Privacy-Preserving Collaborative Filtering Based on Time-Drifting Characteristic

ZHAO Feng; XIONG Yan; LIANG Xiao; GONG Xudong; LU Qiwei

doi:10.1049/cje.2016.01.004

Volume 25 Issue 1

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ZHAO Feng, XIONG Yan, LIANG Xiao, et al., “Privacy-Preserving Collaborative Filtering Based on Time-Drifting Characteristic,” Chinese Journal of Electronics, vol. 25, no. 1, pp. 20-25, 2016, doi: 10.1049/cje.2016.01.004

Citation:

ZHAO Feng, XIONG Yan, LIANG Xiao, et al., “Privacy-Preserving Collaborative Filtering Based on Time-Drifting Characteristic,” Chinese Journal of Electronics, vol. 25, no. 1, pp. 20-25, 2016, doi: 10.1049/cje.2016.01.004

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Privacy-Preserving Collaborative Filtering Based on Time-Drifting Characteristic

doi: 10.1049/cje.2016.01.004

1.
School of Computer Science and Technology, University of Science and Technology of China, Hefei 230027, China;
2.
Dispatching and Control Center, Anhui Electric Power Corporation, Hefei 230022, China

Funds: The research is supported by National Natural Science Foundation of China (No.61202404, No.61170233, No.61232018, No.61272472, No.61272317, No.61300170), the Fundamental Research Funds for the Central Universities (No.WK0110000036) and University Provincial Natural Science Foundation of Anhui Province (No.KJ2013A040).

Received Date: 2014-01-15
Rev Recd Date: 2014-03-05
Publish Date: 2016-01-10

Abstract

Abstract

Recommendation has become increasingly important because of the information overload. Collaborative filtering (CF) technique, as the most popular recommendation method, utilizes the historical preferences of users to predict their future interests on other items. However, CF technique requires collecting users' rating information, which may lead to the disclosure of privacy. We propose a new randomized perturbation approach Time-drifting privacy-preserving collaborative filtering (TPPCF) to well balance privacy of users and accuracy of recommendation. Since users' recent ratings can better represent their interests and preferences, we incorporate a varying weight into the approach. Specifically, we assign higher weights to more recent ratings both when computing user similarity and perturbing users' ratings. To further improve the efficiency, we cluster the users into several groups to reduce computation cost. We demonstrate the effectiveness and efficiency of our method through experiments on MovieLens dataset, which shows TPPCF can achieve higher privacy while generating more accurate recommendation.
- Collaborative filtering (CF),
- Randomization,
- Privacy,
- Varying Weight,
- Time-drifting characteristic

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References(17)

References

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