Research On Academic Collaborator Recommendation Based On Network Embedding

In the face of comprehensive,diversified and intertwined complex scientific research,academic collaborations among scholars enable scholars in different fields to learn new knowledge,broaden new horizons,and realize the efficient use of scientific research resources,accelerating the flow of scientific research information,improving the quality of scientific research,which has profound significance for solving technical problems and theoretical innovations.The collaborator recommendation is for scholars to recommend scholars in suitable research fields for relevant academic collaborations.However,many studies only obtain the scholar's text feature representation through text mining technology or through the cooperative relationship network to obtain the scholar's structural feature representation and through simple integration of text feature representation and structural feature representation to obtain the scholar's feature representation,however,the influence of text information and network structure on the feature representation of scholars is ignored.Network representation learning aims to represent nodes in the network as low-dimensional vectors,while the academic cooperative network contains a lot of text information and network structure information.This heterogeneous information can refine the network structure,which is beneficial to the feature representation of scholars.Whether keyword retrieval or expert archives based on the topic model,there will be a semantic gap between different text representations,and the simple fusion of scholars' text representation and network structure representation can not accurately capture the potential relationship between scholars.In this dissertation,the text information of scholars is fused with the network structure,and the feature representation of scholars is mined by using the network representation learning technology to help scholars recommend proper collaborators.The main work of this dissertation is as follows:There are many potential partnerships in the partnership network,this dissertation proposes a method that Topic-aware Academic Collaborator Recommendation based on Attributed Network Representation Learning,which is called TACR-ANRL for short.By building a heterogeneous information network composed of papers,scholars,and topics to capture the potential collaboration relationships of scholars in the collaboration relationship network as much as possible,and using attribute network representation learning to map scholars' text information and network structure to the same vector space to capture potential cooperative relationships between scholars,we obtain an accurate feature representation of scholars.Finally,based on the scholar's feature representation,the similarity between the scholars is calculated to recommend top-k similar collaborators.Experiments on the data set show that this method can effectively use the topic to capture the potential collaborator relationships generated by the fusion of scholar text information and network structure in the collaborator relationship network and can achieve good performance compared with other collaborators' recommendation methods.The nodes in the collaborator relationship network have rich attributes,and the semantic relationship composed of the attributes can be used to enhance the network structure of the collaborator relationship.This dissertation proposes a method that Content-enhanced Network Embedding for Academic Collaborator Recommendation,which is called CNEacR for short.This method mainly uses the scholar's text information to obtain the scholar's weighted text representation and then constructs a content-enhanced collaborator network that contains both the original collaborator relationship and the content-enhanced semantic relationships,and the scholar's feature representation is obtained through the network representation learning.Finally,calculating the similarity based on the feature representation of scholars between scholars recommends top-k similar collaborators for each scholar.Experiments on the data set show that this method can effectively capture the potential collaborator relationships generated by the fusion of scholar text information and network structure in the collaborator relationship network and can achieve good performance compared with other collaborator recommendation methods.