面向微博用户的消费意图识别算法
利用迁移学习的方法,融合京东问答平台数据与少量已标注的微博数据构建训练集,提出一种基于注意力机制的双向长短期记忆神经网络(Attentional-Bi-LSTM)模型,用于识别用户的隐性消费意图.针对显性意图识别问题,提出一种结合TF-IDF (term frequency-inverse document frequency)与句法分析中动宾关系(VOB)的消费意图对象提取算法.实验结果表明,通过将迁移京东问答平台的数据与微博数据相融合,可以有效地扩充训练集,在此基础上训练的神经网络分类模型具有较高的准确率和召回率;融合VOB和TF-IDF的显性消费意图对象提取方法的准确率达到78.8％....
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| Published in | 北京大学学报（自然科学版） Vol. 56; no. 1; pp. 68 - 74 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | Chinese |
| Published |
东北大学计算机科学与工程学院,沈阳,110819%北京理工大学计算机学院,北京,100081
20.01.2020
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0479-8023 |
| DOI | 10.13209/j.0479-8023.2019.102 |
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| Abstract | 利用迁移学习的方法,融合京东问答平台数据与少量已标注的微博数据构建训练集,提出一种基于注意力机制的双向长短期记忆神经网络(Attentional-Bi-LSTM)模型,用于识别用户的隐性消费意图.针对显性意图识别问题,提出一种结合TF-IDF (term frequency-inverse document frequency)与句法分析中动宾关系(VOB)的消费意图对象提取算法.实验结果表明,通过将迁移京东问答平台的数据与微博数据相融合,可以有效地扩充训练集,在此基础上训练的神经网络分类模型具有较高的准确率和召回率;融合VOB和TF-IDF的显性消费意图对象提取方法的准确率达到78.8％. |
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| AbstractList | 利用迁移学习的方法,融合京东问答平台数据与少量已标注的微博数据构建训练集,提出一种基于注意力机制的双向长短期记忆神经网络(Attentional-Bi-LSTM)模型,用于识别用户的隐性消费意图.针对显性意图识别问题,提出一种结合TF-IDF (term frequency-inverse document frequency)与句法分析中动宾关系(VOB)的消费意图对象提取算法.实验结果表明,通过将迁移京东问答平台的数据与微博数据相融合,可以有效地扩充训练集,在此基础上训练的神经网络分类模型具有较高的准确率和召回率;融合VOB和TF-IDF的显性消费意图对象提取方法的准确率达到78.8％. |
| Author | 夏利 贾云龙 王国仁 韩东红 林海原 |
| AuthorAffiliation | 东北大学计算机科学与工程学院,沈阳,110819%北京理工大学计算机学院,北京,100081 |
| AuthorAffiliation_xml | – name: 东北大学计算机科学与工程学院,沈阳,110819%北京理工大学计算机学院,北京,100081 |
| Author_FL | XIA Li HAN Donghong JIA Yunlong LIN Haiyuan WANG Guoren |
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| DocumentTitle_FL | Consumption Intent Recognition Algorithms for Weibo Users |
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| Keywords | 注意力机制 意图对象提取 迁移学习 消费意图识别 |
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| Title | 面向微博用户的消费意图识别算法 |
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