E-commerce Recommender System Using PCA and K-Means Clustering
Dublin Core
Title
E-commerce Recommender System Using PCA and K-Means Clustering
Subject
recommender system, collaborative filtering, principal component analysis, k-means, e-commerce
Description
Recently, recommender system has an important role in e-commerce to market products for users. One of recommender system
approach that used in e-commerce is Collaborative Filtering. This system works by providing product recommendations based
on products liked by other users who have similar preferences. However, sparse conditions in user data will cause sparsity
problems, namely the system is difficult to provide recommendations because of the lack of important information needed.
Therefore, we propose an e-commerce product recommendation system based on Collaborative Filtering using Principal
Component Analysis (PCA) and K-Means Clustering. K-Means is used to overcome sparsity problems and to form user clusters
to reduce the amount of data that needs to be processed. While PCA is used to reduce data dimensions and improve clustering
performance of K-Means. The test results using the sports product dataset on the Olist e-commerce show that the proposed
system has a lower RMSE value compared to other methods. For the number of neighbors of 10, 20, 30, and 40, our system
obtains values of 0.771806, 0.75747, 0.75304, 0.75304, and 0.75270
approach that used in e-commerce is Collaborative Filtering. This system works by providing product recommendations based
on products liked by other users who have similar preferences. However, sparse conditions in user data will cause sparsity
problems, namely the system is difficult to provide recommendations because of the lack of important information needed.
Therefore, we propose an e-commerce product recommendation system based on Collaborative Filtering using Principal
Component Analysis (PCA) and K-Means Clustering. K-Means is used to overcome sparsity problems and to form user clusters
to reduce the amount of data that needs to be processed. While PCA is used to reduce data dimensions and improve clustering
performance of K-Means. The test results using the sports product dataset on the Olist e-commerce show that the proposed
system has a lower RMSE value compared to other methods. For the number of neighbors of 10, 20, 30, and 40, our system
obtains values of 0.771806, 0.75747, 0.75304, 0.75304, and 0.75270
Creator
Dendy Andra A.N1
, Z. K. A. Baizal2
, Z. K. A. Baizal2
Publisher
Telkom University
Date
Telkom University
Contributor
Fajar bagus W
Format
PDF
Language
Indonesia
Type
Text
Files
Collection
Citation
Dendy Andra A.N1
, Z. K. A. Baizal2, “E-commerce Recommender System Using PCA and K-Means Clustering,” Repository Horizon University Indonesia, accessed June 1, 2025, https://repository.horizon.ac.id/items/show/9113.