A hybrid recommendation algorithm based on social networks

Jing Gong; MeiLing Gao; Bixiao Xu; Wenjun Wang; Zhixin Sun

11th EAI International Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness

Research Article

A hybrid recommendation algorithm based on social networks

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Cite: BibTeX Plain Text

@INPROCEEDINGS{10.4108/eai.19-8-2015.2260770,
    author={Jing Gong and MeiLing Gao and Bixiao Xu and Wenjun Wang and Zhixin Sun},
    title={A hybrid recommendation algorithm based on social networks},
    proceedings={11th EAI International Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness},
    publisher={IEEE},
    proceedings_a={QSHINE},
    year={2015},
    month={9},
    keywords={social network; recommendation algorithm ; collaborative recommendation},
    doi={10.4108/eai.19-8-2015.2260770}
}

Jing Gong
MeiLing Gao
Bixiao Xu
Wenjun Wang
Zhixin Sun
Year: 2015
A hybrid recommendation algorithm based on social networks
QSHINE
IEEE
DOI: 10.4108/eai.19-8-2015.2260770

Jing Gong¹, MeiLing Gao², Bixiao Xu³, Wenjun Wang², Zhixin Sun²^,*

1: College of mathematics & physics of Nanjing University of Posts and Telecommunications,Key Laboratory of Broadband Wireless Communication and Sensor Network Technology
2: Key Laboratory of Broadband Wireless Communication and Sensor Network Technology
3: College of mathematics & physics of Nanjing University of Posts and Telecommunications

*Contact email: sunzx@njupt.edu.cn

Abstract

In the light of the problem of collaborative recommendation and content-based cold start, this paper proposes a hybrid recommendation system based on social network.The method is based on the user social relations network.According to the social behavior of user, by establishing the model of social network users,it puts forward the user similarity measure .Then it takes random walk algorithm as a basis and selects out N users who have the highest similarity with the users’ interest . The test results show that this method can obtain better recommendation effect and customer satisfaction.

Keywords: social network; recommendation algorithm ; collaborative recommendation

Published: 2015-09-08
Publisher: IEEE

: http://dx.doi.org/10.4108/eai.19-8-2015.2260770