Product recommendation for e-commerce business by applying principal component analysis (PCA) and K-means clustering: benefit for the society

Bandyopadhyay, Soma; Thakur, S. S.; Mandal, Jyotsna Kumar

doi:10.1007/s11334-020-00372-5

Cited by 41 publications

(19 citation statements)

References 11 publications

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“…Thus, the dimension of user feature vector equals to the dimension of N. As in [16,25], the work here also attempts aims to use just user ratings and no other additional information to gain good rating prediction performance.…”

Section: User Rating Matrix Representationmentioning

confidence: 99%

“…Much endeavor has been devoted in the literatures to learn better data representation that can evade curse of dimensionality for clustering. For example, in [16], the authors apply principal component analysis to reduce the features of product and customers. Later, they apply K-means clustering to develop product recommendation systems for e-commerce business applications.…”

Section: Introductionmentioning

confidence: 99%

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Deep Embedded Fuzzy Clustering Model for Collaborative Filtering Recommender System

Binbusayyis¹

2022

Intelligent Automation &Amp; Soft Computing

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The increasing user of Internet has witnessed a continued exploration in applications and services that can bring more convenience in people's life than ever before. At the same time, with the exploration of online services, the people face unprecedented difficulty in selecting the most relevant service on the fly. In this context, the need for recommendation system is of paramount importance especially in helping the users to improve their experience with best value-added service. But, most of the traditional techniques including collaborative filtering (CF) which is one of the most successful recommendation technique suffer from two inherent issues namely, rating sparsity and cold-start. Inspired by the breakthrough results and great strides of deep learning in a wide variety of applications, this work for the first time proposes a deep clustering model leveraging the strength of deep learning and fuzzy clustering for user-based collaborative filtering (UCF). At first, the proposed model applies deep autoencoder (AE) to learn users' latent feature representation from the original rating matrix and then conducts fuzzy clustering assignments on learnt users' latent representation to form user clusters. More importantly, the proposed model defines a unified objective function combining the reconstruction loss and clustering loss to yield end-toend UCF process that can jointly optimize the process of data representation learning and clustering assignment. The effectiveness of the proposed model is evaluated with two real-world datasets, MoiveLens100K and MovieLens1M using two standard performance metrics. Experimental results of the proposed model demonstrated the prospects for gain in top-K recommendation accuracy against recent related works with resistant to rating sparsity and cold start problems.

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Section: User Rating Matrix Representationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deep Embedded Fuzzy Clustering Model for Collaborative Filtering Recommender System

Binbusayyis¹

2022

Intelligent Automation &Amp; Soft Computing

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“…Data that has been preprocessed then will be reduced its dimensions using the PCA. PCA performs dimensionality reduction of a set of correlated data [1]. PCA works by utilizing orthogonal transformation techniques to transform a set of data with correlated variables into a collection of unrelated linear variables.…”

Section: Datasetmentioning

confidence: 99%

“…Recommender system is an important part of a business strategy in e-commerce [1]. Recommender system is used to provide product recommendations to users with the aim of helping users get the desired product [2].…”

Section: Introductionmentioning

confidence: 99%

“…Research [5] uses the K-Means model to recommend products in stores based on customer categorization. In the ecommerce domain, researchers [1] designed a product recommendation system for users and a stock keeping unit (SKU) for sellers by combining the recommendation results from PCA and K-Means with the recommendation results using K-Means. In education, K-Means has been used to recommend specialization courses [6].…”

Section: Introductionmentioning

confidence: 99%

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E-commerce Recommender System Using PCA and K-Means Clustering

Andra

Baizal

2022

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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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.

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Hydrogen Economy and Its Production Impact on Automobile Industry Forecasting in Ecuador Using Principal Component Analysis

Espin

Estevez

Thirumuruganandham

2021

Computational Science and Its Applications – ICCSA 2021

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Product recommendation for e-commerce business by applying principal component analysis (PCA) and K-means clustering: benefit for the society

Cited by 41 publications

References 11 publications

Deep Embedded Fuzzy Clustering Model for Collaborative Filtering Recommender System

Deep Embedded Fuzzy Clustering Model for Collaborative Filtering Recommender System

E-commerce Recommender System Using PCA and K-Means Clustering

Hydrogen Economy and Its Production Impact on Automobile Industry Forecasting in Ecuador Using Principal Component Analysis

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