# Variational Autoencoders-Based Algorithm for Multi-Criteria Recommendation Systems
###### Abstract  
In recent years, recommender systems have become a crucial tool, assisting users in discovering and engaging with valuable information and services. Multi-criteria recommender systems have demonstrated significant value in assisting users to identify the most relevant items by considering various aspects of user experiences. Deep learning (DL) models demonstrated outstanding performance across different domains: computer vision, natural language processing, image analysis, pattern recognition, and recommender systems. In this study, we introduce a deep learning model using VAE to improve multi-criteria recommendation systems. Specifically, we propose a variational autoencoder-based model for multi-criteria recommendation systems (VAE-MCRS). The VAE-MCRS model is sequentially trained across multiple criteria to uncover patterns that allow for better representation of user-item interactions. The VAE-MCRS model utilizes the latent features generated by the VAE in conjunction with user-item interactions to enhance recommendation accuracy and predict ratings for unrated items. Experiments carried out using the Yahoo! Movies multi-criteria dataset demonstrate that the proposed model surpasses other state-of-the-art recommendation algorithms, achieving a Mean Absolute Error (MAE) of 0.6038 and a Root Mean Squared Error (RMSE) of 0.7085, demonstrating its superior performance in providing more precise recommendations for multi-criteria recommendation tasks. recommender system; variational autoencoders; deep learning; collaborative filtering; multi-criteria +
Footnote †: journal: _Algorithms_  
**Citation**: [PERSON] Variational Autoencoders-Based Algorithm for Multi-Criteria recommendations. **Citation**: [PERSON]; [PERSON]. overall score, and based on that, recommendations are generated. This strategy does not effectively reflect user needs, leading to inaccurate recommendations as they do not take into consideration the various aspects of user preferences [5]. In practice, end users are inclined to assess an item with respect to many attributes at the same time. For example, one can evaluate a hotel by its location, amenities, food, and atmosphere. RSs can fail to predict this kind of preference if the end user is asked to provide a single overall rating. To address this limitation and extend the usability of the recommendation models, Multi-Criteria Recommender Systems (MCRSs) have emerged [6; 7]. MCRSs improve the quality of the recommendations, in particular, the recommendation accuracy, by allowing the user to incorporate multiple criteria into the recommendation process instead of only one. Nonetheless, MCRSs have their own set of challenges. A significant issue is the need for accurate user preference modeling to capture user preferences across several aspects of an item. Without this, such recommendations will not take into consideration some of the tastes a user has. Additionally, the interdependencies among criteria can lead to multi-collinearity issues, complicating the modeling process and potentially degrading recommendation accuracy. Apart from that, another challenge comes from incomplete ratings across criteria. In most cases, users rate only a few of the criteria required, hence making it very hard to make models that can predict preferences accurately. Solving this sparsity while recommending relevant items is critical. Furthermore, the models should be biased to overfitting through joint training and regularization so that the representations learned are relevant to all the criteria and not just a select few [8; 9; 10]. To tackle these issues, this paper proposes a VAE-based model designed for multi-criteria recommendation systems (VAE-MCRS). The architecture of the VAE model is trained sequentially on many criteria, taking into account the complex relationship between users, their preferences, and the properties of the items.