Summary

Dr. S Gopal Krishna Patro is working as an Assistant Professor, Department of CSE (AI & ML), School of Engineering, Sreenidhi University, Hyderabad, Telangana, India. He completed his Ph. D. in CSE (GIET University, India), M. Tech. in CSE & IT (VSSUT Burla, India), B. Tech. in CSE (BPUT, India), Diploma in CSE (SCTE & VT, India). Dr. Patro has more than 10 years of Academic Experience in Teaching, Research and Administration. Dr. Patro has published 43 Scientific Research Articles, including journals, conferences, and 5 Authored Book Chapters, 1 Patent, and 1 Copyright. Dr. Patro has participated as a reviewer in more than 20 peer-reviewed Scientific Articles. Dr. Patro has been awarded numerous prizes for his exceptional presentation style and has attended over 5 professional expert talks and invited talk programs as a Resource Person. Dr. Patro has worked as an organiser and coordinator in more than 50 International Conferences, FDPs and Hackathon Programmes. Dr. Patro has also participated in more than 30 workshops and seminars.

Edited Journals

ICCK Contributions

Articles 1 Edited 0
Open Access | Research Article | 08 December 2025
Dynamic Hybrid Recommendation Approach for Improving Accuracy in E-Commerce with Limited User Data
by
S. Gopal Krishna Patro
Next-Generation Computing Systems and Technologies | Volume 1, Issue 2: 62-78, 2025 | DOI: 10.62762/NGCST.2025.832339
Abstract
The 'Cold Start' problem, characterized by insufficient transaction history leading to inefficient personalization, represents one of the frequent challenges encountered in e-commerce systems. This issue, along with data sparsity resulting from limited product interactions, further complicates the reliability of conventional recommendation engines. The objective of this research is to design a novel hybridized recommendation system that enhances both security and suggestion accuracy by dynamically adapting to user interactions in digital environments. By leveraging contextual information and sequential user behavior patterns, the proposed method addresses gaps left by traditional recommender... More >
Graphical Abstract
Dynamic Hybrid Recommendation Approach for Improving Accuracy in E-Commerce with Limited User Data