Linked
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Improving recommendation lists through topic diversification
WWW '05 Proceedings of the 14th international conference on World Wide Web
The Detection and Classification of Non-Functional Requirements with Application to Early Aspects
RE '06 Proceedings of the 14th IEEE International Requirements Engineering Conference
IEEE Transactions on Software Engineering
Evaluating Performance of Recommender Systems: An Experimental Comparison
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Do you know?: recommending people to invite into your social network
Proceedings of the 14th international conference on Intelligent user interfaces
FriendSensing: recommending friends using mobile phones
Proceedings of the third ACM conference on Recommender systems
Modern Information Retrieval
Tutorial on evaluating recommender systems
Proceedings of the fourth ACM conference on Recommender systems
Transitive node similarity for link prediction in social networks with positive and negative links
Proceedings of the fourth ACM conference on Recommender systems
Social networking feeds: recommending items of interest
Proceedings of the fourth ACM conference on Recommender systems
Collaboration recommendation on academic social networks
ER'10 Proceedings of the 2010 international conference on Advances in conceptual modeling: applications and challenges
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
Human mobility, social ties, and link prediction
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Friendship prediction and homophily in social media
ACM Transactions on the Web (TWEB)
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Social network analysis (SNA) has been explored in many contexts with different goals. Here, we use concepts from SNA for recommending collaborations in academic networks. Recent work shows that research groups with well connected academic networks tend to be more prolific. Hence, recommending collaborations is useful for increasing a group's connections, then boosting the group research as a collateral advantage. In this work, we propose two new metrics for recommending new collaborations or intensification of existing ones. Each metric considers a social principle (homophily and proximity) that is relevant within the academic context. The focus is to verify how these metrics influence in the resulting recommendations. We also propose new metrics for evaluating the recommendations based on social concepts (novelty, diversity and coverage) that have never been used for such a goal. Our experimental evaluation shows that considering our new metrics improves the quality of the recommendations when compared to the state-of-the-art.