TY  - JOUR
AU  - Patro, S. Gopal Krishna
PY  - 2025
DA  - 2025/12/08
TI  - Dynamic Hybrid Recommendation Approach for Improving Accuracy in E-Commerce with Limited User Data
JO  - Next-Generation Computing Systems and Technologies
T2  - Next-Generation Computing Systems and Technologies
JF  - Next-Generation Computing Systems and Technologies
VL  - 1
IS  - 2
SP  - 62
EP  - 78
DO  - 10.62762/NGCST.2025.832339
UR  - https://www.icck.org/article/abs/NGCST.2025.832339
KW  - e-commerce
KW  - hybrid
KW  - recommendation system (RS)
KW  - security
AB  - 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 systems. A research methodology is employed that integrates context awareness and sequential pattern mining, combining conventional and advanced recommendation techniques within a unified framework. This hybrid strategy tackles challenges posed by Cold Start scenarios while optimizing the use of scarce user interaction data. Preliminary findings indicate that the hybrid approach significantly improves recommendation performance, particularly in settings with limited historical data. By utilizing contextual and sequential information, the proposed system outperforms existing methods in terms of accuracy and personalization. In conclusion, the study demonstrates the effectiveness of hybrid recommendation systems in addressing fundamental e-commerce challenges such as data sparsity and the Cold Start problem. The adaptability of the approach enables accurate predictions without requiring extensive user history. Future research will focus on the scalability of this strategy across diverse e-commerce platforms to deliver more individualized user experiences.
SN  - 3070-3328
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  - 

@article{Patro2025Dynamic,
  author = {S. Gopal Krishna Patro},
  title = {Dynamic Hybrid Recommendation Approach for Improving Accuracy in E-Commerce with Limited User Data},
  journal = {Next-Generation Computing Systems and Technologies},
  year = {2025},
  volume = {1},
  number = {2},
  pages = {62-78},
  doi = {10.62762/NGCST.2025.832339},
  url = {https://www.icck.org/article/abs/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 systems. A research methodology is employed that integrates context awareness and sequential pattern mining, combining conventional and advanced recommendation techniques within a unified framework. This hybrid strategy tackles challenges posed by Cold Start scenarios while optimizing the use of scarce user interaction data. Preliminary findings indicate that the hybrid approach significantly improves recommendation performance, particularly in settings with limited historical data. By utilizing contextual and sequential information, the proposed system outperforms existing methods in terms of accuracy and personalization. In conclusion, the study demonstrates the effectiveness of hybrid recommendation systems in addressing fundamental e-commerce challenges such as data sparsity and the Cold Start problem. The adaptability of the approach enables accurate predictions without requiring extensive user history. Future research will focus on the scalability of this strategy across diverse e-commerce platforms to deliver more individualized user experiences.},
  keywords = {e-commerce, hybrid, recommendation system (RS), security},
  issn = {3070-3328},
  publisher = {Institute of Central Computation and Knowledge}
}