Document Type
Article
Publication Date
10-1-2023
Publication Title
Algorithms
Volume
16
Issue
10
Keywords
collaborative filtering, e-commerce, purchase and click stream, recommendation systems, sequential patterns
Abstract
E-commerce recommendation systems usually deal with massive customer sequential databases, such as historical purchase or click stream sequences. Recommendation systems’ 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–item 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–item matrix for collaborative filtering can (i) improve recommendation accuracy, (ii) reduce user–item rating data sparsity, (iii) increase the novelty rate of recommendations, and (iv) improve the scalability of recommendation systems.
DOI
10.3390/a16100467
E-ISSN
19994893
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Ezeife, Christie I. and Karlapalepu, Hemni. (2023). A Survey of Sequential Pattern Based E-Commerce Recommendation Systems. Algorithms, 16 (10).
https://scholar.uwindsor.ca/computersciencepub/60