Term-weighting approaches in automatic text retrieval
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Content-based book recommending using learning for text categorization
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Explaining collaborative filtering recommendations
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Recommending collaboration with social networks: a comparative evaluation
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FeedMe: a collaborative alert filtering system
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Searching for experts in the enterprise: combining text and social network analysis
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Recommendations in taste related domains: collaborative filtering vs. social filtering
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Harvesting with SONAR: the value of aggregating social network information
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Recommending topics for self-descriptions in online user profiles
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Public vs. private: comparing public social network information with email
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Motivations for social networking at work
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Syskill & webert: Identifying interesting web sites
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Bowling online: social networking and social capital within the organization
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Increasing engagement through early recommender intervention
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Same places, same things, same people?: mining user similarity on social media
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Short and tweet: experiments on recommending content from information streams
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Lessons learned from blog muse: audience-based inspiration for bloggers
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Emerging topic detection on Twitter based on temporal and social terms evaluation
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Recommending twitter users to follow using content and collaborative filtering approaches
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RECON: a reciprocal recommender for online dating
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Common attributes in an unusual context: predicting the desirability of a social match
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This paper studies people recommendations designed to help users find known, offline contacts and discover new friends on social networking sites. We evaluated four recommender 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' 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.