• P. Silpa Chaitanya
• A. Eesha Pravalli
• K. Leela Rani
• G.Nandini
• B.Venkateswari

Nowadays, e-commerce websites are one of the main rising trends that facilitate online selection, and selling of products suggesting an algorithm that aims to forecast the preference (or rating) that a user would something. Collaboration filtering is one such effective way to offer suggestion for the users. We present two types of collaborative filtering techniques in this paper: memory-based and model-based. Algorithms based on memory: item-based collaborative filtering (IBCF) and user-based collaborative filtering (UBCF) filtration (IBCF). RandomForest Classification and K-Means are examples of model-based algorithms. Despite several drawbacks, the K-means clustering algorithm is a popular partitional clustering method for recommender systems due to its low complexity and ease of use.

Recommendation System, Collaborative Filtering, K-means clustering, Item- based recommendation, User-based recommendation

"Developing a Collaborative Filtering Recommendation System for E-commerce Using k-means Clustering", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 11, page no.449-456, November-2024, Available :http://www.jetir.org/papers/JETIRGO06047.pdf

"Developing a Collaborative Filtering Recommendation System for E-commerce Using k-means Clustering", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 11, page no. pp449-456, November-2024, Available at : http://www.jetir.org/papers/JETIRGO06047.pdf