Imputing trust network information in NMF-based collaborative filtering

Alghamedy, Fatemah; Wang, Xiwei; Zhang, Jun

doi:10.1145/3190645.3190672

Cited by 4 publications

(13 citation statements)

References 22 publications

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“…Enlightened by these papers, we apply the imputation process to Aux-NMF [7] by utilizing item auxiliary information. Our proposed method is different from [16] in many aspects. First, we impute New-Items which focus on the advertising beside the recommendation.…”

Section: Related Workmentioning

confidence: 89%

“…A novel algorithm called (IMULT) had been proposed in [15] based on the classic Multiplicative Update Rules (MULT), which utilizes imputation to fill out the subset of unknown ratings. Furthermore, [16] proposed an imputation method to impute New-Users. The results show that the proposed approach can handle the New-Users issue and reduce the recommendation errors.…”

Section: Related Workmentioning

confidence: 99%

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Imputing Item Auxiliary Information in NMF-Based Collaborative Filtering

Alghamedy¹,

Zhang²,

Alghamdi³

2018

Computer Science &Amp; Information Technology (CS &Amp; IT)

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The cold-start items, especially the New-Items which did not receive any ratings, have negative impacts on NMF (Nonnegative Matrix Factorization)-based approaches, particularly the ones that utilize other information besides the rating matrix. We propose an NMF based approach in collaborative filtering based recommendation systems to handle the New-Items issue. The proposed approach utilizes the item auxiliary information to impute missing ratings before NMF is applied. We study two factors with the imputation: (1) the total number of the imputed ratings for each New-Item, and (2) the value and the average of the imputed ratings. To study the influence of these factors, we divide items into three groups and calculate their recommendation errors. Experiments on three different datasets are conducted to examine the proposed approach. The results show that our approach can handle the New-Item's negative impact and reduce the recommendation errors for the whole dataset. KEYWORDSCollaborative filtering, recommendation system, nonnegative matrix factorization, item auxiliary information, imputation .

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Section: Related Workmentioning

confidence: 89%

Section: Related Workmentioning

confidence: 99%

Imputing Item Auxiliary Information in NMF-Based Collaborative Filtering

Alghamedy¹,

Zhang²,

Alghamdi³

2018

Computer Science &Amp; Information Technology (CS &Amp; IT)

Self Cite

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“…There are review websites that allow users to create a list of users whose reviews they suppose are trustworthy which is called a trust list. Social relationship information has been incorporated into both memory-based [6,17,20] and model-based collaborative filtering methods [16,21].…”

Section: Related Workmentioning

confidence: 99%

“…In [21], they proposed a method to impute users in order to improve the ratings prediction. However, the prediction improves only when New-Users are imputed, but not when All-Users are imputed even though the prediction results of cold-start users with some datasets improved.…”

Section: Related Workmentioning

confidence: 99%

“…Enlightened by these papers, we apply the imputation process to Aux-NMF [18] by utilizing the trust network. Our proposed method is different from [14,21] in the missing data selection which we impute, and the known ratings which are used to impute the missing ratings. In addition, we analyze two factors that may affect results of the imputation: (1) the total number of imputed ratings, and (2) the average of imputed ratings value.…”

Section: Related Workmentioning

confidence: 99%

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Enhance NMF-Based Recommendation Systems With Social Information Imputation

Alghamedy¹,

Zhang²

2018

Computer Science &Amp; Information Technology (CS &Amp; IT)

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We propose an NMF (Nonnegative Matrix Factorization)-based approach in collaborative filtering based recommendation systems to improve the Cold-Start-Users predictions since Cold-Start-Users suffer from high error in the results. The proposed method utilizes the trust network information to impute a subset of the missing ratings before NMF is applied. We proposed three strategies to select the subset of missing ratings to impute in order to examine the influence of the imputation with both item groups: Cold-Start-Items and Heavy-Rated-Items; and survey if the trustees' ratings could improve the results more than the other users. We analyze two factors that may affect results of the imputation: (1) the total number of imputed ratings, and (2) the average of imputed rating values. Experiments on four different datasets are conducted to examine the proposed approach. The results show that our approach improves the predicted rating of the cold-start users and alleviates the impact of imputed ratings.

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Imputation Strategies for Cold-Start Users in NMF-Based Recommendation Systems

Alghamedy

Zhang

2019

Proceedings of the 2019 3rd International Conference on Information System and Data Mining

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Imputing trust network information in NMF-based collaborative filtering

Cited by 4 publications

References 22 publications

Imputing Item Auxiliary Information in NMF-Based Collaborative Filtering

Imputing Item Auxiliary Information in NMF-Based Collaborative Filtering

Enhance NMF-Based Recommendation Systems With Social Information Imputation

Imputation Strategies for Cold-Start Users in NMF-Based Recommendation Systems

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