个人信息Personal Information
副教授
博士生导师
硕士生导师
性别:男
毕业院校:北京大学
学位:博士
所在单位:数学科学学院
学科:运筹学与控制论
电子邮箱:rui_li@dlut.edu.cn
Spatial-temporal restricted supervised learning for collaboration recommendation
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论文类型:期刊论文
发表时间:2019-06-01
发表刊物:SCIENTOMETRICS
收录刊物:SSCI、SCIE
卷号:119
期号:3
页面范围:1497-1517
ISSN号:0138-9130
关键字:Spatial-temporal description; Academic influence; Supervised learning; Collaboration recommendation
摘要:Collaboration recommendation from scholarly big data is an important but challenging problem as it might suffer the difficulty of accurate recommendation from three aspects: how to efficiently integrate the available author-related information, how to precisely describe the characteristics of the scholarly data samples, and how to extract the intrinsic features that are more suitable for collaboration recommendation. Facing these challenges, we incorporate the temporal and academic-influence information of the publications with the spatial information of the researchers to present a spatial-temporal restricted supervised learning (STSL) model for collaboration recommendation. We first present a topic clustering model to determine the topic distribution vector of each researcher, where a temporal parameter is introduced to exponentially weight each topic distribution vector and an academic-influence parameter is further introduced to linearly combine all the topic distribution vectors of the publications. Then, inspired by the geographical-advantage phenomena in collaboration, spatial labels are generated by using the personal information of the researchers. Furthermore, considering that the publication data enhanced by spatial-temporal and academic-influence descriptions usually exhibit multimodal or mixmodal properties, we propose a data-driven supervised learning model to extract the intrinsic features inhered in data, which determines a low-dimensional recommendation subspace. A number of experiments are conducted to test the impact of the topic-clustering number, the temporal parameter, the academic-influence parameter, and the number of extracted features. Besides, several widely-used models are adopted to compare with the proposed STSL model for collaboration recommendation, with results verifying its feasibility and effectiveness.