{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T07:08:37Z","timestamp":1776064117403,"version":"3.50.1"},"reference-count":48,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2023,10,3]],"date-time":"2023-10-03T00:00:00Z","timestamp":1696291200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Natural Science and Engineering Research Council (NSERC) of Canada","award":["OGP 0194134"],"award-info":[{"award-number":["OGP 0194134"]}]},{"name":"University of Windsor","award":["OGP 0194134"],"award-info":[{"award-number":["OGP 0194134"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>E-commerce recommendation systems usually deal with massive customer sequential databases, such as historical purchase or click stream sequences. Recommendation systems\u2019 accuracy can be improved if complex sequential patterns of user purchase behavior are learned by integrating sequential patterns of customer clicks and\/or purchases into the user\u2013item rating matrix input of collaborative filtering. This review focuses on algorithms of existing E-commerce recommendation systems that are sequential pattern-based. It provides a comprehensive and comparative performance analysis of these systems, exposing their methodologies, achievements, limitations, and potential for solving more important problems in this domain. The review shows that integrating sequential pattern mining of historical purchase and\/or click sequences into a user\u2013item matrix for collaborative filtering can (i) improve recommendation accuracy, (ii) reduce user\u2013item rating data sparsity, (iii) increase the novelty rate of recommendations, and (iv) improve the scalability of recommendation systems.<\/jats:p>","DOI":"10.3390\/a16100467","type":"journal-article","created":{"date-parts":[[2023,10,3]],"date-time":"2023-10-03T07:28:29Z","timestamp":1696318109000},"page":"467","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A Survey of Sequential Pattern Based E-Commerce Recommendation Systems"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9424-9223","authenticated-orcid":false,"given":"Christie I.","family":"Ezeife","sequence":"first","affiliation":[{"name":"School of Computer Science, University of Windsor, 401 Sunset Ave., Windsor, ON N9B3P4, Canada"}]},{"given":"Hemni","family":"Karlapalepu","sequence":"additional","affiliation":[{"name":"School of Computer Science, University of Windsor, 401 Sunset Ave., Windsor, ON N9B3P4, Canada"}]}],"member":"1968","published-online":{"date-parts":[[2023,10,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Aggarwal, C.C. 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