Affiliation:
1. Shaanxi Regional Electric Power Group Co., Ltd., Xi’an 710000, China
2. School of Computer Science, Shaanxi Normal University, Xi’an 710119, China
Abstract
Point-of-interests (POIs) recommendation technology using user’s check-in data has attracted great attentions in recent years. However, user’s check-in data often contains sensitive information such as time and location data. Due to privacy considerations, many users are unwilling to share their check-in data with untrusted service providers, which has a great negative impact on recommendation quality. Trying to solve this problem, geographical and social society attributes based privacy preserving recommendation method for POIs, named GSSA-PPRM, is proposed in the paper. In the proposed method, a local differentially private matrix factorization algorithm is firstly designed to learn user’s preference with social attribute in client/server style. Then, according to the learned preference and considering geographical distance of POIs, a self-adaptive kernel density estimation algorithm is devised to study user’s check-in behavior. And an algorithm that tallies POI visit count and computes POI popularity by securely collecting user’s check-in data through random response (RR) mechanism is presented. Finally, a rating rule is given to predict the ratings of users for POIs by integrating kernel density estimation and POI popularity. The experimental results on two real datasets validate that the proposed method achieves better POI recommendation quality in condition of keeping user’s privacy.
Funder
Key Research and Development Program of Shaanxi Province
Subject
Computer Networks and Communications,Information Systems
Reference36 articles.
1. Learning geographical preferences for point-of-interest recommendation;B. Liu
2. CoRe: Exploiting the personalized influence of two-dimensional geographic coordinates for location recommendations
3. Mobile location prediction in spatio-temporal context;H. Gao;Nokia mobile data challenge workshop,2012
4. gSCorr: modeling geo-social correlations for new check-ins on location-based social networks;H. Gao
5. Collaborative filtering with temporal dynamics;Y. Koren
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献