What does this research mean for the field?

While research on AI recommender systems in online marketplaces is rapidly growing and dominated by machine learning approaches, a persistent gap remains between experimental model performance and scalable, consumer-centered deployment. Novelty: ClaimNovelty.SYNTHESIS. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

To synthesize fragmented research on artificial intelligence recommender systems in online marketplaces across technological, behavioral, and infrastructural perspectives.

September 14, 2026Discover Artificial IntelligenceOpen Access

Artificial intelligence recommender systems in online marketplaces integrating architectures consumer behavior and personalization

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Authors

ACArianis Chan RSRani Sukmadewi CWChe Aniza Che Wel

Discussion

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Overview

Systematic review demonstrates rapid growth and deployment gaps in e-commerce AI recommender systems, highlighting the need for scalable consumer-centered personalization.

Key Points

To synthesize fragmented research on artificial intelligence recommender systems in online marketplaces across technological, behavioral, and infrastructural perspectives.
Conducted a PRISMA-guided systematic review and bibliometric analysis of 135 Scopus-indexed publications published between 2007 and 2026.
Evaluated methodological frameworks and thematic cluster distributions across the included e-commerce literature.
Identified rapid scholarly publication growth after 2020, with methodologies dominated by machine learning, collaborative filtering, deep learning, and hybrid systems.
Mapped research into six primary thematic clusters: recommendation techniques, consumer behavior, predictive analytics, user experience, platform environments, and system integration.
Observed a persistent translation gap between theoretical experimental model performance and scalable implementation in live marketplace environments.

Cite This Study

Chan et al. (2026) studied this question.

synapsesocial.com/papers/6aa7b3ee0926e14a848b3372 https://doi.org/10.1007/s44163-026-02182-3

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September 14, 2026Discover Artificial IntelligenceOpen Access

Artificial intelligence recommender systems in online marketplaces integrating architectures consumer behavior and personalization

View Full Paper

Ask AI

Bookmark

Authors

ACArianis Chan RSRani Sukmadewi CWChe Aniza Che Wel

Discussion

Member takes

Overview

Systematic review demonstrates rapid growth and deployment gaps in e-commerce AI recommender systems, highlighting the need for scalable consumer-centered personalization.

Key Points

To synthesize fragmented research on artificial intelligence recommender systems in online marketplaces across technological, behavioral, and infrastructural perspectives.
Conducted a PRISMA-guided systematic review and bibliometric analysis of 135 Scopus-indexed publications published between 2007 and 2026.
Evaluated methodological frameworks and thematic cluster distributions across the included e-commerce literature.
Identified rapid scholarly publication growth after 2020, with methodologies dominated by machine learning, collaborative filtering, deep learning, and hybrid systems.
Mapped research into six primary thematic clusters: recommendation techniques, consumer behavior, predictive analytics, user experience, platform environments, and system integration.
Observed a persistent translation gap between theoretical experimental model performance and scalable implementation in live marketplace environments.

Cite This Study

Chan et al. (2026) studied this question.

synapsesocial.com/papers/6aa7b3ee0926e14a848b3372 https://doi.org/10.1007/s44163-026-02182-3

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