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Article

A Technique of Recursive Reliability-Based Missing Data Imputation for Collaborative Filtering

by
Sun-Young Ihm
1,
Shin-Eun Lee
2,
Young-Ho Park
2,
Aziz Nasridinov
3,
Miyeon Kim
4 and
So-Hyun Park
5,*
1
Department of Computer Engineering, PaiChai University, 155-40 Baejae-ro, Seo-Gu, Daejeon 35345, Korea
2
Department of IT Engineering, Sookmyung Women’s University, Cheongpa-ro 47-gil 100, Yongsan-gu, Seoul 04310, Korea
3
Department of Computer Science, Chungbuk National University, Chungdaero-1, Seowon-gu, Cheongju, Chungbuk 28644, Korea
4
Department of Airline Services & Tourism, Seoyeong University, Seogang-ro 1, Buk-gu, Gwangju 61268, Korea
5
Bigdata Using Research Center, Sookmyung Women’s University, Cheongpa-ro 47-gil 100, Yongsan-gu, Seoul 04310, Korea
*
Author to whom correspondence should be addressed.
Appl. Sci. 2021, 11(8), 3719; https://doi.org/10.3390/app11083719
Submission received: 15 February 2021 / Revised: 12 April 2021 / Accepted: 16 April 2021 / Published: 20 April 2021
(This article belongs to the Collection The Development and Application of Fuzzy Logic)
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Abstract

Collaborative filtering (CF) is a recommendation technique that analyzes the behavior of various users and recommends the items preferred by users with similar preferences. However, CF methods suffer from poor recommendation accuracy when the user preference data used in the recommendation process is sparse. Data imputation can alleviate the data sparsity problem by substituting a virtual part of the missing user preferences. In this paper, we propose a k-recursive reliability-based imputation (k-RRI) that first selects data with high reliability and then recursively imputes data with additional selection while gradually lowering the reliability criterion. We also propose a new similarity measure that weights common interests and indifferences between users and items. The proposed method can overcome disregarding the importance of missing data and resolve the problem of poor data imputation of existing methods. The experimental results demonstrate that the proposed approach significantly improves recommendation accuracy compared to those resulting from the state-of-the-art methods while demanding less computational complexity.
Keywords: artificial intelligence; collaborative filtering; data sparsity; missing data imputation; recommendation systems; recursive algorithm; reliability artificial intelligence; collaborative filtering; data sparsity; missing data imputation; recommendation systems; recursive algorithm; reliability

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MDPI and ACS Style

Ihm, S.-Y.; Lee, S.-E.; Park, Y.-H.; Nasridinov, A.; Kim, M.; Park, S.-H. A Technique of Recursive Reliability-Based Missing Data Imputation for Collaborative Filtering. Appl. Sci. 2021, 11, 3719. https://doi.org/10.3390/app11083719

AMA Style

Ihm S-Y, Lee S-E, Park Y-H, Nasridinov A, Kim M, Park S-H. A Technique of Recursive Reliability-Based Missing Data Imputation for Collaborative Filtering. Applied Sciences. 2021; 11(8):3719. https://doi.org/10.3390/app11083719

Chicago/Turabian Style

Ihm, Sun-Young, Shin-Eun Lee, Young-Ho Park, Aziz Nasridinov, Miyeon Kim, and So-Hyun Park. 2021. "A Technique of Recursive Reliability-Based Missing Data Imputation for Collaborative Filtering" Applied Sciences 11, no. 8: 3719. https://doi.org/10.3390/app11083719

APA Style

Ihm, S.-Y., Lee, S.-E., Park, Y.-H., Nasridinov, A., Kim, M., & Park, S.-H. (2021). A Technique of Recursive Reliability-Based Missing Data Imputation for Collaborative Filtering. Applied Sciences, 11(8), 3719. https://doi.org/10.3390/app11083719

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