Potential Friend Recommendation in Online Social Network

Author

Xie, Xing

Author_Institution

Sch. of Comput. Sci., Fudan Univ., Shanghai, China

fYear

2010

fDate

18-20 Dec. 2010

Firstpage

831

Lastpage

835

Abstract

As the wide popularization of online social networks, online users are not content only with keeping online friendship with social friends in real life any more. They hope the system designers can help them exploring new friends with common interest. However, the large amount of online users and their diverse and dynamic interests possess great challenges to support such a novel feature in online social networks. In this paper, by leveraging interest-based features, we design a general friend recommendation framework, which can characterize user interest in two dimensions: context (location, time) and content, as well as combining domain knowledge to improve recommending quality. We also design a potential friend recommender system in a real online social network of biology field to show the effectiveness of our proposed framework.

Keywords

recommender systems; social networking (online); domain knowledge; general friend recommendation framework; interest-based features; online social network; potential friend recommendation; Context; Mathematical model; Mice; Ontologies; Recommender systems; Social network services; Online social network; friend recommending; link prediction;

fLanguage

English

Publisher

ieee

Conference_Titel

Green Computing and Communications (GreenCom), 2010 IEEE/ACM Int'l Conference on & Int'l Conference on Cyber, Physical and Social Computing (CPSCom)

Conference_Location

Hangzhou

Print_ISBN

978-1-4244-9779-9

Electronic_ISBN

978-0-7695-4331-4

Type

conf

DOI

10.1109/GreenCom-CPSCom.2010.28

Filename

5724926