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Article Details

Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

Incorporating Auxiliary Information in Collaborative Filtering Data Update with Privacy Preservation

Author 1: Xiwei Wang
Author 2: Jun Zhang
Author 3: Pengpeng Lin
Author 4: Nirmal Thapa
Author 5: Yin Wang
Author 6: Jie Wang

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Digital Object Identifier (DOI) : 10.14569/IJACSA.2014.050432

Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 5 Issue 4, 2014.

  • Abstract and Keywords
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Abstract: Online shopping has become increasingly popular in recent years. More and more people are willing to buy products through Internet instead of physical stores. For promotional purposes, almost all online merchants provide product recommendations to their returning customers. Some of them ask professional recommendation service providers to help develop and maintain recommender systems while others need to share their data with similar shops for better product recommendations. There are two issues, (1) how to protect customers’ privacy while retaining data utility before they release the data to the third parties; (2) based on (1), how to handle data growth efficiently. In this paper, we propose a NMF (Nonnegative Matrix Factorization)-based data update approach in collaborative filtering (CF) that solves the problems. The proposed approach utilizes the intrinsic property of NMF to distort the data for protecting user’s privacy. In addition, the user and item auxiliary information is taken into account in incremental nonnegative matrix tri-factorization to help improve the data utility. Experiments on three different datasets (MovieLens, Sushi and LibimSeTi) are conducted to examine the proposed approach. The results show that our approach can quickly update the new data and provide both high level privacy protection and good data utility.

Keywords: auxiliary information; collaborative filtering; data growth; nonnegative matrix factorization; privacy

Xiwei Wang , Jun Zhang, Pengpeng Lin, Nirmal Thapa, Yin Wang and Jie Wang, “Incorporating Auxiliary Information in Collaborative Filtering Data Update with Privacy Preservation” International Journal of Advanced Computer Science and Applications(IJACSA), 5(4), 2014. http://dx.doi.org/10.14569/IJACSA.2014.050432

@article{2014,
title = {Incorporating Auxiliary Information in Collaborative Filtering Data Update with Privacy Preservation},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2014.050432},
url = {http://dx.doi.org/10.14569/IJACSA.2014.050432},
year = {2014},
publisher = {The Science and Information Organization},
volume = {5},
number = {4},
author = {Xiwei Wang and Jun Zhang and Pengpeng Lin and Nirmal Thapa and Yin Wang and Jie Wang}
}


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