Advanced Academic Team Worker Recommendation Models

Wu, Mi

arXiv.org Artificial Intelligence 

Collaborator recommendation is an important task in academic domain. Most of the existing approaches have the assumption that the recommendation system only need to recommend a specific researcher for the task. However, academic successes can be owed to productive collaboration of a whole academic team. In this work, we propose a new task: academic team worker recommendation: with a given status: student, assistant professor or prime professor, research interests and specific task, we can recommend an academic team formed as (prime professor, assistant professor, student). For this task, we propose a model CQBG-R(Citation-Query Blended Graph-Ranking). The key ideas is to combine the context of the query and the papers with the graph topology to form a new graph(CQBG), which can target at the research interests and the specific research task for this time. The experiment results show the effectiveness of the proposed method.

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