Variational Autoencoders-Based Algorithm for Multi-Criteria Recommendation Systems

• Published in: Algorithms Vol. 17; no. 12; p. 561
• Main Authors: Fraihat, Salam, Shambour, Qusai, Al-Betar, Mohammed Azmi, Makhadmeh, Sharif Naser
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.12.2024
• Subjects: Accuracy; Algorithms; Analysis; Artificial intelligence; collaborative filtering; Computational linguistics; Computer vision; Data compression; Deep learning; Image analysis; Language processing; Machine learning; Machine vision; multi-criteria; Multiple criterion; Natural language interfaces; Natural language processing; Neurons; Normal distribution; Online instruction; Pattern analysis; Pattern recognition; Preferences; recommender system; Recommender systems; Root-mean-square errors; Sparsity; Tourism; User experience; variational autoencoders; Germany
• ISSN: 1999-4893; 1999-4893
• DOI: 10.3390/a17120561

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.