Title: AI-Powered Sorting in E-Commerce:
Authors: Personalization and Performance Optimization
Volume: 10
Issue: 7
Pages: 97-103
Publication Date: 2026/07/28
Abstract:
The rapid growth of e-commerce platforms has led to an unprecedented increase in the volume and diversity of products available to online consumers, making efficient and personalized product ranking a critical challenge. Traditional sorting algorithms, such as Quick Sort, Merge Sort, and Heap Sort, efficiently order products according to predefined attributes such as price, rating, or popularity. However, these approaches rely on static sorting criteria and fail to capture the diverse preferences, browsing behaviors, purchase histories, and contextual intentions of individual users. This paper proposes a hybrid AI-powered sorting framework that integrates artificial intelligence with classical sorting algorithms to generate personalized product rankings while maintaining computational efficiency. The proposed framework consists of two stages: an AI-based relevance scoring module that evaluates products using multiple features, including query relevance, user preferences, product ratings, popularity, and contextual information, followed by an efficient comparison-based sorting algorithm that orders products according to their predicted relevance scores. The computational complexity of the proposed framework is analyzed, demonstrating that personalized ranking can be achieved with an overall time complexity of O(n log n) while preserving scalability for large-scale e-commerce applications. The framework highlights how artificial intelligence enhances the quality of product ranking by adapting to individual user needs, whereas classical sorting algorithms ensure efficient ordering and rapid response times. Furthermore, the paper discusses practical implementation considerations, including feature engineering, candidate filtering, caching strategies, and the trade-offs between personalization accuracy and computational performance. The proposed approach provides a scalable and flexible foundation for intelligent product ranking systems and offers valuable insights for the development of next-generation personalized e-commerce search and recommendation platforms. Future work will focus on implementing the framework using real-world e-commerce datasets and evaluating its effectiveness through ranking quality, user engagement, and system performance metrics.