Design and Development of Data-Driven Product Recommender Model for E-Commerce using Behavioral Analytics

Authors

  • D. Srinivasa Kumar Professor, Department of Basic Sciences and Humanities, GMR Institute of Technology, Rajam,Vizianagaram (Dist), Andhra Pradesh, India, Pincode: 532127
  • Kilaru Madhavi Assistant Professor, Department of Humanities and Sciences, VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad, Telangana, India, Pincode: 500090
  • Tenneti Ramprasad Associate Professor, Department of Mathematics, Vasavi College of Engineering (A), Hyderabad, Telangana, India, Pincode: 500031.
  • K. R. Sekhar Assistant Professor, Faculty of Mathematics, School of Technology, The Apollo University, Murukambattu, Chittoor, Andhra Pradesh, India, Pincode: 517127.
  • Srinivasa Rao Dhanikonda Associate Professor, Department of IT, BVRIT HYDERABAD College of Engineering for Women, Hyderabad, Telangana, India, Pincode: 500090
  • CH Ravi Professor, Department of CSE, AVN Institute of Engineering and Technology, Ibrahimpatnam, Hyderabad, Telangana, India, Pincode: 501510

Keywords:

Data-Driven, Recommender System, Behavioral Analytics, E-Commerce, Cross-domain Recommender System, Deep Neural Networks

Abstract

Recommendations assist users in more precisely locating the information they require for a given sample. People all around the world have been drawn to E-Commerce-based businesses in recent years. The Recommendation Model (RM) is an important system in internet business that recommends products to consumers based on their previous actions. Furthermore, the RM is effectively employed by both corporate service suppliers and customers. Furthermore, because so much product information exists online, recommender systems are critical for analyzing the existence of items that should be offered to clients, which enhances customer decision-making by giving extensive knowledge about the product and saves the effort required. However, the complications are recognized and observed from various methodologies as per the literature. To maintain proper RM, the research needs to focus more on data collection and analysis that provide real-time support. Thus, the user behavior data and machine learning concepts are utilized for designing Data-Driven Product Recommender Model (DD-PRM). From the experimental results, it has been determined that the proposed DD-PRM outperforms than the exiting models.

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Published

23.02.2024

How to Cite

Kumar, D. S. ., Madhavi, K. ., Ramprasad, T. ., Sekhar, K. R. ., Dhanikonda, S. R. ., & Ravi, C. . (2024). Design and Development of Data-Driven Product Recommender Model for E-Commerce using Behavioral Analytics. International Journal of Intelligent Systems and Applications in Engineering, 12(17s), 381–392. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4884

Issue

Vol. 12 No. 17s (2024)

Section

Research Article

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