Collaborator Recommendation on Research Social Network Platforms

科研社交網絡平台中的合作者推薦

Student thesis: Doctoral Thesis

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Author(s)

  • Chen YANG

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Detail(s)

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Supervisors/Advisors
Award date11 Sep 2015

Abstract

Collaborator recommendation is an important activity for scholars given that suitable collaborators could help improve research quality and accelerate the research progress. Extensive developments in scientific social network platforms have resulted in the need for collaborations among researchers through virtual communities. Collaborator recommendation systems can play an important role in these online communities to promote academic collaborations.

Information overload and information asymmetry are the two key issues to be addressed in collaborator recommendation systems. In general, the potential academic collaboration contexts should be defined, and the corresponding solutions are required to provide efficient recommendations. Existing collaborator recommendation studies mainly focus on the similarity between two researchers, which is based on the expertise similarity and social network proximity. Although tremendous effort has been exerted in this area, a general framework for academic collaborator recommendation and effective recommendation mechanisms are still lacking.

In this thesis, a general framework is presented to solve the collaborator recommendation problem. Two main collaborator recommendation contexts are defined, namely, the similarity-based collaborator recommendation and the collaborator recommendation in a specific context. For the similarity-based collaborator recommendation, a hybrid approach is presented to integrate five heterogeneous features from the expertise relevance, social network proximity, and institutional connectivity dimensions. An expertise coverage-oriented collaborator-seeking mechanism is also proposed for the collaborator recommendation in a restrictive context. The traditional latent Dirichlet allocation model is extended to improve its performance for the corpus of documents with different effects.

Two series of experiments are conducted on real-world collaboration datasets. The experimental results have verified and demonstrated the convincing performance of the two proposed approaches in comparison with a list of state-of-the-art benchmark methods. The proposed solutions have been implemented as recommender applications on the ScholarMate platform, one of the largest academic social network communities in China. The proposed methods work effectively and can guide researchers toward better knowledge sharing and collaboration.