| 基于社区划分的学术论文推荐模型 |
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| 引用本文： | 黄泳航,汤庸,李春英,汤志康,刘继伟.基于社区划分的学术论文推荐模型[J].计算机应用,2016,36(5):1279-1283. |
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| 作者姓名： | 黄泳航 汤庸 李春英 汤志康 刘继伟 |
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| 作者单位： | 1. 华南师范大学 计算机学院, 广州 510631;2. 广东技术师范学院 计算机网络中心, 广州 510655;3. 广东技术师范学院 计算机学院, 广州 510655 |
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| 基金项目： | 国家863计划重大项目(2013AA01A212);国家自然科学基金资助项目(61272067,61502180);广东省自然科学基金资助项目(2015A030310509,2014A030310238);广州市科技计划项目(2014J4300033)。 |
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| 摘 要： | 针对学术社交网络独有的社交性,构建了基于社区划分的学术论文推荐模型。模型选择社区复杂好友关系网络图中最大连通分量作为数据处理逻辑单元,在此基础上进行核心关系网划分,并采用非参数控制的方式,在所建立的核心关系网内建立标签,在学术社交网络中通过标签传播进行社区划分,根据社区划分结果在社区内部的用户之间推荐学术论文。该社区划分算法与经典社区划分算法在人工网络上进行仿真实验,结果表明该算法在不同特征的人工网络上皆能取得良好的社区发现质量。
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| 关 键 词： | 核心关系网 社区划分 标签传播 自适应阈值 学术论文推荐 |
| 收稿时间： | 2015-10-29 |
| 修稿时间： | 2015-12-28 |
Academic paper recommendation model based on community partition |
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| HUANG Yonghang,TANG Yong,LI Chunying,TANG Zhikang,LIU Jiwei.Academic paper recommendation model based on community partition[J].journal of Computer Applications,2016,36(5):1279-1283. |
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| Authors: | HUANG Yonghang TANG Yong LI Chunying TANG Zhikang LIU Jiwei |
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| Affiliation: | 1. School of Computer, South China Normal University, Guangzhou Guangdong 510631, China;2. Computer Network Center, Guangdong Polytechnic Normal University, Guangzhou Guangdong 510665, China;3. School of Computer, Guangdong Polytechnic Normal University, Guangzhou Guangdong 510655, China |
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| Abstract: | An academic paper recommendation model based on community partition was proposed according to sociability in social network. The model regarded the largest connected component in complex network as the logic unit in data processing, and divided up the largest connected component into non-intersect kernel sub-network. The labels would be established according to kernel sub-network by non-parameter control mode. Communities were divided in scholar social network through label propagation, and academic papers were recommended among the users in the communities by the results of the community partition. The proposed community partition method was compared with the classic community partition method in the experiments on artificial network. The experimental results show that the proposed method can achieve good community partition qualities on different characteristic artificial networks. |
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| Keywords: | kernel sub-network community partition label propagation self-adaptive threshold academic paper recommendation |
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