{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T10:13:06Z","timestamp":1777716786683,"version":"3.51.4"},"reference-count":20,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T00:00:00Z","timestamp":1777420800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Recommender systems are increasingly exposed to anomalous user behavior that can distort recommendation outcomes and compromise system reliability. In real-world settings, explicit labels identifying malicious activity are rarely available, motivating the adoption of unsupervised detection approaches. This study presents a systematic comparative analysis of classical machine learning and deep learning techniques for anomaly detection in recommender systems. Using the MovieLens 1M dataset, we construct a user-level behavioral representation based on statistical, temporal, and interaction-based features derived from explicit rating data. Three unsupervised detection models are evaluated: Isolation Forest, One-Class Support Vector Machine, and an autoencoder-based neural network. To address the absence of ground-truth labels, evaluation is conducted using a comprehensive label-free protocol, including score distribution analysis, percentile-based thresholding, ranking stability, and inter-model agreement. In addition, controlled experiments with synthetic attack profiles are conducted to assess detection performance under different adversarial strategies. Results indicate that individual models capture complementary aspects of anomalous behavior, exhibiting low to moderate agreement. An ensemble scoring strategy improves ranking stability and provides a consistent mechanism for identifying highly deviant user profiles. The findings suggest that ensemble-based unsupervised detection constitutes a practical and interpretable first-layer screening approach for recommender system monitoring under label-scarce conditions.<\/jats:p>","DOI":"10.3390\/info17050426","type":"journal-article","created":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:46:11Z","timestamp":1777455971000},"page":"426","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Comparative Study of Unsupervised Machine Learning and Deep Learning Techniques for Anomaly Detection in Recommender Systems"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6045-8692","authenticated-orcid":false,"given":"Rodolfo","family":"Bojorque","sequence":"first","affiliation":[{"name":"Campus El Vecino, Universidad Polit\u00e9cnica Salesiana, Cuenca 010102, Ecuador"},{"name":"Math Innovation Group, Universidad Polit\u00e9cnica Salesiana, Cuenca 010102, Ecuador"},{"name":"Advanced Computing and Data Research Group, Universidad Polit\u00e9cnica Salesiana, Cuenca 010102, Ecuador"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7472-9417","authenticated-orcid":false,"given":"Remigio","family":"Hurtado","sequence":"additional","affiliation":[{"name":"Campus El Vecino, Universidad Polit\u00e9cnica Salesiana, Cuenca 010102, Ecuador"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7056-8588","authenticated-orcid":false,"given":"Miguel","family":"Arcos-Argudo","sequence":"additional","affiliation":[{"name":"Campus El Vecino, Universidad Polit\u00e9cnica Salesiana, Cuenca 010102, Ecuador"},{"name":"Math Innovation Group, Universidad Polit\u00e9cnica Salesiana, Cuenca 010102, Ecuador"},{"name":"Advanced Computing and Data Research Group, Universidad Polit\u00e9cnica Salesiana, Cuenca 010102, Ecuador"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5324-0759","authenticated-orcid":false,"given":"Mauricio","family":"Ortiz","sequence":"additional","affiliation":[{"name":"Campus El Vecino, Universidad Polit\u00e9cnica Salesiana, Cuenca 010102, Ecuador"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,4,29]]},"reference":[{"key":"ref_1","unstructured":"Ahram, T.Z. (2019). Hierarchical Clustering for Collaborative Filtering Recommender Systems. Proceedings of the Advances in Artificial Intelligence, Software and Systems Engineering, Springer International Publishing."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Lam, S.K., and Riedl, J. (2004, January 17\u201320). Shilling recommender systems for fun and profit. Proceedings of the 13th International Conference on World Wide Web, New York, NY, USA.","DOI":"10.1145\/988672.988726"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"23-es","DOI":"10.1145\/1278366.1278372","article-title":"Toward trustworthy recommender systems: An analysis of attack models and algorithm robustness","volume":"7","author":"Mobasher","year":"2007","journal-title":"ACM Trans. Internet Technol."