Article: Strongly correlated high-utility item-sets recommendation in e-commerce applications using EGUI-tree over data stream Journal: International Journal of Grid and Utility Computing (IJGUC) 2025 Vol.16 No.4 pp.325 - 337 Abstract: The product recommendation feature in e-commerce applications plays a vital role in helping customers choose the right items. By analysing their purchase history, the system recommends relevant items. Although customers have the freedom to choose from the suggestions, if they do purchase them, it benefits both buyers and sellers. Sellers experience increased sales, while buyers save time searching for products. To meet these requirements, a recommendation system should prioritise items with High Utility (HU) and strong correlation. HU items generate high profits, and strongly correlated items have a higher probability of being selected. Here, since up-to-date data in the data stream are got, the stream of purchase transactions is mined using the sliding window technique to extract such item-sets. To store the immediately processed results, a tree like structure termed Extended Global Utility Item-sets Tree is used. Experimental results on real-time data prove the proposed algorithm is optimistic and scalable. Inderscience Publishers - linking academia, business and industry through research

Title: Strongly correlated high-utility item-sets recommendation in e-commerce applications using EGUI-tree over data stream

Authors: P. Amaranatha Reddy; M.H.M. Krishna Prasad; S. Rao Chintalapudi

Addresses: Department of Computer Science and Engineering, University College of Engineering (UCE), Jawaharlal Nehru Technological University (JNTU), Kakinada, Andhra Pradesh, India; Department of Computer Applications, Government Degree College, Nandikotkur, Andhra Pradesh, India ' Department of Computer Science and Engineering, University College of Engineering (UCE), Jawaharlal Nehru Technological University (JNTU), Kakinada, Andhra Pradesh, India ' Department of CSE (AI&ML), CMR Technical Campus, Kandlakoya, Telangana, Hyderabad, India

Abstract: The product recommendation feature in e-commerce applications plays a vital role in helping customers choose the right items. By analysing their purchase history, the system recommends relevant items. Although customers have the freedom to choose from the suggestions, if they do purchase them, it benefits both buyers and sellers. Sellers experience increased sales, while buyers save time searching for products. To meet these requirements, a recommendation system should prioritise items with High Utility (HU) and strong correlation. HU items generate high profits, and strongly correlated items have a higher probability of being selected. Here, since up-to-date data in the data stream are got, the stream of purchase transactions is mined using the sliding window technique to extract such item-sets. To store the immediately processed results, a tree like structure termed Extended Global Utility Item-sets Tree is used. Experimental results on real-time data prove the proposed algorithm is optimistic and scalable.

Keywords: HUIM; high utility item-set mining; recommendation system; utility; correlation; data stream; sliding window.

DOI: 10.1504/IJGUC.2025.147676

International Journal of Grid and Utility Computing, 2025 Vol.16 No.4, pp.325 - 337

Received: 21 Oct 2022
Accepted: 25 Feb 2023
Published online: 25 Jul 2025 *

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Title: Strongly correlated high-utility item-sets recommendation in e-commerce applications using EGUI-tree over data stream

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