Imputing missing values in Collaborative Filtering using pattern frequent itemsets

Chujai, Pasapitch; Rasmequan, Suwanna; Suksawatchon, Ureerat; Suksawatchon, Jakkarin

doi:10.1109/ieecon.2014.6925873

Cited by 2 publications

(2 citation statements)

References 8 publications

(8 reference statements)

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“…Chujai et al [7] proposed an approach for imputing missing values based on the pattern of frequent itemsets to address sparsity problem. The approach utilises not only the ratings for the items but include more demographic information of the user and item to fill up the missing rating data in the user-item rating matrix by mining frequent itemsets in the dataset.…”

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

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AutoImpute: Autoencoder based imputation of single-cell RNA-seq data

Talwar

Mongia

Sengupta

et al. 2018

Sci Rep

146

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The emergence of single-cell RNA sequencing (scRNA-seq) technologies has enabled us to measure the expression levels of thousands of genes at single-cell resolution. However, insufficient quantities of starting RNA in the individual cells cause significant dropout events, introducing a large number of zero counts in the expression matrix. To circumvent this, we developed an autoencoder-based sparse gene expression matrix imputation method. AutoImpute, which learns the inherent distribution of the input scRNA-seq data and imputes the missing values accordingly with minimal modification to the biologically silent genes. When tested on real scRNA-seq datasets, AutoImpute performed competitively wrt., the existing single-cell imputation methods, on the grounds of expression recovery from subsampled data, cell-clustering accuracy, variance stabilization and cell-type separability.

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Imputing missing values in Collaborative Filtering using pattern frequent itemsets

Cited by 2 publications

References 8 publications

An Enhanced Data Sparsity Reduction Method for Effective Collaborative Filtering Recommendations

An Enhanced Data Sparsity Reduction Method for Effective Collaborative Filtering Recommendations

AutoImpute: Autoencoder based imputation of single-cell RNA-seq data

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