LEVERAGING COLLABORATIVE FILTERING AND ASSOCIATION MINING FOR ONLINE PRODUCT RECOMMENDATION SYSTEM

Volume 22 No 4 (2024)

Akshita Sunerah

DOI: 10.48047/nq.2024.22.4.nq24026

Abstract

Recommendation engines are used to offer recommendations for products to purchase or events to attend. They lead consumers toward products that meet their needs by condensing the quantity of the informational database. Many ways have been developed for item recommendation, such as content mining, collaborative, and association methods. This research addresses the problem of data sparsity by combining association rule mining and collaborative-based filtering to increase performance. The results are shown, and the recommended recommendation algorithms perform better than the current ones and solve issues with scalability and data sparsity.

Keywords

Collaborative filtering, Association rule mining.

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