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Privacy-aware network embedding-based ensemble for social recommendation

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Abstract

Recommender systems play a significant role in helping online users to find the relevant items based on their past preferences. With the sweep of the social network, the social recommendation has emerged that relies on users' social connections to make personalized recommendations of items. A social recommender system leverages users' social connections to address sparsity in rating data and improve recommendation efficiency. Despite their efficacy, the risk of privacy breach is high in the social recommender system as it uses sensitive information for the recommendation. To address this issue, the paper proposes a novel privacy-preserving matrix factorization-based network embedding model that employs an objective perturbation mechanism. The intrinsically nonlinear characteristics of users' social relationships are learned via a Deep Neural Network. The proposed framework uses the differential privacy to secure users' social ties. The recommendation is made by considering the users' personal preferences and social influence generated by the differential private network embedding model. The experimental evaluation of benchmark datasets proves that the proposed method can secure users' privacy without degrading the recommendation accuracy.

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Data availability

The datasets analysed during the current study are available in the LibRec repository. These datasets were derived from the following public domain resources [LibRec: https://guoguibing.github.io/librec/datasets.html; Epinions: http://www.trustlet.org/downloaded_epinions.html].

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This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, PSG College of Technology, Coimbatore, Tamil Nadu, India

    K. Vani & G. Sudha Sadasivam

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  1. K. Vani
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Contributions

KV: Conceptualization, Methodology, Formal analysis, Software, Investigation, Validation, Resources, Writing—original draft, review & editing, Visualization. GSS: Resources, Writing—review & editing, Supervision.

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Correspondence to K. Vani.

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Vani, K., Sadasivam, G.S. Privacy-aware network embedding-based ensemble for social recommendation. J Supercomput 79, 8912–8939 (2023). https://doi.org/10.1007/s11227-022-04958-7

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