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IAES International Journal of Artificial Intelligence (IJ-AI)
Published by Institute of Advanced Engineering and Science
ISSN : 20894872     EISSN : 22528938     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Engineering
IAES International Journal of Artificial Intelligence (IJ-AI) publishes articles in the field of artificial intelligence (AI). The scope covers all artificial intelligence area and its application in the following topics: neural networks; fuzzy logic; simulated biological evolution algorithms (like genetic algorithm, ant colony optimization, etc); reasoning and evolution; intelligence applications; computer vision and speech understanding; multimedia and cognitive informatics, data mining and machine learning tools, heuristic and AI planning strategies and tools, computational theories of learning; technology and computing (like particle swarm optimization); intelligent system architectures; knowledge representation; bioinformatics; natural language processing; multiagent systems; etc.
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Articles 81 Documents
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Search results for , issue "Vol 14, No 3: June 2025" : 81 Documents clear
Uncertainty-aware contextual multi-armed bandits for recommendations in e-commerce Subramani, Anantharaman; Kumar, Niteesh; Chowdhury, Arpan Dutta; Prajapat, Ramgopal
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i3.pp2519-2527

Abstract

The growing e-commerce landscape has seen a shift towards personalized product recommendations, which play a critical role in influencing consumer behavior and driving revenue. This study explores the efficacy of contextual multi-armed bandits (CMAB) in optimizing personalized recommendations by intelligently balancing exploration and exploitation. Recognizing the inherent uncertainty in user behaviors, we propose an enhanced CMAB policy that incorporates item correlation matrix as an additional component of uncertainty to the conventional binary exploration and exploitation setup of bandit policies. Our approach aims to increase the overall relevance of recommendations through the 'triadic framework’ of CMAB, that seamlessly integrates with existing bandit policies, enabling adaptive recommendations based on diverse user attributes. By outperforming traditional models, this uncertainty-aware method demonstrates its potential in refining recommendation accuracy, thus maximizing revenue in a competitive e-commerce environment. Future research will explore dynamic uncertainty modeling and cross-domain applications to further advance the field.

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