{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,6]],"date-time":"2026-04-06T16:33:58Z","timestamp":1775493238422,"version":"3.50.1"},"reference-count":27,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2024,12,8]],"date-time":"2024-12-08T00:00:00Z","timestamp":1733616000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100019286","name":"Artificial Intelligence Research Center (AIRC), College of Engineering and Information Technology, Ajman University, Ajman, UAE","doi-asserted-by":"publisher","award":["2023-IRG-ENIT-6"],"award-info":[{"award-number":["2023-IRG-ENIT-6"]}],"id":[{"id":"10.13039\/501100019286","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>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\u2013item interactions. The VAE-MCRS model utilizes the latent features generated by the VAE in conjunction with user\u2013item 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.<\/jats:p>","DOI":"10.3390\/a17120561","type":"journal-article","created":{"date-parts":[[2024,12,9]],"date-time":"2024-12-09T10:11:47Z","timestamp":1733739107000},"page":"561","update-policy":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Variational Autoencoders-Based Algorithm for Multi-Criteria Recommendation Systems"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0002-1025-7868","authenticated-orcid":false,"given":"Salam","family":"Fraihat","sequence":"first","affiliation":[{"name":"Artificial Intelligence Research Center, College of Engineering and Information Technology, Ajman University, Ajman P.O. Box 346, United Arab Emirates"}]},{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0002-3026-845X","authenticated-orcid":false,"given":"Qusai","family":"Shambour","sequence":"additional","affiliation":[{"name":"Department of Software Engineering, Faculty of Information Technology, Al-Ahliyya Amman University, Amman 19111, Jordan"}]},{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0003-1980-1791","authenticated-orcid":false,"given":"Mohammed Azmi","family":"Al-Betar","sequence":"additional","affiliation":[{"name":"Artificial Intelligence Research Center, College of Engineering and Information Technology, Ajman University, Ajman P.O. Box 346, United Arab Emirates"}]},{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0002-2894-7998","authenticated-orcid":false,"given":"Sharif Naser","family":"Makhadmeh","sequence":"additional","affiliation":[{"name":"Department of Information Technology, King Abdullah II School for Information Technology, The University of Jordan, Amman 11942, Jordan"}]}],"member":"1968","published-online":{"date-parts":[[2024,12,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Ko, H., Lee, S., Park, Y., and Choi, A. (2022). A survey of recommendation systems: Recommendation models, techniques, and application fields. Electronics, 11.","DOI":"10.3390\/electronics11010141"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1109\/MCI.2024.3363984","article-title":"Recent developments in recommender systems: A survey","volume":"19","author":"Li","year":"2024","journal-title":"IEEE Comput. Intell. 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[Ph.D. Thesis, George Mason University]."}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/www.mdpi.com\/1999-4893\/17\/12\/561\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:49:53Z","timestamp":1760114993000},"score":1,"resource":{"primary":{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/www.mdpi.com\/1999-4893\/17\/12\/561"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,8]]},"references-count":27,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["a17120561"],"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.3390\/a17120561","relation":{},"ISSN":["1999-4893"],"issn-type":[{"value":"1999-4893","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,8]]}}}