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Exploring the Landscape of Hybrid Recommendation Systems in E-Commerce: A Systematic Literature Review
Linnaeus University.ORCID iD: 0009-0006-5065-7731
University of New Brunswick, Canada.ORCID iD: 0000-0001-7092-2244
Linnaeus University.ORCID iD: 0000-0003-0512-6350
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.ORCID iD: 0000-0001-6745-4398
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2024 (English)In: IEEE Access, E-ISSN 2169-3536, Vol. 12, p. 28273-28296Article, review/survey (Refereed) Published
Abstract [en]

This article presents a systematic literature review on hybrid recommendation systems (HRS) in the e-commerce sector, a field characterized by constant innovation and rapid growth. As the complexity and volume of digital data increases, recommendation systems have become essential in guiding customers to services or products that align with their interests. However, the effectiveness of single-architecture recommendation algorithms is often limited by issues such as data sparsity, challenges in understanding user needs, and the cold start problem. Hybridization, which combines multiple algorithms in different methods, has emerged as a dominant solution to these limitations. This approach is utilized in various domains, including e-commerce, where it significantly improves user experience and sales. To capture the recent trends and advancements in HRS within e-commerce over the past six years, we review the state-of-the-art overview of HRS within e-commerce. This review meticulously evaluates existing research, addressing primary inquiries and presenting findings that contribute to evidence-based decision-making, understanding research gaps, and maintaining transparency. The review begins by establishing fundamental concepts, followed by detailed methodologies, findings from addressing the research questions, and exploration of critical aspects of HRS. In summarizing and incorporating existing research, this paper offers valuable insights for researchers and outlines potential avenues for future research, ultimately providing a comprehensive overview of the current state and prospects of HRS in e-commerce.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2024. Vol. 12, p. 28273-28296
Keywords [en]
E-commerce, hybrid recommendation systems, recommendation systems, systematic literature review, Behavioral research, Decision making, Market Research, Recommender systems
National Category
Information Systems
Identifiers
URN: urn:nbn:se:bth-26045DOI: 10.1109/access.2024.3365828ISI: 001174345100001Scopus ID: 2-s2.0-85186846425OAI: oai:DiVA.org:bth-26045DiVA, id: diva2:1843233
Available from: 2024-03-08 Created: 2024-03-08 Last updated: 2024-04-04Bibliographically approved

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Jusufi, Ilir

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