Improved Recommender for Location Privacy Preferences

• DOI: 10.5539/cis.v8n4p64
• Publication Date: 2015-11-11T04:58:36Z
• Authors: Anas A. Hadi; Jonathan Cazalas
• Keywords: Popularity

Location-based services are one of the fastest growing technologies. Millions users using these and sharing their locations smart devices. The popularity such applications, while enabling others to access user’s location, brings with it many privacy issues. user has ability set his location preferences manually. Many face difficulties in order proper way. One solution is use machine learning based methods predict automatically. These models suffer from degraded performance when there no sufficient training data. Another make decision for intended user, depending on collected opinions similar users. <em>User-User Collaborative Filtering (CF)</em> an example within this category. In paper, we will introduce improved predictor. results show significant improvements performance. accuracy was 75.30% up 84.82%, leak reduced 11.75% 7.65%. We also introduced integrated model which combines both collaborative filtering get advantages them.