Recommending collaboration with social networks: a comparative evaluation
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Social matching: A framework and research agenda
ACM Transactions on Computer-Human Interaction (TOCHI)
Harvesting with SONAR: the value of aggregating social network information
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Do you know?: recommending people to invite into your social network
Proceedings of the 14th international conference on Intelligent user interfaces
It takes variety to make a world: diversification in recommender systems
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
Make new friends, but keep the old: recommending people on social networking sites
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Increasing engagement through early recommender intervention
Proceedings of the third ACM conference on Recommender systems
Aggregating content and network information to curate twitter user lists
Proceedings of the 4th ACM RecSys workshop on Recommender systems and the social web
Integrating social information into collaborative filtering for celebrities recommendation
ACIIDS'13 Proceedings of the 5th Asian conference on Intelligent Information and Database Systems - Volume Part II
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Social networking sites have begun to be used in the enterprise as a method of connecting employees. Recommender systems may be used to recommend social contacts in order to increase user engagement, encourage collaboration and facilitate expertise discovery. This paper evaluates the effects of four recommendation algorithms on the network as a whole and the social structure. We demonstrate that depending on the basis of the recommendation algorithm the effects on the network vary greatly and their potential impact should be understood. It is hoped this research can be used as guidance for future recommendation algorithms.