Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Ashok, Sakshi; Athulya, S.; Thomas, Jyothi Title A name given to the resource Price Minds: AI-Driven Insights, Recommendations and Dynamic Pricing Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived 2025 IEEE 2nd International Conference on Advances in Modern Age Technologies for Health and Engineering Science, AMATHE 2025 - Proceedings; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/AMATHE65477.2025.11080732" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/AMATHE65477.2025.11080732</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105012713891?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105012713891?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Ashok S., Christ University, Dept. of CSE, Bangalore, 560074, India; Athulya S., Christ University, Dept. of CSE, Bangalore, 560074, India; Thomas J., Christ University, Dept. of CSE, Bangalore, 560074, India Description An account of the resource This research aims to enhance e-commerce systems by leveraging customer behavior analysis, dynamic pricing, and personalized recommendations. With the increasing demand for tailored shopping experiences and competitive pricing, businesses require adaptive solutions. The study integrates synthetic and real-time customer data to identify purchasing patterns and segment customers effectively. Dynamic pricing strategies are applied to optimize revenue while maintaining customer satisfaction. A unified framework combines clustering techniques, real-time data streams, and decision-making models to deliver actionable insights for business operations. The proposed system dynamically adjusts pricing and recommends products based on individual customer preferences and behavior. The approach addresses the growing need for intelligent systems that adapt to market trends and consumer demands. Results demonstrate improved operational efficiency, better customer engagement, and enhanced profitability. This work highlights the importance of real-time analytics and intelligent pricing mechanisms in advancing e-commerce and creating competitive advantages in rapidly evolving markets. 2025 IEEE. Subject The topic of the resource Artificial Intelligence; Customer Segmentation; Dynamic Pricing; Recommendation Systems; Reinforcement Learning Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833150103-7; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online