Copyright © 2025 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{183419,
        author = {Suhasan Chintadripet Dillibatcha},
        title = {The Role of AI/ML in Personalizing Recommendations and Increasing Average Order Value},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {3},
        pages = {2315-2324},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=183419},
        abstract = {This paper explores the transformative role of Artificial Intelligence (AI) and Machine Learning (ML) in personalizing recommendations and increasing Average Order Value (AOV) in e-commerce. By leveraging various data sources such as transactional, demographic, contextual, social, and sentiment data, the paper introduces a new, integrated model designed to provide highly personalized product suggestions. The proposed model overcomes the limitations of traditional recommendation systems, including collaborative filtering, content-based filtering, and matrix factorization, by offering more accurate, timely, and context-aware recommendations. Through a comparative analysis of the new model against baseline models, the study demonstrates its superior predictive performance and its potential to significantly enhance AOV. The findings highlight the model’s capacity to dynamically adjust to shifting consumer preferences, improve engagement, and drive higher sales. Furthermore, the paper discusses the implications for practitioners in leveraging AI/ML for business growth and outlines key considerations for policymakers regarding data privacy, ethics, and transparency in AI-driven personalization. The review offers valuable insights into the future of personalized e-commerce, emphasizing the importance of integrating diverse data sources for maximizing customer satisfaction and business profitability.},
        keywords = {AI, Machine Learning, Personalized Recommendations, Average Order Value (AOV), E-commerce, Data Integration, Sentiment Analysis, Consumer Behavior, Collaborative Filtering, Content-Based Filtering, Business Optimization, Data Privacy, Ethics in AI.},
        month = {August},
        }