracting both user and item representations. This architecture enables JoVA to gather correlations between users and items. The authors also introduced JoVA-Hinge, an enhanced version of JoVA aimed to improve the use of implicit feedback in recommendations.