PRUB: A Privacy Protection Friend Recommendation System Based on User Behavior

The fast developing social network is a double-edged sword. It remains a serious problem to provide users with excellent mobile social network services as well as protecting privacy data. Most popular social applications utilize behavior of users to build connection with people having similar behavior, thus improving user experience. However, many users do not want to share their certain behavioral information to the recommendation system. In this paper, we aim to design a secure friend recommendation system based on the user behavior, called PRUB. The system proposed aims at achieving fine-grained recommendation to friends who share some same characteristics without exposing the actual user behavior. We utilized the anonymous data from a Chinese ISP, which records the user browsing behavior, for 3 months to test our system. The experiment result shows that our system can achieve a remarkable recommendation goal and, at the same time, protect the privacy of the user behavior information.

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Analyzing on User Behavior and User Experience of Social Network Services

Simulation Tools and Techniques - Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering ◽

10.1007/978-3-030-32216-8_17 ◽

2019 ◽

pp. 180-185

Author(s):

Rong Bao ◽

Lei Chen ◽

Ping Cui

Keyword(s):

Social Network ◽

User Experience ◽

User Behavior ◽

Network Services ◽

Social Network Services

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Factors Affecting Loyalty of Mobile Social Networks' Users

International Journal of E-Business Research ◽

10.4018/ijebr.2017010104 ◽

2017 ◽

Vol 13 (1) ◽

pp. 66-81 ◽

Cited By ~ 4

Author(s):

Nastaran Hajiheydari ◽

Babak Hazaveh Hesar Maskan ◽

Mahdi Ashkani

Keyword(s):

Social Networks ◽

Social Network ◽

Structural Equation ◽

Online Survey ◽

Equation Modeling ◽

Mobile Social Networks ◽

Mobile Social Network ◽

Key Factors ◽

Factors Affecting ◽

Social Applications

Increasing world-wide trends of using mobile social networks and the rise of competition between different social applications makes it essential for social network providers and marketers to identify the key factors leading to user loyalty. The purpose of this paper is to identify the key factors that affect the loyalty of mobile social networks users. The proposed model was tested through structural equation modeling techniques and an online survey. The sample consisted of 388 mobile social networks users in Iran. The results indicate that sociability, entertainment and fashion are primary drivers of attitude toward a mobile social network. The results also show the significant role of attitude and satisfaction on consumer loyalty. This study helps both marketers and mobile social network providers know the key drivers of customer loyalty in order to tailor their marketing efforts and communication strategies.

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Trust Evaluation Based Friend Recommendation in Proximity Based Mobile Social Network

Computational Social Networks - Lecture Notes in Computer Science ◽

10.1007/978-3-319-42345-6_14 ◽

2016 ◽

pp. 158-169

Author(s):

Fizza Abbas ◽

Ubaidullah Rajput ◽

Hasoo Eun ◽

Dongsoo Ha ◽

Taeseon Moon ◽

...

Keyword(s):

Social Network ◽

Mobile Social Network ◽

Trust Evaluation ◽

Friend Recommendation

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A Prediction Method of Mobile User Preference Based on the Influence between Users

International Journal of Digital Multimedia Broadcasting ◽

10.1155/2018/8081409 ◽

2018 ◽

Vol 2018 ◽

pp. 1-12 ◽

Cited By ~ 1

Author(s):

Yancui Shi ◽

Jianhua Cao ◽

Congcong Xiong ◽

Xiankun Zhang

Keyword(s):

Social Network ◽

User Behavior ◽

Prediction Method ◽

Mobile User ◽

User Preferences ◽

User Preference ◽

Context Information ◽

Mobile Social Network ◽

Data Set ◽

Interaction Behavior

User preference will be impacted by other users. To accurately predict mobile user preference, the influence between users is introduced into the prediction model of user preference. First, the mobile social network is constructed according to the interaction behavior of the mobile user, and the influence of the user is calculated according to the topology of the constructed mobile social network and mobile user behavior. Second, the influence between users is calculated according to the user’s influence, the interaction behavior between users, and the similarity of user preferences. When calculating the influence based on the interaction behavior, the context information is considered; the context information and the order of user preferences are considered when calculating the influence based on the similarity of user preferences. The improved collaborative filtering method is then employed to predict mobile user preferences based on the obtained influence between users. Finally, the experiment is executed on the real data set and the integrated data set, and the results show that the proposed method can obtain more accurate mobile user preferences than those of existing methods.

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