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<XML><RECORDS>
<RECORD>
	<REFERENCE_TYPE>3</REFERENCE_TYPE>
	<AUTHORS>
		<AUTHOR>Chen, J.</AUTHOR>
		<AUTHOR>Geyer, W.</AUTHOR>
		<AUTHOR>Dugan, C.</AUTHOR>
		<AUTHOR>Muller, M.</AUTHOR>
		<AUTHOR>Guy, I.</AUTHOR>
	</AUTHORS>
	<YEAR>2009</YEAR>
	<TITLE>Making New Friends, but Keep the Old - Recommending People on Social Networking Sites (forthcoming)</TITLE>
	<SECONDARY_TITLE>ACM SIGCHI Conference on Human Factors in Computing Systems</SECONDARY_TITLE>
	<PUBLISHER> </PUBLISHER>
	<ABSTRACT>&lt;p&gt;This paper studies people recommendations designed to help users find known, offline contacts and discover new friends on social networking sites. We evaluated four&Acirc;&nbsp; recommender&Acirc;&nbsp; algorithms in an enterprise social networking site using a personalized survey of 500 users and a field study of 3,000 users. We found all algorithms effective in expanding users&acirc;€™ friend lists. Algorithms based on social network information were able to produce better-received recommendations and find more known contacts for users, while algorithms using similarity of user-created content were stronger in discovering new friends. We also collected qualitative feedback from our survey users and draw several meaningful design implications.&lt;/p&gt;</ABSTRACT>
</RECORD>
</RECORDS></XML>
