{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T04:28:51Z","timestamp":1777696131417,"version":"3.51.4"},"reference-count":17,"publisher":"SAGE Publications","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IDA"],"published-print":{"date-parts":[[2022,3,14]]},"abstract":"<jats:p>Recommender systems apply machine learning and data mining techniques for filtering unseen information, and they can provide an opportunity to predict whether a user would be interested in a given item. The main types of recommender systems are collaborative filtering (CF) and content-based filtering, which suffer from scalability and data sparsity resulting in poor quality recommendations and reduced coverage. There are two incremental algorithms based on Singular Value Decomposition (SVD) with high scalability for recommender systems which are named the incremental SVD algorithm and incremental Approximating the Singular Value Decomposition (ApproSVD) algorithm. In both mentioned methods, the estimated value of rank for approximating the recommender systems\u2019 data matrix is chosen experimentally in the related literature. In this paper, we investigate the role of singular values for estimating a more reliable amount of rank in the mentioned dimensionality reduction techniques to improve the recommender systems\u2019 performance. In other words, we offered a strategy for choosing the optimal rank that approximates the data matrix more accurately in incremental algorithms with the help of singular values. The numerical results illustrate that the suggested strategy improves the accuracy of the recommendations and run times of both algorithms when employs for Movielens, Netflix, and Jester dataset.<\/jats:p>","DOI":"10.3233\/ida-205733","type":"journal-article","created":{"date-parts":[[2022,3,22]],"date-time":"2022-03-22T18:19:49Z","timestamp":1647973189000},"page":"447-467","source":"Crossref","is-referenced-by-count":9,"title":["A strategy to estimate the optimal low-rank in incremental SVD-based algorithms for recommender systems"],"prefix":"10.1177","volume":"26","author":[{"given":"Maryam","family":"Bahrkazemi","sequence":"first","affiliation":[{"name":"School of Mathematics, Iran University of Science and Technology, Narmak, Tehran, Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maryam","family":"Mohammadi","sequence":"additional","affiliation":[{"name":"Faculty of Mathematical Sciences and Computer, Kharazmi University, Tehran, Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"issue":"1","key":"10.3233\/IDA-205733_ref1","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/j.csda.2006.11.006","article-title":"Algorithms and applications for approximate nonnegative matrix factorization","volume":"52","author":"Berry","year":"2006","journal-title":"Computational Statistics and Data Analysis, Elsevier"},{"key":"10.3233\/IDA-205733_ref2","unstructured":"D. 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Pr\u00fcgel-Bennett, The advantage of careful imputation sources in sparse data-environment of recommender systems: Generating improved SVD-based recommendations, Informatica 37(1) (2013)."},{"issue":"4","key":"10.3233\/IDA-205733_ref8","doi-asserted-by":"crossref","first-page":"823","DOI":"10.1016\/j.eswa.2004.12.037","article-title":"Collaborative filtering based on iterative principal component analysis","volume":"28","author":"Kim","year":"2005","journal-title":"Expert Systems with Applications"},{"issue":"11","key":"10.3233\/IDA-205733_ref9","doi-asserted-by":"crossref","first-page":"2212","DOI":"10.1080\/03081087.2016.1267104","article-title":"Literature survey on low rank approximation of matrices","volume":"65","author":"Kishore Kumar","year":"2017","journal-title":"Linear and Multilinear Algebra"},{"issue":"3","key":"10.3233\/IDA-205733_ref10","doi-asserted-by":"crossref","first-page":"723","DOI":"10.3233\/IDA-194599","article-title":"User-item content awareness in matrix factorization based collaborative recommender systems","volume":"24","author":"Mohammadi","year":"2020","journal-title":"Intelligent Data Analysis"},{"key":"10.3233\/IDA-205733_ref11","doi-asserted-by":"crossref","unstructured":"Y.J. Park and A. Tuzhilin, The long tail of recommender systems and how to leverage it, in: Proceedings of the 2008 ACM Conference on Recommender Systems, 2008, pp. 11\u201318.","DOI":"10.1145\/1454008.1454012"},{"key":"10.3233\/IDA-205733_ref12","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.patrec.2015.05.019","article-title":"New SVD based initialization strategy for non-negative matrix factorization","volume":"63","author":"Qiao","year":"2015","journal-title":"Pattern Recogn. Lett"},{"key":"10.3233\/IDA-205733_ref13","doi-asserted-by":"crossref","unstructured":"B.M. Sarwar, G. Karypis, J.A. Konstan and J.T. Riedl, Application of dimensionality reduction in recommender systems \u2013 A Case Study, in: Proceeding of ACM Web KDD Workshop on Web Mining for E-Commerce, ACM Press, New York, 2000, pp. 82\u201390.","DOI":"10.21236\/ADA439541"},{"key":"10.3233\/IDA-205733_ref14","unstructured":"B. Sarwar, G. Karypis, J. Konstan and J. Riedl, Incremental singular value decomposition algorithms for highly scalable recommender systems, in: Fifth International Conference on Computer and Information Technology, Citeseer, 2002, pp.\u00a027\u201328."},{"key":"10.3233\/IDA-205733_ref15","doi-asserted-by":"crossref","unstructured":"B. Sarwar, G. Karypis, J. Konstan and J. Riedl, Analysis of recommendation algorithms for e-commerce, in: Proceedings of the 2nd ACM Conference on Electronic Commerce, ACM, 2000, pp. 158\u2013167.","DOI":"10.1145\/352871.352887"},{"key":"10.3233\/IDA-205733_ref17","doi-asserted-by":"crossref","unstructured":"G. Tak\u00e0cs, I. Pil\u00e0szy, B. N\u00e8meth and D. 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Zhang, A personalized recommendation algorithm based on approximating the singular value decomposition (ApproSVD), in: Proceedings of the 2012 IEEE\/WIC\/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology, vol. 02, IEEE Computer Society, 2012, pp. 458\u2013464.","DOI":"10.1109\/WI-IAT.2012.225"}],"container-title":["Intelligent Data Analysis"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/IDA-205733","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:19:20Z","timestamp":1777454360000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/IDA-205733"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,14]]},"references-count":17,"journal-issue":{"issue":"2"},"URL":"https:\/\/doi.org\/10.3233\/ida-205733","relation":{},"ISSN":["1088-467X","1571-4128"],"issn-type":[{"value":"1088-467X","type":"print"},{"value":"1571-4128","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3,14]]}}}