The project review information plays an important role in the recommendation of review experts. In this paper, we aim to determine review expert’s rating by using the historical rating records and the final decision ...The project review information plays an important role in the recommendation of review experts. In this paper, we aim to determine review expert’s rating by using the historical rating records and the final decision results on the previous projects, and by means of some rules, we construct a rating matrix for projects and experts. For the data sparseness problem of the rating matrix and the 'cold start' problem of new expert recommendation, we assume that those projects/experts with similar topics have similar feature vectors and propose a review expert collaborative recommendation algorithm based on topic relationship. Firstly, we obtain topics of projects/experts based on latent Dirichlet allocation(LDA) model, and build the topic relationship network of projects/experts. Then, through the topic relationship between projects/experts, we find a neighbor collection which shares the largest similarity with target project/expert,and integrate the collection into the collaborative filtering recommendation algorithm based on matrix factorization. Finally, by learning the rating matrix to get feature vectors of the projects and experts, we can predict the ratings that a target project will give candidate review experts, and thus achieve the review expert recommendation. Experiments on real data set show that the proposed method could predict the review expert rating more effectively, and improve the recommendation effect of review experts.展开更多
According to the current problems of higher education management informatization,this paper puts forward a development scheme of collaborative platform on education management.The main technology includes three parts...According to the current problems of higher education management informatization,this paper puts forward a development scheme of collaborative platform on education management.The main technology includes three parts.First,integrate the distributed database and use two-tier linked list to realize dynamic data access.Second,the relation graph is used to display the data of each student,so as to realize the visual sharing of data.Third,realize the collaborative information security mechanism from three aspects to ensure the legal sharing of data.Finally,the platform development is completed with Java.It can help to improve the effectiveness of educating students.展开更多
Purpose:Based on real-world academic data,this study aims to use network embedding technology to mining academic relationships,and investigate the effectiveness of the proposed embedding model on academic collaborator...Purpose:Based on real-world academic data,this study aims to use network embedding technology to mining academic relationships,and investigate the effectiveness of the proposed embedding model on academic collaborator recommendation tasks.Design/methodology/approach:We propose an academic collaborator recommendation model based on attributed network embedding(ACR-ANE),which can get enhanced scholar embedding and take full advantage of the topological structure of the network and multi-type scholar attributes.The non-local neighbors for scholars are defined to capture strong relationships among scholars.A deep auto-encoder is adopted to encode the academic collaboration network structure and scholar attributes into a low-dimensional representation space.Findings:1.The proposed non-local neighbors can better describe the relationships among scholars in the real world than the first-order neighbors.2.It is important to consider the structure of the academic collaboration network and scholar attributes when recommending collaborators for scholars simultaneously.Research limitations:The designed method works for static networks,without taking account of the network dynamics.Practical implications:The designed model is embedded in academic collaboration network structure and scholarly attributes,which can be used to help scholars recommend potential collaborators.Originality/value:Experiments on two real-world scholarly datasets,Aminer and APS,show that our proposed method performs better than other baselines.展开更多
基金supported by National Natural Science Foundation of China(611750 68,61472168,61163004)Natural Science Foundation of Yunnan Province(2013FA130)Talent Promotion Project of Ministry of Science and Technology(2014HE001)
文摘The project review information plays an important role in the recommendation of review experts. In this paper, we aim to determine review expert’s rating by using the historical rating records and the final decision results on the previous projects, and by means of some rules, we construct a rating matrix for projects and experts. For the data sparseness problem of the rating matrix and the 'cold start' problem of new expert recommendation, we assume that those projects/experts with similar topics have similar feature vectors and propose a review expert collaborative recommendation algorithm based on topic relationship. Firstly, we obtain topics of projects/experts based on latent Dirichlet allocation(LDA) model, and build the topic relationship network of projects/experts. Then, through the topic relationship between projects/experts, we find a neighbor collection which shares the largest similarity with target project/expert,and integrate the collection into the collaborative filtering recommendation algorithm based on matrix factorization. Finally, by learning the rating matrix to get feature vectors of the projects and experts, we can predict the ratings that a target project will give candidate review experts, and thus achieve the review expert recommendation. Experiments on real data set show that the proposed method could predict the review expert rating more effectively, and improve the recommendation effect of review experts.
基金The authors received a specific funding with No.218051360020XN113 for this study。
文摘According to the current problems of higher education management informatization,this paper puts forward a development scheme of collaborative platform on education management.The main technology includes three parts.First,integrate the distributed database and use two-tier linked list to realize dynamic data access.Second,the relation graph is used to display the data of each student,so as to realize the visual sharing of data.Third,realize the collaborative information security mechanism from three aspects to ensure the legal sharing of data.Finally,the platform development is completed with Java.It can help to improve the effectiveness of educating students.
基金supported by National Natural Science Foundation of China(No.61603310)the Fundamental Research Funds for the Central Universities(No.XDJK2018B019).
文摘Purpose:Based on real-world academic data,this study aims to use network embedding technology to mining academic relationships,and investigate the effectiveness of the proposed embedding model on academic collaborator recommendation tasks.Design/methodology/approach:We propose an academic collaborator recommendation model based on attributed network embedding(ACR-ANE),which can get enhanced scholar embedding and take full advantage of the topological structure of the network and multi-type scholar attributes.The non-local neighbors for scholars are defined to capture strong relationships among scholars.A deep auto-encoder is adopted to encode the academic collaboration network structure and scholar attributes into a low-dimensional representation space.Findings:1.The proposed non-local neighbors can better describe the relationships among scholars in the real world than the first-order neighbors.2.It is important to consider the structure of the academic collaboration network and scholar attributes when recommending collaborators for scholars simultaneously.Research limitations:The designed method works for static networks,without taking account of the network dynamics.Practical implications:The designed model is embedded in academic collaboration network structure and scholarly attributes,which can be used to help scholars recommend potential collaborators.Originality/value:Experiments on two real-world scholarly datasets,Aminer and APS,show that our proposed method performs better than other baselines.
