Experimental Hybrid Technique for Enhancing the Quality of Personalized Product Recommendation System using Deep Learning

Authors

  • Yogesh Gurav Professor, Dr. D. Y. Patil Technical Campus, Varale-Talegaon, Pune.
  • S. K. Prashanth Associate Professor, Dept of IT, Vasavi College of Engineering, Hyderabad.
  • Asma A. Shaikh Assistant Professor, Marathwada Mitra Mandal's College of Engineering, Pune
  • M. Ravichand Professor of English, Mohan Babu University. erstwhile Sree Vidyanikethan Engineering College
  • Kadam Vikas Samarthrao Assistant Professor, Dept. of Computer Engineering, Sinhgad Institute of Technology, Lonavala
  • Vinayak Biradar Assistant Professor, Dept of IT, Vardhaman College of Engineering, Hyderabad, Telangana India

Keywords:

Deep Learning, Recommendation System, IA-CNN Algorithm, Amazon product rating dataset, hybrid recommendation algorithm

Abstract

Deep learning has recently gained a lot of grip in recommender systems. Hybrid recommendation systems, content-based recommendation and Deep learning were all used in multiple ways. Big data has been doing this for nearly 10 years, and the amount of available data on the network will be quickly growing. When challenged with complicated and huge data sets, it's indeed difficult for many people to obtain the necessary data rapidly. At this point, the recommendation system, including its features, is one of the most significant techniques for communicating with the large data overload issue. The development of recommendation algorithms has been aided by the growth of the e-commerce industry in particular. Traditional single recommendation algorithms are plagued by data sparsity, long-tail items and cold start. At this point, hybrid recommendation algorithms can efficiently keep away from some of the flaws of single algorithms. In response to these concerns, this paper proposes an experimental hybrid technique for enhancing the quality of the personalized product recommendation system algorithm that depends on deep learning IA-CNN to give back for a single collaborative model's limitations. To generalize and categorize the output results, first, the system employs a comprehensive approach, fusing product- and user-based collaborative filtering strategies. That is the methodology that the algorithm uses. Improved deep learning techniques are then used to capture nonlinear interactions between users and products that are more detailed and abstract. Finally, we devised experiments to test the algorithm's efficacy. Tests on the Amazon product rating dataset are performed against the benchmark algorithm, and the outcomes show that the proposed IA-CNN algorithm outperforms the on the test dataset, the benchmark algorithm was used for rating prediction.

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Published

21.09.2023

How to Cite

Gurav, Y. ., Prashanth, S. K. ., Shaikh, A. A. ., Ravichand, M. ., Samarthrao, K. V. ., & Biradar, V. . (2023). Experimental Hybrid Technique for Enhancing the Quality of Personalized Product Recommendation System using Deep Learning. International Journal of Intelligent Systems and Applications in Engineering, 11(4), 376–386. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3535

Issue

Vol. 11 No. 4 (2023)

Section

Research Article

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