# Variational Autoencoders-Based Algorithm for Multi-Criteria Recommendation Systems
## 2 Background and Related Work
### Variational Autoencoder-Based Recommender Systems
Experiments conducted on four datasets show the effectiveness of JoVA-Hinge in outperforming a set of related recommendation approaches. While JoVA demonstrated strong performance across multiple datasets, it was primarily designed for single-criterion implicit feedback scenarios. [PERSON] and [PERSON] [20] proposed an integrated MC model that features autoencoder and deep neural networks, wherein the initial weights of the model were optimized using the Firefly algorithm, labeled MCAE-FADNN. The model is broken down into three phases. The first phase aims to use the autoencoder to fill in the gaps by predicting the missing ratings of the criteria. The second phase entails making up the gaps concerning the overall ratings of criteria that are missing in the current training data. Finally, the third phase involves generating recommendations and measuring the effectiveness of the model. The model helps address the challenges of data sparsity while providing accurate recommendations. Experiments conducted using two datasets, as well as evaluation metrics of MAE, RMSE, Precision, Recall, F2, and MAP, showed that the proposed MCAE-FADNN model performed better than relevant baseline models. [PERSON] et al. [18] introduced a multi-criteria recommendation approach that utilizes deep learning and matrix factorization to alleviate the sparsity problem. To perform nonlinear and hidden connections among users' multi-criteria data, deep AEs are used together with single-value decomposition, which improves the overall accuracy of recommendations. Using the latent vector obtained from the autoencoder and the dimensionality reduction performed by singular-value decomposition, the proposed method can accordingly render accurate predictions of the missing ratings for the users. The experimental results on the Yahoo! Movies multi-criteria dataset indicate that the proposed approach surpasses existing recommendation methods by addressing data sparsity and resulting in more accuracy in recommendations as described by RMSE and MAE values. While these works showed promising results in addressing data sparsity and improving recommendation accuracy, they did not fully exploit VAE's capability to model uncertainty in user preferences. [PERSON] et al. made notable contributions in addressing the limitations of VAE-based models, particularly in the context of implicit feedback and Top-N recommendations. In their first work [16], they introduced a self-adapting credibility weight technique that re-evaluates the positive samples and utilizes higher-order relationships to identify key negative samples within the item-item matrix. In addition, the authors simplify the complex nonlinear structure and instead create a straightforward, efficient VAE framework with a linear structure. Integrating reconstruction loss functions for positive samples and key negative samples, VAE++ outperformed the VAE-based models by a great margin, as is shown by extensive experiments conducted on four publicly available real-world datasets. Building on this, [PERSON] et al. [17] addressed a central issue with collaborative filtering recommender systems that rely on implicit feedback, especially in the context of Top-N recommendation. They challenge the common practice of treating all interacted items as equal positives and all non-interacted items as negatives, stating that this underscores the complexity of user preferences. To address this issue, they designed a simple effective linear VAE framework, named VAE*, that essentially adapts the credibility weight mechanism for positive samples and higher-order interactions for identifying relevant negative samples. They also streamlined the VAE framework into a linear specification by removing the non-linear components. Their theoretical justification and extensive empirical tests on six datasets show that their method considerably outperforms existing VAE-based models.