The team employed a hybrid bibliometric–systematic literature review methodology, guided by the PRISMA framework, combining quantitative science mapping with qualitative thematic synthesis. Starting from 162 records retrieved from Scopus on January 28, 2026, two independent reviewers screened titles and abstracts against predefined inclusion and exclusion criteria, ultimately retaining 135 publications across 52 academic sources. The field exhibits an annual growth rate of 14.35 percent, a strikingly young average document age of 2.49 years, and an average of 4.25 citations per document drawn from a cumulative base of 5,946 cited works. Authorship analysis revealed 521 contributing authors with an average of 4.62 co-authors per paper, reflecting the deeply interdisciplinary character of a research area that straddles computer science, marketing, and information systems.

The temporal picture is one of explosive acceleration. Before 2020, scholarly output on AI recommenders in marketplace contexts was sporadic, characteristic of an exploratory phase. A notable increase emerged in 2020, followed by a sharp surge from 2023 onward, with publication peaks of 44 documents in 2024 and 48 in 2025. The authors attribute this trajectory to structural shifts in consumer behavior during and after the COVID-19 pandemic, which accelerated digital adoption and pushed firms to prioritize scalable, automated personalization. As online platforms absorbed enormous volumes of behavioral data, machine learning and deep learning architectures became the default machinery for modeling user–item interactions, transforming marketplaces from transaction-oriented platforms into intelligence-driven ecosystems in which product discovery itself is algorithmically mediated.

Methodologically, the reviewed literature remains dominated by traditional machine learning approaches, prized for their accessibility and modest computational demands. Collaborative filtering and deep learning methods form the second tier, marking a clear shift toward representation learning and data-driven personalization. Deep models—ranging from session-based neural networks to stacked denoising autoencoders—capture nonlinear preference patterns that classical techniques miss, while hybrid architectures that blend multiple recommendation strategies show improved accuracy and resilience against persistent problems such as data sparsity and the cold-start dilemma. Sentiment analysis and natural language processing, including BERT-based frameworks, are increasingly woven into recommendation pipelines, allowing systems to incorporate the emotional and attitudinal signals embedded in reviews and ratings rather than relying solely on transaction histories.

Keyword co-occurrence mapping in VOSviewer revealed six thematic clusters that the authors interpret through a proposed multi-level framework linking AI architecture, consumer cognition, and marketplace implementation. Clusters one and three concern the technological core: recommendation techniques, natural language processing, and predictive analytics that forecast purchase behavior from classification algorithms, random forests, and recurrent neural networks. Clusters two and four address the human side—how personalization intensity, explanation interfaces, and adaptive content shape satisfaction, trust, and purchase intention. Clusters five and six concern platform environments and system integration: interface design, e-service quality, scalable backend architectures, real-time data pipelines, and the governance frameworks required to keep personalization lawful and reliable at industrial scale.

The behavioral analysis draws heavily on two theoretical pillars. The Stimulus–Organism–Response model treats algorithmic features—personalization depth, adaptive ranking, transparency cues—as external stimuli that shape internal cognitive and affective states, which in turn drive engagement and purchasing. The Theory of Planned Behavior explains how attitudes, subjective norms, and perceived behavioral control convert those internal states into intentions. Within this lens, explainable AI emerges as more than a compliance feature: studies show that attribute-based explanations raise user trust and lower algorithmic anxiety in utilitarian shopping contexts, while perceived fairness and privacy protection feed a multidimensional trust construct spanning the recommender itself, the platform, and the individual recommendations it delivers.

Citation analysis exposes the field’s intellectual DNA and its blind spots. The most cited work, a machine learning recommender built on association rule mining by Loukili and colleagues, exemplifies performance-oriented research that prizes predictive accuracy. The second most cited study advances deep neural collaborative filtering, capturing nonlinear preference structures. The third integrates multitask deep learning to predict buying behavior from affective signals in user-generated content. Together these milestones trace an evolution from rule-based optimization to neural architectures to sentiment-aware personalization—yet the authors note that academic recognition remains concentrated on methodological innovation, while trust sustainability, algorithmic bias, and long-term deployment outcomes are comparatively underexplored in the field’s most influential papers.

Perhaps the review’s most consequential finding is the persistent implementation gap between experimental prototypes and production-ready systems. Many algorithms achieve impressive predictive performance in benchmarks, but far fewer studies address infrastructure scalability, data governance, privacy compliance, interoperability, or integration with enterprise architectures. Federated learning approaches and compliance-aware backend designs point toward architectures that can personalize while respecting regulatory constraints, and API-driven integration with unified data normalization is identified as essential for delivering consistent personalization across channels. Geographically, research output is heavily concentrated in India, China, and the United States, with emerging contributions from Indonesia, Morocco, and Malaysia—a pattern the authors link to the maturity of digital market ecosystems and the dominance of fast-turnaround conference venues, which account for roughly 71 percent of the corpus.

The authors also acknowledge the limits of their synthesis. Reliance on a single database may have excluded relevant work indexed elsewhere; the conference-heavy dataset may overrepresent algorithmic advances relative to behavioral theory; and only English-language publications were included, potentially omitting studies from major e-commerce regions. The proposed multi-level framework is explicitly conceptual rather than statistically validated, intended to organize existing knowledge and guide future inquiry. Even so, the synthesis offers a structured roadmap organized around five directions: deeper theoretical integration between behavioral science and algorithm design, longitudinal studies of effects such as algorithm fatigue and over-personalization, ethical research on explainability and bias mitigation, technological work on context-aware and generative personalization, and managerial attention to deployment feasibility.

The overriding message is deceptively simple: a recommender system is only as effective as the weakest of its three interdependent layers. A model that is accurate but opaque erodes trust; a system that is trusted but unscalable never reaches production; an architecture that is scalable but psychologically tone-deaf fails to convert engagement into loyalty. As digital marketplaces pivot from static recommendation lists toward immersive, generative, and real-time personalization, the review argues that the next generation of AI commerce will be judged not by prediction accuracy alone, but by its capacity to be transparent, trustworthy, and deployable—a reframing with profound implications for the platforms that mediate billions of consumer decisions every day.

Subject of Research: A systematic review of AI-based recommender systems in online marketplaces, integrating algorithmic architectures, consumer behavior, and personalization

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

Article References: Artificial intelligence recommender systems in online marketplaces integrating architectures consumer behavior and personalization. (n.d.). https://doi.org/10.1007/s44163-026-02182-3

Image Credits: AI Generated

DOI: 10.1007/s44163-026-02182-3

Keywords: artificial intelligence, recommender systems, online marketplaces, personalization, consumer behavior, e-commerce, machine learning, deep learning, collaborative filtering, explainable AI, bibliometric analysis, digital commerce