[1]宋 毅.基于深度学习挖掘用户搜索主题研究[J].计算机技术与发展,2021,31(01):43-47.[doi:10. 3969 / j. issn. 1673-629X. 2021. 01. 008]
　SONG Yi.Research on Mining User Search Topic Based on Deep Learning[J].,2021,31(01):43-47.[doi:10. 3969 / j. issn. 1673-629X. 2021. 01. 008]
点击复制

基于深度学习挖掘用户搜索主题研究()
分享到：

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
31
期数:
2021年01期
页码:
43-47
栏目:
大数据分析与挖掘
出版日期:
2021-01-10

文章信息/Info

Title:
Research on Mining User Search Topic Based on Deep Learning
文章编号:
1673-629X(2021)01-0043-05
作者:
宋 毅
哈尔滨华德学院 数据科学与人工智能学院,黑龙江 哈尔滨 150025
Author(s):
SONG Yi
School of Data Science and Artificial Intelligence,Harbin Huade University,Harbin 150025,China
关键词:
深度学习; 用户搜索主题; 用户模型; 挖掘兴趣; 个性化搜索
Keywords:
deep learning; user search topic; user model; mining interest; personalized search
分类号:
TP39
DOI:
10. 3969 / j. issn. 1673-629X. 2021. 01. 008
摘要:
主要研究了基于深度学习技术挖掘用户搜索主题相关的感兴趣内容。 通过深度挖掘算法分析用户搜索记录、查询历史以及用户感兴趣的相关文档视为用户搜索主题数据的来源,进而挖掘兴趣主题。挖掘模型主要采用向量空间模型,将用户搜索主题模型表示成用户搜索主题向量形式。 形成主题和用户兴趣关系网,用户搜索主题向量的构造过程:选择一组用户查询词,并对它们进行深度挖掘分类,最后用它们构造用户搜索主题特征向量,进而分析用户兴趣点。 结合用户随着时间的变化,以及过程中有不用的搜索词,以及无关的搜索噪声词去掉,调整兴趣度,用户搜索主题需要具有更新学习机制,动态跟踪了用户兴趣变化趋势。 该用户搜索主题研究过程克服了数据稀疏、类别偏差、扩展性差等缺点。 实验结果表明,该模型识别用户搜索主题准确率良好。
Abstract:
We mainly study that the content of the interest related to the user’s search topic based on deep learning is mined. Through deep mining algorithm,the user’s search records,query history and relevant documents that the user is interested in are analyzed as the source of user’s search subject data to mine the interest subjects. The mining model mainly uses the vector space model to represent the user search topic model in the form of user search topic vector. The construction process of user search topic vector is as follows: selecting a group of user query words,then mining and classifying them in depth,finally using them to construct user search topic fea-ture vector to analyze user interest points. Combined with the changes of users over time,the different search terms in the process,? and the irrelevant search noise words removed,the degree of interest adjusted,users need to have an updated learning mechanism to dynamically track the changing trend of user’s interest. The research process of user search topic overcomes the shortcomings of data sparsity,category deviation and poor scalability. The experiment shows that the model has better accuracy in identifying user’s search-ing topics.

相似文献/References:

[1]陈强锐,谢世朋.基于深度学习的肺部肿瘤检测方法[J].计算机技术与发展,2018,28(04):201.[doi:10.3969/ j. issn.1673-629X.2018.04.043]
　CHEN Qiang-rui,XIE Shi-peng.Lung Cancer Detection Method Based on Deep Learning[J].,2018,28(01):201.[doi:10.3969/ j. issn.1673-629X.2018.04.043]
[2]施泽浩,赵启军.基于全卷积网络的目标检测算法[J].计算机技术与发展,2018,28(05):55.[doi:10.3969/j.issn.1673－629X.2018.05.013]
　SHI Ze-hao,ZHAO Qi-jun.Object Detection Algorithm Based on Fully Convolutional Neural Network[J].,2018,28(01):55.[doi:10.3969/j.issn.1673－629X.2018.05.013]
[3]黄法秀,张世杰,吴志红,等.数据增广下的人脸识别研究[J].计算机技术与发展,2020,30(03):67.[doi:10. 3969 / j. issn. 1673-629X. 2020. 03. 013]
　HUANG Fa-xiu,ZHANG Shi-jie,WU Zhi-hong,et al.Research on Face Recognition Based on Data Augmentation[J].,2020,30(01):67.[doi:10. 3969 / j. issn. 1673-629X. 2020. 03. 013]
[4]陈浩翔,蔡建明,刘铿然,等. 手写数字深度特征学习与识别[J].计算机技术与发展,2016,26(07):19.
　CHEN Hao-xiang,CAI Jian-ming,LIU Keng-ran,et al. Deep Learning and Recognition of Handwritten Numeral Features[J].,2016,26(01):19.
[5]高翔,陈志,岳文静,等.基于视频场景深度学习的人物语义识别模型[J].计算机技术与发展,2018,28(06):53.[doi:10.3969/ j. issn.1673-629X.2018.06.012]
　GAO Xiang,CHEN Zhi,YUE Wen-jing,et al.Human Semantic Recognition Model Based on Video Scene Deep Learning[J].,2018,28(01):53.[doi:10.3969/ j. issn.1673-629X.2018.06.012]
[6]贺飞翔,赵启军. 基于深度学习的头部姿态估计[J].计算机技术与发展,2016,26(11):1.
　HE Fei-xiang,ZHAO Qi-jun. Head Pose Estimation Based on Deep Learning[J].,2016,26(01):1.
[7]徐 融,邱晓晖.一种改进的 YOLO V3 目标检测方法[J].计算机技术与发展,2020,30(07):30.[doi:10. 3969 / j. issn. 1673-629X. 2020. 07. 007]
　XU Rong,QIU Xiao-hui.An Improved YOLO V3 Object Detection[J].,2020,30(01):30.[doi:10. 3969 / j. issn. 1673-629X. 2020. 07. 007]
[8]曾志平[] [],萧海东[],张新鹏[]. 基于DBN的金融时序数据建模与决策[J].计算机技术与发展,2017,27(04):1.
　ZENG Zhi-ping[] [],XIAO Hai-dong[],ZHANG Xin-peng[]. Modeling and Decision-making of Financial Time Series Data with DBN[J].,2017,27(01):1.
[9]李全兵,文 钊*,田艳梅*,等.基于 WGAN 的音频关键词识别研究[J].计算机技术与发展,2021,31(08):26.[doi:10. 3969 / j. issn. 1673-629X. 2021. 08. 005]
　LI Quan-bing,WEN Zhao *,TIAN Yan-mei *,et al.Research on Audio Keywords Recognition Based on WassersteinGenerative Adversarial Network[J].,2021,31(01):26.[doi:10. 3969 / j. issn. 1673-629X. 2021. 08. 005]
[10]李宏林. 分析式纹理合成技术及其在深度学习的应用[J].计算机技术与发展,2017,27(11):7.
　LI Hong-lin. Analyzed Texture-synthesis Techniques and Their Applications in Deep Learning[J].,2017,27(01):7.

更新日期/Last Update: 2020-01-10