Link prediction approach to collaborative filtering
Proceedings of the 5th ACM/IEEE-CS joint conference on Digital libraries
Matching People and Jobs: A Bilateral Recommendation Approach
HICSS '06 Proceedings of the 39th Annual Hawaii International Conference on System Sciences - Volume 06
Make new friends, but keep the old: recommending people on social networking sites
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Relevance and ranking in online dating systems
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
RECON: a reciprocal recommender for online dating
Proceedings of the fourth ACM conference on Recommender systems
Finding someone you will like and who won't reject you
UMAP'11 Proceedings of the 19th international conference on User modeling, adaption, and personalization
Proceedings of the 2nd International Workshop on Information Heterogeneity and Fusion in Recommender Systems
Machine learned job recommendation
Proceedings of the fifth ACM conference on Recommender systems
CCR: a content-collaborative reciprocal recommender for online dating
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
The effect of suspicious profiles on people recommenders
UMAP'12 Proceedings of the 20th international conference on User Modeling, Adaptation, and Personalization
Online dating recommender systems: the split-complex number approach
Proceedings of the 4th ACM RecSys workshop on Recommender systems and the social web
Multiple objective optimization in recommender systems
Proceedings of the sixth ACM conference on Recommender systems
Social referral: leveraging network connections to deliver recommendations
Proceedings of the sixth ACM conference on Recommender systems
Is it time for a career switch?
Proceedings of the 22nd international conference on World Wide Web
Generating supplemental content information using virtual profiles
Proceedings of the 7th ACM conference on Recommender systems
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While Recommender Systems are powerful drivers of engagement and transactional utility in social networks, People recommenders are a fairly involved and diverse subdomain. Consider that movies are recommended to be watched, news is recommended to be read, people however, are recommended for a plethora of reasons -- such as recommendation of people to befriend, follow, partner, targets for an advertisement or service, recruiting, partnering romantically and to join thematic interest groups. This tutorial aims to first describe the problem domain, touch upon classical approaches like link analysis and collaborative filtering and then take a rapid deep dive into the unique aspects of this problem space like reciprocity, intent understanding of recommender and the recomendee, contextual people recommendations in communication flows and social referrals -- a paradigm for delivery of recommendations using the social graph. These aspects will be discussed in the context of published original work developed by the authors and their collaborators and in many cases deployed in massive-scale real world applications on professional networks such as LinkedIn.