Review
Version 1
Preserved in Portico This version is not peer-reviewed
A Survey of Sequential Pattern Based E-Commerce Recommendation Systems
Version 1
: Received: 23 August 2023 / Approved: 23 August 2023 / Online: 24 August 2023 (09:45:51 CEST)
A peer-reviewed article of this Preprint also exists.
Ezeife, C.I.; Karlapalepu, H. A Survey of Sequential Pattern Based E-Commerce Recommendation Systems. Algorithms 2023, 16, 467. Ezeife, C.I.; Karlapalepu, H. A Survey of Sequential Pattern Based E-Commerce Recommendation Systems. Algorithms 2023, 16, 467.
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 reviews focuses on algorithmic techniques of existing E-commerce recommendation systems that are sequential pattern based such as ChoRec05, ChenRec09, HuangRec09, LiuRec09, ChoiRec12, Hybrid Model RecSys16, Product RecSys16, SainiRec17, HPCRec18 and HSPCRec19. It provides a comprehensive and comparative performance analysis of these systems, exposing their methodologies, achievements, limitations, and potentials for solving more important problems in this domain. The review showed that integrating sequential pattern mining of historical purchase and/or click sequences into user-item matrix for collaborative filtering (i) improved recommendation accuracy (ii) reduced user-item rating data sparsity (iii) increased novelty rate of recommendations and (iv) improved scalability of the recommendation system.
Keywords
Recommendation systems; collaborative filtering; sequential patterns; e-commerce; purchase and clickstream history
Subject
Computer Science and Mathematics, Computer Science
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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