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Publicação
Machine Learning Algorithms for E-commerce Personalization: A Systematic Literature Review
| Nome: | Descrição: | Tamanho: | Formato: | |
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| 4.29 MB | Adobe PDF |
Autores
Resumo(s)
The increasing reliance on data-driven personalization in e-commerce has positioned machine
learning (ML) algorithms at the forefront of enhancing user engagement and driving conversion.
However, the variety of algorithmic approaches and the inconsistencies in reported outcomes
present a challenge for practitioners and researchers seeking to evaluate their effectiveness.
This study conducts a systematic literature review and bibliometric analysis to assess the
performance of various ML algorithms used in e-commerce personalization and to map the
intellectual structure of the field. Drawing on 32 peer-reviewed articles published between 2014
and 2024, the review compares models such as collaborative filtering, deep neural collaborative
filtering (DNCF), and multivariant user interface (UI) personalization based on metrics
including click-through rate (CTR), time on site, and conversion rate (CR). The findings
indicate that DNCF consistently outperforms traditional models in CTR and recall, while UIdriven strategies show stronger effects on CR. A keyword co-occurrence and citation network
analysis further highlight emerging themes, including sentiment analysis, trust modeling, and
real-time personalization. This research advances the literature by offering a dual perspective
that integrates algorithmic comparison with bibliometric insight, revealing both technical
advancements and thematic fragmentation in the field. It provides practical recommendations
for e-commerce practitioners while also identifying gaps related to emotional engagement
metrics, ethical considerations, and real-time adaptability. The study presents a structured
framework for evaluating ML personalization strategies and proposes a future research agenda
centered on hybrid systems, fairness, and longitudinal impact assessment.
Descrição
Dissertation presented as the partial requirement for obtaining a Master's degree in Data Driven Marketing, specialization in Digital Marketing and Analytics
Palavras-chave
Machine Learning E-commerce Personalization Recommender Systems Systematic Review Bibliometric Analysis SDG 8 - Decent work and economic growth SDG 9 - Industry, innovation and infrastructure SDG 12 - Responsible production and consumption SDG 17 - Partnerships for the goals