Document Type
Article
Publication Date
4-6-2022
Abstract
Point-of-interest (POI) recommender systems (RSes) have gained significant popularity in recent years due to the prosperity of location-based social networks (LBSN). However, in the interest of personalization services, various sensitive contextual information is collected, causing potential privacy concerns. This paper proposes a cascaded privacy-preserving POI recommendation (CRS) framework that protects contextual information such as user comments and locations. We demonstrate a minimized trade-off between the privacy-preserving feature and prediction accuracy by applying a semi-decentralized model to real-world datasets.
Version
The Version of Record (VoR) of this Author Manuscript has been published and can be accessed using the DOI below.
Copyright Statement
Electronics is an international, peer-reviewed, open access journal on the science of electronics and its applications published semimonthly online by MDPI.
DOI
https://doi.org/10.3390/electronics11071153
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Publication Title
Electronics
Volume Number
11
Issue Number
7
First Page
1
Recommended Citation
Cui, Longyin and Wang, Xiwei, "A Cascade Framework for Privacy-Preserving Point-of-Interest Recommender System" (2022). Computer Science Faculty Publications. 41.
https://neiudc.neiu.edu/comp-pub/41
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