Preprocessing matrix factorization for solving data sparsity on memory-based collaborative filtering

Ardimansyah, Mochamad Iqbal; Huda, Arief Fatchul; Baizal, Z. K. A.

doi:10.1109/icsitech.2017.8257168

Cited by 5 publications

(3 citation statements)

References 13 publications

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“…Ardimansyah, Huda and Baizal [1] presented an approach based on matrix factorisation pre-processing to solve the problem of sparse explicit rating data for memory-based CF recommender. The approach pre-processes empty rating values by filling them with the scores estimated from matrix factorisation.…”

Section: Related Workmentioning

confidence: 99%

“…Numerous studies have been conducted on CF systems to deal with the data sparsity problem for predicting correct item rating in order to provide good recommendations [15,13,23,25,5,7,20,22,17,1,12,4 ]. Despite these studies, several challenges are left on resolved, which include: The existing method [4] fails to pre-process the missing ratings of the new items which increase the sparsity of the rating matrix.…”

Section: Introductionmentioning

confidence: 99%

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An Enhanced Data Sparsity Reduction Method for Effective Collaborative Filtering Recommendations

Roko¹,

Almu²,

Saidu³

2020

IJEME

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Collaborative filtering recommender system suffers from data sparsity problem due to its reliance on numerical ratings to provide recommendations to users. This problem makes it difficult for the system to compute accurate similar neighbours for the items and provide good quality recommendations. Existing methods fail to pre-process the missing ratings of the new items and to predict cold items to the active users which lead to poor quality recommendations. In this work, a sparsity reduction method is presented to improve the quality of recommendations. The method utilises Bi-Separated clustering algorithm to cluster the ratings matrix simultaneously into users and items bi-clusters based on ratings classification. It also employs Bi-Mean Imputation algorithm to fill the missing ratings in the bi-clusters using the estimated means. The method then performs the traditional collaborative filtering process on the new rating matrix for cold items prediction. The experimental results demonstrated that compared to the existing method, the proposed BiSCBiMI improves density of the rating matrix by 5.75%, 10.73% and 7.35% as well as Mean Absolute Error (MAE) of the new items prediction for all of the considered datasets. The results indicated that, the proposed approaches are effective in reducing the data sparsity problem as well as items prediction, which in turn returns good quality recommendations.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Enhanced Data Sparsity Reduction Method for Effective Collaborative Filtering Recommendations

Roko¹,

Almu²,

Saidu³

2020

IJEME

View full text Add to dashboard Cite

show abstract

“…As a result, the accuracy of recommendations will decrease and the system cannot generate recommendations that are relevant to the user. This inaccuracy of product recommendations to users can lead to decreased user confidence in the system [4].…”

Section: Introductionmentioning

confidence: 99%

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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Games Recommender System Using Singular Value Decomposition

Jadulhaq,

Baizal

2023

2023 3rd International Conference on Intelligent Cybernetics Technology &Amp; Applications (ICICyTA)

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Preprocessing matrix factorization for solving data sparsity on memory-based collaborative filtering

Cited by 5 publications

References 13 publications

An Enhanced Data Sparsity Reduction Method for Effective Collaborative Filtering Recommendations

An Enhanced Data Sparsity Reduction Method for Effective Collaborative Filtering Recommendations

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

Games Recommender System Using Singular Value Decomposition

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