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Data Analysis and Knowledge Discovery  2017, Vol. 1 Issue (11): 62-74    DOI: 10.11925/infotech.2096-3467.2017.0694
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Identifying Lead Players of User Innovation Communities Based on Feature Extraction and Random Forest Classification
Yuan Xinwei(), Yang Shaohua, Wang Chaochao, Du Zhanhe
School of Economics and Management, Xi’an University of Technology, Xi’an 710054, China
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Abstract  

[Objective] This paper aims to identify the lead players of user innovation communities to promote the open innovation for enterprises. [Methods] First, we extracted features of the users from related content and behavior data of the innovation community. Then, we proposed a method to idenfity the lead users based on Random Forest classification model. Finally, we examine our new method with real data from the MIUI forum of Xiaomi community. [Results] The proposed method could identify the lead and non-lead users. [Limitations] Only examined our method with the MIUI forum, therefore, adjustments were needed to use it for other user innovation communities. [Conclusions] The proposed method could identify lead users from various online communities more efficiently and effectively.

Key wordsUser Innovation Community      Lead User Identification      User Feature      Random Forest Classification     
Received: 14 July 2017      Published: 27 November 2017
ZTFLH:  C93 F27  

Cite this article:

Yuan Xinwei,Yang Shaohua,Wang Chaochao,Du Zhanhe. Identifying Lead Players of User Innovation Communities Based on Feature Extraction and Random Forest Classification. Data Analysis and Knowledge Discovery, 2017, 1(11): 62-74.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2017.0694     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2017/V1/I11/62

特征 主要来源
具有领先于普通用户的需求 Von Hippel E (1986)[7]
具有强烈的创新动机 Von Hippel E (1986)[7]
期望从需求解决方案中获得高收益 Morrison P D等(2004)[18]; Spann M等(2009)[19]; Oosterloo A(2010)[20]
对现有产品表现出强烈不满 Lüthje C等(2004)[8]; Conradie P D等(2016)[21]; Belz F M等(2010)[10]; Pajo S等(2013)[22]
作为意见领袖的潜质 Belz F M等(2010)[10]; Pajo S等(2013)[22]
参与性 Lüthje C等(2004)[8]; Belz F M等(2010)[10]; Pajo S等(2013)[22]
比普通用户更快速地采纳新产品 Pajo S等(2013)[22]
较强创新能力 Belz F M等(2010)[10]; 何国正等(2009)[23]
拥有丰富的产品知识 Lüthje C 等(2004) [8]; Belz F M 等(2010) [10]
拥有丰富的产品使用经验 Lüthje C等(2004)[8]; Conradie P D等(2016)[21]; Belz F M等(2010)[10]; Pajo S等(2013)[22]
行为特征 具体指标 指标含义 体现的领先用户特征
参与 积分 用户通过签到、发表主题和评论、保持在线以及参与社区论坛活动等方式获得的积分 参与性、产品知识
和使用经验
主题数 用户发表主题的数量
评论数 用户对他人主题的评论数
在线时长 用户在社区中所花费的时间长短
社区影响 贡献值 社区对用户贡献的认可, 在一些社区通过贡献值这一指标体现出来 产品知识和使用经验、
意见领袖潜质、创新
能力
威望值 社区对用户发表主题质量的肯定, 在一些社区通过威望值这一指标体现出来
主题平均回复量 用户发表的主题所获得的平均回复数量,
即主题平均回复量=主题总回复数量/主题数量
主题平均点击量 用户发表的主题所获得的平均点击数量,
即主题平均点击量=主题总点击数量/主题数量
精华帖数量 当用户发表的主题得到社区认可的精华帖数量
关系建立 好友数 用户在社区中的好友数量 参与性、意见领袖潜质
空间访问量 用户在社区中的个人主页空间的被访问数量
用户ID 主题核心内容 主题发表平台
137748*** 提供解决卡机问题的5种方法 PC
94494*** 提供谷歌套件的安装教程 PC
182392*** 汇总了小米手机存在的已知问题 PC
1579712*** 建议优化自动升级体验 小米手机4
310*** 提出通话录音对方声音偏低的
改进建议
PC
1594000*** 反馈手机软件安装问题 小米手机4c
词语 信息熵 词语 信息熵 词语 信息熵 词语 信息熵
安卓 0.316213 还原 0.223788 权限 0.734063 工具箱 0.262723
备份 0.611726 唤醒 0.658948 缺点 0.310863 工艺 0.201891
壁纸 0.610224 技能 0.324151 缺陷 0.264289 功耗 0.372700
避免 0.578136 技巧 0.345415 容量 0.494894 功率 0.201180
边框 0.451883 技术 0.593708 设定 0.410444 功能 0.793653
编程 0.273725 架构 0.241603 设计 0.609646 共享 0.509798
编译 0.259703 脚本 0.332176 深刻 0.257540 故障 0.273800
标准 0.684879 教程 0.598938 释放 0.190585 管理 0.789340
补丁 0.401210 解码 0.209270 授权 0.553083 规格 0.211333
参考 0.676205 解锁 0.302388 刷新 0.485019 耗电 0.613368
参照 0.219423 进程 0.599015 思考 0.229520 频段 0.424151
差异 0.187917 禁止 0.602119 提升 0.557852 频率 0.502831
沉浸 0.511611 精简 0.304325 突破 0.318078 品牌 0.239496
程度 0.491713 精密 0.218749 推荐 0.695444 品质 0.345167
程序 0.742381 精品 0.570579 挖掘 0.097196 平衡 0.187100
触摸屏 0.301239 均衡 0.357329 完美 0.713902 评测 0.487002
传感器 0.450762 开放 0.615575 维护 0.469622 评估 0.323698
创新 0.494803 开启 0.766367 系列 0.667252 屏蔽 0.734825
创造 0.337468 框架 0.483972 细节 0.567618 瓶颈 0.183485
搭载 0.370015 扩展 0.419518 细腻 0.251230 清理 0.724261
代码 0.637274 流畅 0.667485 限制 0.704581 运行 0.770446
颠覆 0.282982 流程 0.249353 协议 0.377887 增强 0.567740
对象 0.169484 路径 0.338769 虚拟 0.597106 制式 0.461678
二进制 0.186764 乱码 0.509190 渲染 0.325471 主板 0.462250
服务器 0.636913 美化 0.148695 研发 0.301277 专家 0.231291
改进 0.701178 命令 0.387072 研究 0.467130 字符 0.296423
改善 0.311125 模块 0.652269 验证 0.636157 最强 0.295451
根据 0.684804 内存 0.725427 移植 0.410075 最新 0.809079
根目录 0.318490 内核 0.460854 引领 0.261932
工程师 0.684590 配置 0.613033 优化 0.710139
领先用户 非领先用户 预测准确率
领先用户 28 2 93.33%
非领先用户 3 27 90%
领先用户 非领先用户 准确率
领先用户 19 1 95%
非领先用户 1 19 95%
领先用户 非领先用户 准确率
领先用户 19 1 95%
非领先用户 2 38 95%
领先用户 非领先用户 准确率
领先用户 19 1 95%
非领先用户 3 77 96.25%
领先用户 非领先用户 准确率
领先用户 19 1 95%
非领先用户 7 153 95.63%
领先用户(20名) 非领先用户(20名) 非领先用户(40名) 非领先用户(80名) 非领先用户(160名)
BP神经网络模型 85.00% 70.00% 72.50% 78.75% 76.11%
C-SVM分类模型 85.00% 90.00% 87.50% 93.75% 94.44%
随机森林分类模型 95.00% 95.00% 95.00% 96.25% 95.63%
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