Acquisition method of users’ browsing behavior preference based on the fusion of social network link and theme model

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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Terrorism and Crime Related Weblog Social Network: Link, Content Analysis and Information Visualization

2007 IEEE Intelligence and Security Informatics ◽

10.1109/isi.2007.379533 ◽

2007 ◽

Cited By ~ 21

Author(s):

Christopher C. Yang ◽

Tobun D. Ng

Keyword(s):

Content Analysis ◽

Social Network ◽

Information Visualization ◽

Network Link

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The Algorithm Study of Support Vector Machine Based on Social Network of Public Opinions

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.513-517.2394 ◽

2014 ◽

Vol 513-517 ◽

pp. 2394-2397

Author(s):

Hong Biao Xie ◽

Hong Jun Qiu

Keyword(s):

Public Opinion ◽

Social Network ◽

Text Classification ◽

Social Groups ◽

Classification Problem ◽

Support Vector ◽

Social Stability ◽

Social Phenomena ◽

Svm Algorithm ◽

Network Link

Public opinion refers to the certain social groups subjective reflection of certain social phenomena and reality within a period of time. The important measures to maintain social stability and the ruling party's ruling safety are to instantly master the dynamic public opinion and to actively guide social public opinion. In this paper, the author found the model of social network public opinion hotspot issues. The SVM algorithm is adopted to improve the information processing and analysis testing, effectively resolving the text classification problem. It verifies that this method plays an important role in the hot issues analyses of the network link.

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Dynamical Modeling, Analysis, and Control of Information Diffusion over Social Networks: A Deep Learning-Based Recommendation Algorithm in Social Network

Discrete Dynamics in Nature and Society ◽

10.1155/2020/3273451 ◽

2020 ◽

Vol 2020 ◽

pp. 1-8

Author(s):

Kefei Cheng ◽

Xiaoyong Guo ◽

Xiaotong Cui ◽

Fengchi Shan

Keyword(s):

Social Networks ◽

Deep Learning ◽

Social Network ◽

Information Diffusion ◽

Recommendation Algorithm ◽

Central Sequence ◽

Modeling Analysis ◽

News Recommendation ◽

Browsing Behavior ◽

And Control

The recommendation algorithm can break the restriction of the topological structure of social networks, enhance the communication power of information (positive or negative) on social networks, and guide the information transmission way of the news in social networks to a certain extent. In order to solve the problem of data sparsity in news recommendation for social networks, this paper proposes a deep learning-based recommendation algorithm in social network (DLRASN). First, the algorithm is used to process behavioral data in a serializable way when users in the same social network browse information. Then, global variables are introduced to optimize the encoding way of the central sequence of Skip-gram, in which way online users’ browsing behavior habits can be learned. Finally, the information that the target users’ have interests in can be calculated by the similarity formula and the information is recommended in social networks. Experimental results show that the proposed algorithm can improve the recommendation accuracy.

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