Nigerian Professor of artificial intelligence, Celestine Iwendi has received the HerbertSimon Award for Outstanding Contribution in the International Journal of Information Technology and Decision Making (IJITDM).
The award is coming for his research paper presentation in 2022 titled ‘Pointer-Based Item-to-Item Collaborative Filtering Recommendation System Using a Machine Learning Model’ on IJITDM alongside another Nigerian, Ebuka Ibeke, and other other co-authors namely Harshini Eggoni, Sreerajavenkatareddy Velagala, and Gautam Srivastava.
They will be honoured in August 16, during the forthcoming ITQM conference holding in USA.
Reflection on Receiving the Herbert Simon Award for Outstanding Contribution in IJITDM, the Head, Centre of Intelligence of Things, University of Greater Manchester, Bolton, said is both a deeply humbling and profoundly motivating recognition of my work.
He also said, to have their paper selected by the Editor-in-Chief and the Editorial Board of IJITDM, based on criteria such as theoretical impact, methodological rigour, significance to the ITDM field, and academic influence, affirms that our research is not only timely but truly contributing to the advancement of global scholarship in decision-making and intelligent systems.
“This award holds immense personal significance. It represents a culmination of years of focused effort at the intersection of machine learning, collaborative intelligence, and real-world problem-solving, areas that have defined both my academic vision and the mission of the Centre of Intelligence of Things (CIoTh) at the University of Greater Manchester, Bolton. As someone whose work spans AI for good, digital transformation, and interdisciplinary collaboration, this honour reinforces my belief in research that is globally impactful and socially relevant.
“Beyond recognition, the Herbert Simon Award is a spark—it energises me to deepen my inquiry, expand partnerships, and continue producing high-quality, actionable research that informs policy, industry, and technology innovation. I am especially inspired to foster the next generation of researchers and thinkers, encouraging them to uphold the same standard of excellence that this award symbolises,” the Chair, IEEE Computer Society Election Committee (Worldwide) said.
Iwendi, who extended his sincere appreciation to the World Scientific Publishing Company, the Editor-in-Chief, and the award committee for this prestigious honour. said that “is a milestone in his academic journey and a beacon for the many research questions still to be explored.”
According to the paper, in today’s data-driven economy, recommendation systems are essential for optimising user experience and engagement across platforms. This presentation introduces an enhanced item-to-item collaborative filtering approach leveraging a pointer-based machine learning model, combined with deep learning and contextual AI.
The work architecture incorporates neural collaborative filtering and graph-based learning to capture complex user-item interactions and semantic patterns. Temporal and behavioural context signals are integrated to deliver adaptive, real-time recommendations.
According to the paper, the use of explainable AI (XAI) ensures transparency and builds user trust in the system.
“Experimental evaluation shows the proposed model significantly outperforms traditional collaborative filtering in accuracy and user satisfaction, offering a scalable and intelligent solution for contemporary recommendation challenges,” the paper stated.
However, the paper proposed a machine learning model system where 0, 2, 4 are used to rate products. 0 is negative, 2 is neutral, 4 is positive. This will be in addition to the existing review system that takes care of the users’ reviews and comments, without disrupting it.
They implemented this model by using Keras, Pandas and Sci-kit Learning libraries to run the internal work. The proposed approach improved prediction with 79 percent accuracy for Yelp datasets of businesses across 11 metropolitan areas in four countries, along with a mean absolute error (MAE) of 21 percent, precision at 79 percent , recall at 80 percent and F1-Score at 79 percent.
Their model shows scalability advantage and how organizations can revolutionize their recommender systems to attract possible customers and increase patronage. Also, the proposed similarity algorithm was compared to conventional algorithms to estimate its performance and accuracy in terms of its root mean square error (RMSE), precision and recall.
Celestine is an ambassador in the prestigious Manchester Conference Ambassador Programme, a visiting Prof to five Universities, and an IEEE Humanitarian Philanthropist.
He has also received the prestigious recognition of the Royal Academy of Engineering through the Exceptional Talent Scheme, acknowledging his substantial contributions to Artificial Intelligence and its medical applications.
Additionally, he takes pride in his three-year inclusion in Elsevier’s publication, featuring the World’s Top 2% Influential Scientists. Celestine is the Chair of the Election Committee of IEEE Computer Society Worldwide.
While, Dr. Ebuka Ibeke is a Senior Lecturer and course Leader on MSc Business Analytics, MSc Business Analytics for Healthcare Management, and Energy Data Management with Business Analytics at Robert Gordon University.
He is a Senior Fellow of Higher Education Academy, United Kingdom, and a Guest Editor in Multiple journals.
Commenting on the award, he said that “receiving the Herbert Simon Award is a deeply meaningful recognition of our work at the intersection of machine learning, collaborative intelligence, and real-world problem-solving. More than an accolade, it motivates me to expand impactful research, strengthen collaborations, and inspire future researchers to pursue excellence and socially relevant innovation.”
The Herbert A. Simon Award is presented to researchers who have made important lifetime contributions to the field of complex systems science. The Award was named in memory of Herbert A. Simon for his pioneering work on complex systems, artificial intelligence, information processing, decision-making, problem-solving, and organization theory.
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