学术社交网络中的权威学者推荐模型

doi:10.11772/j.issn.1001-9081.2020010110

计算机应用 ›› 2020, Vol. 40 ›› Issue (9): 2594-2599.DOI: 10.11772/j.issn.1001-9081.2020010110

学术社交网络中的权威学者推荐模型

李春英¹, 汤庸², 肖政宏¹, 李天送¹

1. 广东技术师范大学计算机科学学院, 广州 510665;
2. 华南师范大学计算机学院, 广州 510631

收稿日期:2020-02-02 修回日期:2020-05-17 出版日期:2020-09-10 发布日期:2020-05-19
通讯作者: 汤庸
作者简介:李春英(1978-),女,黑龙江齐齐哈尔人,副教授,博士,CCF会员,主要研究方向:社会网络与人本计算、Web数据挖掘以及教育大数据分析;汤庸(1964-),男,湖南张家界人,教授,博士生导师,博士,CCF会士,主要研究方向:人本计算与社交软件、学者大数据管理与知识图谱构建、社交网络与信息安全;肖政宏(1965-),男,湖南常德人,教授,博士,主要研究方向:大数据理论与技术、智能信息处理、机器学习;李天送(1994-),男,广东惠州人,硕士研究生,主要研究方向:社区检测、推荐系统、教育大数据分析与挖掘。
基金资助:
国家自然科学基金资助项目（61807009，61772211）；广东省科技计划项目（2017A040405057）；广东技术师范学院教学团队项目（57202020247）；广东技术师范学院创新强校项目（991460306）。

Influential scholar recommendation model in academic social network

LI Chunying¹, TANG Yong², XIAO Zhenghong¹, LI Tiansong¹

1. School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou Guangdong 510665, China;
2. School of Computer Science, South China Normal University, Guangzhou Guangdong 510631, China

Received:2020-02-02 Revised:2020-05-17 Online:2020-09-10 Published:2020-05-19
Supported by:
This work is partially supported by the National Natural Science Foundation of China (61807009, 61772211), the Science and Technology Planning Project of Guangdong Province (2017A040405057), the Teaching Team Program of Guangdong Polytechnic Normal University (57202020247), the Innovation and School Development Program of Guangdong Polytechnic Normal University (991460306).

摘要/Abstract

摘要： 目前，学术社交网络平台存在的信息过载和信息不对称等问题导致学者特别是影响力低的学者很难找到自己感兴趣的内容，同时，学术社交网络中影响力大的学者对学术社区的形成具有一定的促进作用并且对影响力低的学者的科学研究具有一定的导向作用，因此提出一种融合学术社区检测的权威学者推荐模型（ISRMACD）来为学术社交网络中的低影响力学者提供推荐服务。首先，利用影响力大的学者圈作为社区的核心结构对学术社交网络中学者间的关系纽带——好友关系所产生的复杂网络拓扑关系进行学术社区检测；然后，对社区内的学者计算影响力，并实现社区内部的权威学者推荐服务。在学者网数据集上的实验结果表明，该推荐模型在不同的权威学者推荐数量下均取得了较高的推荐质量，并且每次推荐10名权威学者取得的推荐精度最高，达到70%及以上。

关键词: 学术社交网络, 学者网, 推荐系统, 学术社区检测, 权威学者推荐

Abstract: At present, academic social network platforms have problems such as information overload and information asymmetry, which makes it difficult for scholars, especially those with low influence, to find contents they are interested in. At the same time, the scholars with high influence in the academic social network promote the formation of academic community and guide the scientific research of the scholars with low influence. Therefore, an Influential Scholar Recommendation Model based on Academic Community Detection (ISRMACD) was proposed to provide recommendation service for the scholars with low influence in academic social networks. First, the influential scholar group was used as the core structure of community to detect the academic community in complex network topological relationship generated by the relationship bonding — friendship among the scholars in the academic social network. Then the influences of scholars in the academic social network were calculated, and the recommendation service of influential scholars in the community was implemented. Experimental results on SCHOLAT dataset show that the proposed model achieves high recommendation quality under different influential scholar recommendation numbers, and has the best recommendation accuracy obtained by recommending 10 influential scholars each time, reaching 70% and above.

Key words: academic social network, SCHOLAT, recommendation system, academic community detection, influential scholar recommendation

中图分类号:

TP391.3

李春英, 汤庸, 肖政宏, 李天送. 学术社交网络中的权威学者推荐模型[J]. 计算机应用, 2020, 40(9): 2594-2599.

LI Chunying, TANG Yong, XIAO Zhenghong, LI Tiansong. Influential scholar recommendation model in academic social network[J]. Journal of Computer Applications, 2020, 40(9): 2594-2599.

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学术社交网络中的权威学者推荐模型

Influential scholar recommendation model in academic social network

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