The connectivity server: fast access to linkage information on the Web
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
WMR--A Graph-Based Algorithm for Friend Recommendation
WI '06 Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence
Measurement and analysis of online social networks
Proceedings of the 7th ACM SIGCOMM conference on Internet measurement
Friends and foes: ideological social networking
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Characterization of the twitter @replies network: are user ties social or topical?
SMUC '10 Proceedings of the 2nd international workshop on Search and mining user-generated contents
Leveraging personal photos to inferring friendships in social network services
Expert Systems with Applications: An International Journal
Who should I add as a "friend"?: a study of friend recommendations using proximity and homophily
Proceedings of the 4th International Workshop on Modeling Social Media
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In recent years Social Networking has enjoyed a significant increase in popularity. The main reason behind this surge in popularity is the social experience associated with connecting content to people and also connecting people with other people. Knowing, seeing, hearing what our friends and like-minded people feel or listen to or upload is an unparalleled experience. Similar to real life, finding good friends is not easy without the help of good recommendations. In this Industry Talk paper we present the MySpace friend recommendation algorithm named People You May Know. We will also comment on both the quality and the effectiveness of the algorithms.