A Context-Aware Recommendation Model for Improving Conversion Rates in E-Commerce Platforms
Keywords:
Artificial Intelligence, E-Commerce, Context-Aware Recommendation Systems, Machine Learning Personalized Recommendation, Conversion Rate Optimization, Deep Learning, Customer Behavior AnalysisAbstract
The rapid expansion of e-commerce platforms has significantly increased the need for intelligent systems that can personalize the online shopping experience and improve customer engagement. Recommendation systems play a crucial role in guiding users toward relevant products; however, traditional recommendation techniques often rely solely on historical user-item interactions and fail to consider contextual information. This limitation reduces their effectiveness in dynamic online environments where user preferences frequently change. This research proposes a context-aware recommendation model designed to improve conversion rates in e-commerce platforms by integrating user behavioral data with contextual attributes such as time, device type, location, and browsing session characteristics. The proposed framework utilizes machine learning techniques to analyze the relationships between users, products, and contextual factors in order to generate highly personalized product recommendations. An experimental evaluation was conducted using a simulated e-commerce dataset to assess the performance of the proposed model. The results demonstrate that incorporating contextual information significantly enhances recommendation accuracy, precision, and recall. Furthermore, the context-aware model achieves a substantial improvement in conversion rates compared with traditional collaborative filtering and hybrid recommendation approaches. The findings highlight the importance of context-aware artificial intelligence systems in modern digital commerce environments. By delivering more relevant and personalized product recommendations, such systems can enhance user experience, increase customer engagement, and generate higher revenue for e-commerce platforms. Future research may explore the integration of reinforcement learning, multimodal data sources, and privacy-preserving techniques to further advance the capabilities of AI-driven recommendation systems.
Downloads
References
J. Zhao, M. Yang, Y. Zhao, X. Hu, W. Zhou, and H. Li, "Mcmarl: Parameterizing value function via mixture of categorical distributions for multi-agent reinforcement learning," IEEE Transactions on Games, vol. 16, no. 3, pp. 556-565, 2023.
J. Reynolds, "eCommerce: a critical review," International Journal of Retail & Distribution Management, vol. 28, no. 10, pp. 417-444, 2000.
C. Yang et al., "Grafting log analysis method of power data based on multi-layer clustering," in 2023 5th International Conference on Frontiers Technology of Information and Computer (ICFTIC), 2023: IEEE, pp. 93-96.
Z. Liu, H. Zheng, D. Chu, Q. Z. Sheng, J. Yu, and X. Xu, "Towards dynamic reconfiguration of composite services via failure estimation of general and domain quality of services," Future Generation Computer Systems, vol. 143, pp. 76-92, 2023.
A. Bandi, B. Kongari, R. Naguru, S. Pasnoor, and S. V. Vilipala, "The rise of agentic ai: A review of definitions, frameworks, architectures, applications, evaluation metrics, and challenges," Future Internet, vol. 17, no. 9, p. 404, 2025.
M. S. Lam et al., "Just-In-Time Objectives: A General Approach for Specialized AI Interactions," arXiv preprint arXiv:2510.14591, 2025.
C. Koo, J. Kim, and R. Alt, "spatial is special:: Special issue on exploration for spatial approach in smart tourism cities," ed, 2023.
A. Raza, "TRADE SECRETS AS A SUBSTITUTE FOR AI PROTECTION: A CRITICAL INVESTIGATION INTO DIFFERENT DIMENSIONS OF TRADE SECRETS," 2024.
J. Barach, "Enhancing intrusion detection with CNN attention using NSL-KDD dataset," in 2024 Artificial Intelligence for Business (AIxB), 2024: IEEE, pp. 15-20.
