The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Pengguna perkhidmatan web terharu dengan jumlah maklumat yang disampaikan kepada mereka dan menghadapi kesukaran untuk mencari maklumat yang mereka perlukan. Oleh itu, sistem pengesyoran yang meramalkan citarasa pengguna merupakan faktor penting untuk kejayaan perniagaan. Walau bagaimanapun, sistem pengesyoran memerlukan maklumat peribadi pengguna dan dengan itu boleh membawa kepada pelanggaran privasi yang serius. Untuk menyelesaikan masalah ini, banyak penyelidikan telah dijalankan tentang melindungi maklumat peribadi dalam sistem pengesyoran dan melaksanakan privasi pembezaan, teknik perlindungan privasi yang memasukkan bunyi ke dalam data asal. Walau bagaimanapun, kajian terdahulu tidak mengkaji faktor berikut dalam menggunakan privasi pembezaan kepada sistem pengesyoran. Pertama, mereka tidak menganggap keterbatasan maklumat penilaian pengguna. Jumlah item adalah lebih banyak daripada bilangan item yang dinilai pengguna. Oleh itu, matriks penilaian yang dibuat untuk pengguna dan item akan menjadi sangat jarang. Ciri ini menjadikan pengecaman corak pengguna dalam matriks penilaian sukar. Oleh itu, isu sparsity harus dipertimbangkan dalam penggunaan privasi pembezaan kepada sistem pengesyoran. Kedua, kajian terdahulu memberi tumpuan kepada melindungi maklumat penilaian pengguna tetapi tidak bertujuan untuk melindungi senarai item yang dinilai pengguna. Sistem pengesyoran harus melindungi senarai item ini kerana mereka juga mendedahkan pilihan pengguna. Dalam kajian ini, kami mencadangkan skim pengesyoran berbeza peribadi yang berdasarkan kaedah pengumpulan untuk menyelesaikan isu sparsity dan untuk melindungi senarai item dinilai pengguna dan maklumat penilaian pengguna. Teknik yang dicadangkan menunjukkan prestasi yang lebih baik dan perlindungan privasi pada data penilaian filem sebenar berbanding dengan teknik sedia ada.
Taewhan KIM
Sogang University
Kangsoo JUNG
Sogang University
Seog PARK
Sogang University
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Salinan
Taewhan KIM, Kangsoo JUNG, Seog PARK, "Sparsity Reduction Technique Using Grouping Method for Matrix Factorization in Differentially Private Recommendation Systems" in IEICE TRANSACTIONS on Information,
vol. E103-D, no. 7, pp. 1683-1692, July 2020, doi: 10.1587/transinf.2019EDP7238.
Abstract: Web service users are overwhelmed by the amount of information presented to them and have difficulties in finding the information that they need. Therefore, a recommendation system that predicts users' taste is an essential factor for the success of businesses. However, recommendation systems require users' personal information and can thus lead to serious privacy violations. To solve this problem, many research has been conducted about protecting personal information in recommendation systems and implementing differential privacy, a privacy protection technique that inserts noise into the original data. However, previous studies did not examine the following factors in applying differential privacy to recommendation systems. First, they did not consider the sparsity of user rating information. The total number of items is much more than the number of user-rated items. Therefore, a rating matrix created for users and items will be very sparse. This characteristic renders the identification of user patterns in rating matrixes difficult. Therefore, the sparsity issue should be considered in the application of differential privacy to recommendation systems. Second, previous studies focused on protecting user rating information but did not aim to protect the lists of user-rated items. Recommendation systems should protect these item lists because they also disclose user preferences. In this study, we propose a differentially private recommendation scheme that bases on a grouping method to solve the sparsity issue and to protect user-rated item lists and user rating information. The proposed technique shows better performance and privacy protection on actual movie rating data in comparison with an existing technique.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2019EDP7238/_p
Salinan
@ARTICLE{e103-d_7_1683,
author={Taewhan KIM, Kangsoo JUNG, Seog PARK, },
journal={IEICE TRANSACTIONS on Information},
title={Sparsity Reduction Technique Using Grouping Method for Matrix Factorization in Differentially Private Recommendation Systems},
year={2020},
volume={E103-D},
number={7},
pages={1683-1692},
abstract={Web service users are overwhelmed by the amount of information presented to them and have difficulties in finding the information that they need. Therefore, a recommendation system that predicts users' taste is an essential factor for the success of businesses. However, recommendation systems require users' personal information and can thus lead to serious privacy violations. To solve this problem, many research has been conducted about protecting personal information in recommendation systems and implementing differential privacy, a privacy protection technique that inserts noise into the original data. However, previous studies did not examine the following factors in applying differential privacy to recommendation systems. First, they did not consider the sparsity of user rating information. The total number of items is much more than the number of user-rated items. Therefore, a rating matrix created for users and items will be very sparse. This characteristic renders the identification of user patterns in rating matrixes difficult. Therefore, the sparsity issue should be considered in the application of differential privacy to recommendation systems. Second, previous studies focused on protecting user rating information but did not aim to protect the lists of user-rated items. Recommendation systems should protect these item lists because they also disclose user preferences. In this study, we propose a differentially private recommendation scheme that bases on a grouping method to solve the sparsity issue and to protect user-rated item lists and user rating information. The proposed technique shows better performance and privacy protection on actual movie rating data in comparison with an existing technique.},
keywords={},
doi={10.1587/transinf.2019EDP7238},
ISSN={1745-1361},
month={July},}
Salinan
TY - JOUR
TI - Sparsity Reduction Technique Using Grouping Method for Matrix Factorization in Differentially Private Recommendation Systems
T2 - IEICE TRANSACTIONS on Information
SP - 1683
EP - 1692
AU - Taewhan KIM
AU - Kangsoo JUNG
AU - Seog PARK
PY - 2020
DO - 10.1587/transinf.2019EDP7238
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E103-D
IS - 7
JA - IEICE TRANSACTIONS on Information
Y1 - July 2020
AB - Web service users are overwhelmed by the amount of information presented to them and have difficulties in finding the information that they need. Therefore, a recommendation system that predicts users' taste is an essential factor for the success of businesses. However, recommendation systems require users' personal information and can thus lead to serious privacy violations. To solve this problem, many research has been conducted about protecting personal information in recommendation systems and implementing differential privacy, a privacy protection technique that inserts noise into the original data. However, previous studies did not examine the following factors in applying differential privacy to recommendation systems. First, they did not consider the sparsity of user rating information. The total number of items is much more than the number of user-rated items. Therefore, a rating matrix created for users and items will be very sparse. This characteristic renders the identification of user patterns in rating matrixes difficult. Therefore, the sparsity issue should be considered in the application of differential privacy to recommendation systems. Second, previous studies focused on protecting user rating information but did not aim to protect the lists of user-rated items. Recommendation systems should protect these item lists because they also disclose user preferences. In this study, we propose a differentially private recommendation scheme that bases on a grouping method to solve the sparsity issue and to protect user-rated item lists and user rating information. The proposed technique shows better performance and privacy protection on actual movie rating data in comparison with an existing technique.
ER -