DocumentCode :
582844
Title :
Recommendation on social network based on graph model
Author :
Li, Jun ; Ma, Shuchao ; Hong, Shuang
Author_Institution :
Dept. of Autom., Univ. of Sci. & Technol. of China, Hefei, China
fYear :
2012
fDate :
25-27 July 2012
Firstpage :
7548
Lastpage :
7551
Abstract :
Twitter social network site provides a powerful means of sharing, organizing and finding content and contacts. The social network web of twitter forms a large graph; whose vertices are people and edges are relationships of the person. Twitter social networking is a typical of complex networks. Understanding the complex network is important with studying the characteristics of twitter network at large scale. Our interests in twitter focus on its complex network properties, such as scale free effect and small world effect. Here we demonstrate that the twitter social network is a scale free network and a small world network. A good recommender system is important for the social network web. The properties of the twitter network graph provide a theoretical basis for recommendation. In this work, we propose a graph-based recommendation algorithm using the relationship of users and adopt the Random Walk with Restarts to generate the recommendation users and evaluate the performance over precision-recall graph. The results show that recommendation based on the graph model performs well benefits from the relationship.
Keywords :
collaborative filtering; network theory (graphs); performance evaluation; recommender systems; small-world networks; social networking (online); Twitter; complex networks; contacts; content organization; content sharing; graph edges; graph model; graph vertices; graph-based recommendation algorithm; network graph; performance evaluation; precision-recall graph; random walk; recommendation users; recommender system; scale free network; small world network; social network Web; social networking site; Complex networks; Educational institutions; Histograms; Topology; Twitter; graph model; recommendation; scale free network; small world network; social network; twitter;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Control Conference (CCC), 2012 31st Chinese
Conference_Location :
Hefei
ISSN :
1934-1768
Print_ISBN :
978-1-4673-2581-3
Type :
conf
Filename :
6391278
Link To Document :
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