基于递归张量神经网络的微信公众号文章的新颖度评估方法

doi:10.3772/j.issn.1000-0135.2019.02.005

情报学报

2019, Vol. 38

Issue (2): 159-169 DOI: 10.3772/j.issn.1000-0135.2019.02.005

情报分析方法与技术

本期目录 | 过刊浏览 | 高级检索

基于递归张量神经网络的微信公众号文章的新颖度评估方法

王平¹, 侯景瑞², 吴任力²

1. 武汉大学信息资源研究中心，武汉 430072
2. 武汉大学信息管理学院，武汉 430072

The Novelty Evaluation of Articles on WeChat Subscription Based on Recursive Neural Tensor Network

Wang Ping¹, Hou Jingrui², Wu Renli²

1. Center for Studies of Information Resources, Wuhan University, Wuhan 430072
2. School of Information Management, Wuhan University, Wuhan 430072

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摘要自媒体平台内容同质化问题日益严重，导致用户难以从中获取新颖优质的信息，因此对其文章内容进行新颖度评估就显得尤为重要。本文以微信公众号文章为例，提出了一种自媒体平台文章的新颖度评估方法，该方法利用非监督的句级Doc2Vec语言模型构建文本向量，基于递归张量神经网络构建新颖度测度模型，进而通过模型训练求解并量化评估文章的新颖度。本文从微信公众平台自动采集4,628篇文章开展实证研究，首先设置不同的张量切片数量进行对照实验，综合新颖度分布特征和训练时间计算最优参数，然后通过计算文档相似度验证了文章的新颖度和相似度之间的线性回归关系。该实验结果证明了本方法具有较强的可行性和有效性，从深度学习的视角拓展和丰富了文本新颖度评估的研究，也为自媒体平台的新颖话题探测和前沿知识发现提供了支撑。

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	王平
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关键词 ：新颖度评估, 递归张量神经网络, Doc2Vec, 文本向量, 余弦相似度

收稿日期: 2018-08-13

基金资助:国家自然科学基金面上项目“基于深度学习的多特征层次化用户生产内容质量测度与缺陷检测模型研究”（71774121）；国家自然科学基金青年科学基金项目“多因素融合下的微博话题可信度评估模型及实证研究”（71303179）；中央高校基本科研业务费专项资金自主项目“人机交互与协作创新团队”（Whu2016020）。

作者简介: 王平，男，1981年生，副教授，主要研究领域为信息质量、文本挖掘等，E-mail:wangping@whu.edu.cn；侯景瑞，男，1993年生，硕士研究生，主要研究领域为数据挖掘与知识组织；吴任力，男，1994年生，硕士研究生，主要研究领域为信息资源管理、数据挖掘。

引用本文:

王平, 侯景瑞, 吴任力. 基于递归张量神经网络的微信公众号文章的新颖度评估方法[J]. 情报学报, 2019, 38(2): 159-169.
Wang Ping, Hou Jingrui, Wu Renli. The Novelty Evaluation of Articles on WeChat Subscription Based on Recursive Neural Tensor Network. 情报学报, 2019, 38(2): 159-169.

链接本文:

https://qbxb.istic.ac.cn/CN/10.3772/j.issn.1000-0135.2019.02.005 或 https://qbxb.istic.ac.cn/CN/Y2019/V38/I2/159

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