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Burke, R., Mobasher, B., Williams, C., and Bhaumik, R. (2006, January 20\u201323). Classification features for attack detection in collaborative recommender systems. Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Philadephia, PA, USA.","DOI":"10.1145\/1150402.1150465"},{"key":"ref_5","first-page":"1","article-title":"Robust Recommender System: A Survey and Future Directions","volume":"58","author":"Zhang","year":"2025","journal-title":"ACM Comput. Surv."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Cherifi, H., Donduran, M., Rocha, L.M., Cherifi, C., and Varol, O. (2025). Enhancing Recommender Systems with Anomaly Detection: A Graph Neural Network Approach. Proceedings of the Complex Networks & Their Applications XIII, Springer Nature.","DOI":"10.1007\/978-3-031-82431-9"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"767","DOI":"10.1007\/s10462-012-9364-9","article-title":"Shilling Attacks against Recommender Systems: A Comprehensive Survey","volume":"42","author":"Gunes","year":"2014","journal-title":"Artif. Intell. Rev."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"683","DOI":"10.1016\/j.ins.2021.07.041","article-title":"Ready for emerging threats to recommender systems? A graph convolution-based generative shilling attack","volume":"578","author":"Wu","year":"2021","journal-title":"Inf. Sci."},{"key":"ref_9","first-page":"211","article-title":"Detecting abnormal profiles in collaborative filtering recommender systems","volume":"47","author":"Yang","year":"2016","journal-title":"J. Intell. Inf. Syst."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1541880.1541882","article-title":"Anomaly detection: A survey","volume":"41","author":"Chandola","year":"2009","journal-title":"ACM Comput. Surv."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Aggarwal, C.C. (2017). An Introduction to Outlier Analysis, Springer International Publishing.","DOI":"10.1007\/978-3-319-47578-3_1"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Liu, F.T., Ting, K.M., and Zhou, Z.H. (2008, January 15\u201319). Isolation Forest. Proceedings of the 2008 Eighth IEEE International Conference on Data Mining, Pisa, Italy.","DOI":"10.1109\/ICDM.2008.17"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1443","DOI":"10.1162\/089976601750264965","article-title":"Estimating the Support of a High-Dimensional Distribution","volume":"13","author":"Platt","year":"2001","journal-title":"Neural Comput."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1126\/science.1127647","article-title":"Reducing the Dimensionality of Data with Neural Networks","volume":"313","author":"Hinton","year":"2006","journal-title":"Science"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Chalapathy, R., and Chawla, S. (2019). Deep Learning for Anomaly Detection: A Survey. arXiv.","DOI":"10.1145\/3394486.3406704"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"891","DOI":"10.1007\/s10618-015-0444-8","article-title":"On the evaluation of unsupervised outlier detection: Measures, datasets, and an empirical study","volume":"30","author":"Guilherme","year":"2016","journal-title":"Data Min. Knowl. Discov."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2827872","article-title":"The MovieLens Datasets: History and Context","volume":"5","author":"Harper","year":"2015","journal-title":"ACM Trans. Interact. Intell. Syst."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1109\/MC.2009.263","article-title":"Matrix Factorization Techniques for Recommender Systems","volume":"42","author":"Koren","year":"2009","journal-title":"Computer"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Ricci, F., Rokach, L., and Shapira, B. (2015). Recommender Systems Handbook, Springer.","DOI":"10.1007\/978-1-4899-7637-6"},{"key":"ref_20","unstructured":"Aggarwal, C.C. (2016). Recommender Systems: The Textbook, Springer International Publishing. Computer Science."}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/17\/5\/426\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T10:20:01Z","timestamp":1777458001000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/17\/5\/426"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,29]]},"references-count":20,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2026,5]]}},"alternative-id":["info17050426"],"URL":"https:\/\/doi.org\/10.3390\/info17050426","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,29]]}}}