Context-aware Academic Collaborator Recommendation.

Collaborator Recommendation is a useful application in exploiting big academic data. However, existing works leave out the contextual restriction (i.e., research topics) of people's academic collaboration, thus cannot recommend suitable collaborators for the required research topics. In this work, we propose Context-aware Collaborator Recommendation (CACR), which aims to recommend high-potential new collaborators for people's context-restricted requests. To this end, we design a novel recommendation framework, which consists of two fundamental components: the Collaborative Entity Embedding network (CEE) and the Hierarchical Factorization Model (HFM). In particular, CEE jointly represents researchers and research topics as compact vectors based on their co-occurrence relationships, whereby capturing researchers' context-aware collaboration tendencies and topics' underlying semantics. Meanwhile, HFM extracts researchers' activenesses and conservativenesses, which reflect their intensities of making academic collaborations and tendencies of working with non-collaborated fellows. The extracted activenesses and conservativenesses work collaboratively with the context-aware collaboration tendencies, such that high-quality recommendation can be produced. Extensive experimental studies are conducted with large-scale academic data, whose results verify the effectiveness of our proposed approaches.