A Cascade Framework for Privacy-Preserving Point-of-Interest Recommender System
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...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-04-01
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| Series: | Electronics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-9292/11/7/1153 |
| _version_ | 1797439709914333184 |
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| author | Longyin Cui Xiwei Wang |
| author_facet | Longyin Cui Xiwei Wang |
| author_sort | Longyin Cui |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T11:57:01Z |
| format | Article |
| id | doaj.art-e2cebb234eef43859c004742f88593f8 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-09T11:57:01Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-e2cebb234eef43859c004742f88593f82023-11-30T23:08:10ZengMDPI AGElectronics2079-92922022-04-01117115310.3390/electronics11071153A Cascade Framework for Privacy-Preserving Point-of-Interest Recommender SystemLongyin Cui0Xiwei Wang1Department of Computer Science, College of Engineering, University of Kentucky, Lexington, KY 40508, USADepartment of Computer Science, Northeastern Illinois University, Chicago, IL 60625, USAPoint-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.https://www.mdpi.com/2079-9292/11/7/1153recommender systemprivacy preservingPOI recommendationcollaborative filteringclustering |
| spellingShingle | Longyin Cui Xiwei Wang A Cascade Framework for Privacy-Preserving Point-of-Interest Recommender System Electronics recommender system privacy preserving POI recommendation collaborative filtering clustering |
| title | A Cascade Framework for Privacy-Preserving Point-of-Interest Recommender System |
| title_full | A Cascade Framework for Privacy-Preserving Point-of-Interest Recommender System |
| title_fullStr | A Cascade Framework for Privacy-Preserving Point-of-Interest Recommender System |
| title_full_unstemmed | A Cascade Framework for Privacy-Preserving Point-of-Interest Recommender System |
| title_short | A Cascade Framework for Privacy-Preserving Point-of-Interest Recommender System |
| title_sort | cascade framework for privacy preserving point of interest recommender system |
| topic | recommender system privacy preserving POI recommendation collaborative filtering clustering |
| url | https://www.mdpi.com/2079-9292/11/7/1153 |
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