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
Evidence-based literature reviews play a vital role in contemporary research,facilitating the synthesis of knowledge from multiple sources to inform decisionmaking and scientific advancements.Within this framework,de-...Evidence-based literature reviews play a vital role in contemporary research,facilitating the synthesis of knowledge from multiple sources to inform decisionmaking and scientific advancements.Within this framework,de-duplication emerges as a part of the process for ensuring the integrity and reliability of evidence extraction.This opinion review delves into the evolution of de-duplication,highlights its importance in evidence synthesis,explores various de-duplication methods,discusses evolving technologies,and proposes best practices.By addressing ethical considerations this paper emphasizes the significance of deduplication as a cornerstone for quality in evidence-based literature reviews.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.
文摘Evidence-based literature reviews play a vital role in contemporary research,facilitating the synthesis of knowledge from multiple sources to inform decisionmaking and scientific advancements.Within this framework,de-duplication emerges as a part of the process for ensuring the integrity and reliability of evidence extraction.This opinion review delves into the evolution of de-duplication,highlights its importance in evidence synthesis,explores various de-duplication methods,discusses evolving technologies,and proposes best practices.By addressing ethical considerations this paper emphasizes the significance of deduplication as a cornerstone for quality in evidence-based literature reviews.
