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Review
. 2025 Jan 7;11(1):12.
doi: 10.3390/jimaging11010012.

Hybrid Quality-Based Recommender Systems: A Systematic Literature Review

Bihi Sabiri  1 , Amal Khtira  2 , Bouchra El Asri  1 , Maryem Rhanoui  3   4
Affiliations
Review

Hybrid Quality-Based Recommender Systems: A Systematic Literature Review

Bihi Sabiri et al. J Imaging. .

Abstract

As technology develops, consumer behavior and how people search for what they want are constantly evolving. Online shopping has fundamentally changed the e-commerce industry. Although there are more products available than ever before, only a small portion of them are noticed; as a result, a few items gain disproportionate attention. Recommender systems can help to increase the visibility of lesser-known products. Major technology businesses have adopted these technologies as essential offerings, resulting in better user experiences and more sales. As a result, recommender systems have achieved considerable economic, social, and global advancements. Companies are improving their algorithms with hybrid techniques that combine more recommendation methodologies as these systems are a major research focus. This review provides a thorough examination of several hybrid models by combining ideas from the current research and emphasizing their practical uses, strengths, and limits. The review identifies special problems and opportunities for designing and implementing hybrid recommender systems by focusing on the unique aspects of big data, notably volume, velocity, and variety. Adhering to the Cochrane Handbook and the principles developed by Kitchenham and Charters guarantees that the assessment process is transparent and high in quality. The current aim is to conduct a systematic review of several recent developments in the area of hybrid recommender systems. The study covers the state of the art of the relevant research over the last four years regarding four knowledge bases (ACM, Google Scholar, Scopus, and Springer), as well as all Web of Science articles regardless of their date of publication. This study employs ASReview, an open-source application that uses active learning to help academics filter literature efficiently. This study aims to assess the progress achieved in the field of hybrid recommender systems to identify frequently used recommender approaches, explore the technical context, highlight gaps in the existing research, and position our future research in relation to the current studies.

Keywords: big data; hybrid quality-based recommendations; strategy recommender systems; systematic review.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
The basic steps in conducting a systematic literature review.
Figure 2
Figure 2
Literature review process template.
Figure 3
Figure 3
Scopus: number of articles published in the study area from 2020 to 2024.
Figure 4
Figure 4
Web of Science: number of articles published in the study area.
Figure 5
Figure 5
Springer: number of articles published in the study area from 2020 to 2024 without preview-only content.
Figure 6
Figure 6
Google Scholar: number of articles published in the study area from 2020 to 2024.
Figure 7
Figure 7
ACM Digital Library: number of articles published in the study area.
Figure 8
Figure 8
Data selection methods.
Figure 9
Figure 9
Article counts for the distribution of academic paper databases. (a) Ratio of articles vs. database. (b) Academic paper database spread.
Figure 10
Figure 10
The research approach employed: diagram of the PRISMA process for inclusion and exclusion.
Figure 11
Figure 11
Spread of research based on the publication year of chosen papers.
Figure 12
Figure 12
Machine-learning-based ASReview pipeline. Graphic icons denote actions performed by human or computer.
Figure 13
Figure 13
Top 1000 abstract words.
Figure 14
Figure 14
Top 1000 words in whole papers.
Figure 15
Figure 15
Word frequency in abstracts (top 30).
Figure 16
Figure 16
Word frequency: top 1000 words in whole papers.
Figure 17
Figure 17
Confusion matrix for the articles selected for the study.
Figure 18
Figure 18
Multicategorical article analysis with a complete color-coded legend.
Figure 19
Figure 19
Trends in using assessment datasets for recommender system research.
See this image and copyright information in PMC

References

    1. Xu L., Sang X. E-Commerce Online Shopping Platform Recommendation Model Based on Integrated Personalized Recommendation. Sci. Program. 2022;2022:4823828. doi: 10.1155/2022/4823828. - DOI
    1. Hossain I., Palash M., Sejuty A., Tanjim N., Nasim M., Saif S., Suraj A., Haque M., Karim N. A Survey of Recommender System Techniques and the Ecommerce Domain. arXiv. 20222208.07399
    1. Murillo V., Avendano D., Lopez F., Calleros J. A Systematic Literature Review on the Hybrid Approaches for Recommender Systems. Comput. Sist. 2022;26:357–372. doi: 10.13053/cys-26-1-4180. - DOI
    1. Chen R., Hua Q., Chang Y., Wang B., Zhang L., Kong X. A survey of collaborative filtering-based recommender systems: From traditional methods to hybrid methods based on social networks. IEEE Access. 2018;6:64301–64320. doi: 10.1109/ACCESS.2018.2877208. - DOI
    1. De Nadai M., Fabbri F., Gigioli P., Wang A., Li A., Silvestri F., Kim L., Lin S., Radosavljevic V., Ghael S., et al. Personalized Audiobook Recommendations at Spotify Through Graph Neural Networks; Proceedings of the WWW 2024: The ACM Web Conference; Singapore. 13–17 May 2024; pp. 403–412.

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