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In order to accomplish complex tasks, it is often necessary to compose a team consisting of experts with diverse competencies. However, for proper functioning, it is also preferable that a team be socially cohesive. A team recommendation system, which facilitates the search for potential team members can be of great help both for (i) individuals who need to seek out collaborators and (ii) managers who need to build a team for some specific tasks. A decision support system which readily helps summarize such metrics, and possibly rank the teams in a personalized manner according to the end users preferences, can be a great tool to navigate what would otherwise be an information avalanche. In this work we present a general framework of how to compose such subsystems together to build a composite team recommendation system, and instantiate it for a case study of academic teams.
The Heterogeneous Information Network (HIN) formalism is very flexible and enables complex recommendations models. We evaluate the effect of different parts of a HIN on the accuracy and the diversity of recommendations, then investigate if these effe
GitHub has become a popular social application platform, where a large number of users post their open source projects. In particular, an increasing number of researchers release repositories of source code related to their research papers in order t
As a result of the importance of academic collaboration at smart conferences, various researchers have utilized recommender systems to generate effective recommendations for participants. Recent research has shown that the personality traits of users
This research addresses recommending presentation sessions at smart conferences to participants. We propose a venue recommendation algorithm, Socially-Aware Recommendation of Venues and Environments (SARVE). SARVE computes correlation and social char
Recommending products to consumers means not only understanding their tastes, but also understanding their level of experience. For example, it would be a mistake to recommend the iconic film Seven Samurai simply because a user enjoys other action mo