OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

BERT-enhanced sentiment analysis for personalized e-commerce recommendations
Ikram Karabila, Nossayba Darraz, Anas El-Ansari, et al.
Multimedia Tools and Applications (2023) Vol. 83, Iss. 19, pp. 56463-56488
Closed Access | Times Cited: 15

Showing 15 citing articles:

AI-Driven Personalization: Generative Models in E-Commerce
Manu Vallabh Mishra
International Journal of Advanced Research in Science Communication and Technology (2025), pp. 110-116
Open Access | Times Cited: 1

A hybrid approach combining sentiment analysis and deep learning to mitigate data sparsity in recommender systems
Ikram Karabila, Nossayba Darraz, Anas El-Ansari, et al.
Neurocomputing (2025), pp. 129886-129886
Closed Access

Multimodal approach to public health interventions using EGG and mobile health technologies
Xiao Zhang, Han Liu, Mingyang Sun, et al.
Frontiers in Public Health (2025) Vol. 12
Open Access

From online reviews to smartwatch recommendation: An integrated aspect-based sentiment analysis framework
Rajeev Kumar Ray, A.R. Singh
Journal of Retailing and Consumer Services (2024) Vol. 82, pp. 104059-104059
Closed Access | Times Cited: 3

Online commodity recommendation model for interaction between user ratings and intensity-weighted hierarchical sentiment: a case study of LYCOM
Chonghui Zhang, Na Zhang, Weihua Su, et al.
Omega (2024) Vol. 129, pp. 103161-103161
Closed Access | Times Cited: 2

Detecting trending products through moving average and sentiment analysis
Nossayba Darraz, Ikram Karabila, Anas El-Ansari, et al.
Multimedia Tools and Applications (2024)
Closed Access | Times Cited: 1

Enhanced E-commerce Recommender System Based on Deep Learning and Ensemble Approaches
Ikram Karabila, Nossayba Darraz, Anas El-Ansari, et al.
(2024), pp. 1-8
Closed Access

A novel E-commerce recommender system using deep learning approaches*
Ikram Karabila, Nossayba Darraz, Anas El-Ansari, et al.
(2024), pp. 1-8
Closed Access

Improving Hybrid Recommendations with VADER-Powered Sentiment Analysis*
Nossayba Darraz, Ikram Karabila, Anas El-Ansari, et al.
(2024), pp. 1-7
Closed Access

Addressing data sparsity and cold-start challenges in recommender systems using advanced deep learning and self-supervised learning techniques
Ikram Karabila, Nossayba Darraz, Anas El-Ansari, et al.
Journal of Experimental & Theoretical Artificial Intelligence (2024), pp. 1-31
Closed Access

A Diffusion Data Enhancement Retentive Model for Sequential Recommendation
Tengqing Wu
5th International Conference on Computer Information Science and Application Technology (CISAT 2022) (2024), pp. 114-118
Closed Access

Integrating social media and deep learning for real-time urban waterlogging monitoring
Muhammad Waseem Boota, Shan‐e‐hyder Soomro, Muhammad Ozair Ahmad, et al.
Journal of Hydrology Regional Studies (2024) Vol. 56, pp. 102070-102070
Open Access

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