Research on Search Intent Prediction for Big Data of National Grid System Standards

Hu Xueyong; Wang Bei; Zhao Lei; Yang Yang; Hu Aiyu; Pan Ge; Zhou Baoxian

doi:10.1145/3404555.3404642

Research on Search Intent Prediction for Big Data of National Grid System Standards

Hu Xueyong
, Wang Bei
, Zhao Lei
, Yang Yang
, Hu Aiyu
, Pan Ge
, Zhou Baoxian

Tianjin University of Science & Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Smart grids are becoming more complex due to the development of big data., and technical documents and institutional standards are constantly updated. As a result, It is difficult for workers in different positions to obtain the required information and data. This thesis is oriented towards this problem, and combined with deep learning algorithms to build a user intent prediction model based on the existing knowledge map. By extracting user characteristics and using a dynamic matching algorithm, the purpose of intent prediction is achieved. In this way, the required standards and requirements can be found faster and more directly in the work process, which effectively improves the working efficiency of employees and reduces the difficulty of learning and training.

Original language	English
Title of host publication	ICCAI 2020 - Proceedings of the 2020 6th International Conference on Computing and Artificial Intelligence
Publisher	Association for Computing Machinery
Pages	89-93
Number of pages	5
ISBN (Electronic)	9781450377089
DOIs	https://doi.org/10.1145/3404555.3404642
Publication status	Published - 23 Apr 2020
Externally published	Yes
Event	6th International Conference on Computing and Artificial Intelligence, ICCAI 2020 - Virtual, Online, China Duration: 23 Apr 2020 → 26 Apr 2020

Publication series

Name	ACM International Conference Proceeding Series

Conference

Conference	6th International Conference on Computing and Artificial Intelligence, ICCAI 2020
Country/Territory	China
City	Virtual, Online
Period	23/04/20 → 26/04/20

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Deep learning
convolutional neural network
dynamic matching
knowledge map
personalization
search intent prediction

Access to Document

10.1145/3404555.3404642

Cite this

Xueyong, H., Bei, W., Lei, Z., Yang, Y., Aiyu, H., Ge, P., & Baoxian, Z. (2020). Research on Search Intent Prediction for Big Data of National Grid System Standards. In ICCAI 2020 - Proceedings of the 2020 6th International Conference on Computing and Artificial Intelligence (pp. 89-93). (ACM International Conference Proceeding Series). Association for Computing Machinery. https://doi.org/10.1145/3404555.3404642

@inproceedings{9a130f8728904133b5a623b7857ac7ed,

title = "Research on Search Intent Prediction for Big Data of National Grid System Standards",

abstract = "Smart grids are becoming more complex due to the development of big data., and technical documents and institutional standards are constantly updated. As a result, It is difficult for workers in different positions to obtain the required information and data. This thesis is oriented towards this problem, and combined with deep learning algorithms to build a user intent prediction model based on the existing knowledge map. By extracting user characteristics and using a dynamic matching algorithm, the purpose of intent prediction is achieved. In this way, the required standards and requirements can be found faster and more directly in the work process, which effectively improves the working efficiency of employees and reduces the difficulty of learning and training.",

keywords = "Deep learning, convolutional neural network, dynamic matching, knowledge map, personalization, search intent prediction",

author = "Hu Xueyong and Wang Bei and Zhao Lei and Yang Yang and Hu Aiyu and Pan Ge and Zhou Baoxian",

note = "Publisher Copyright: {\textcopyright} 2020 ACM.; 6th International Conference on Computing and Artificial Intelligence, ICCAI 2020 ; Conference date: 23-04-2020 Through 26-04-2020",

year = "2020",

month = apr,

day = "23",

doi = "10.1145/3404555.3404642",

language = "English",

series = "ACM International Conference Proceeding Series",

publisher = "Association for Computing Machinery",

pages = "89--93",

booktitle = "ICCAI 2020 - Proceedings of the 2020 6th International Conference on Computing and Artificial Intelligence",

}

Xueyong, H, Bei, W, Lei, Z, Yang, Y, Aiyu, H, Ge, P & Baoxian, Z 2020, Research on Search Intent Prediction for Big Data of National Grid System Standards. in ICCAI 2020 - Proceedings of the 2020 6th International Conference on Computing and Artificial Intelligence. ACM International Conference Proceeding Series, Association for Computing Machinery, pp. 89-93, 6th International Conference on Computing and Artificial Intelligence, ICCAI 2020, Virtual, Online, China, 23/04/20. https://doi.org/10.1145/3404555.3404642

Research on Search Intent Prediction for Big Data of National Grid System Standards. / Xueyong, Hu; Bei, Wang; Lei, Zhao et al.
ICCAI 2020 - Proceedings of the 2020 6th International Conference on Computing and Artificial Intelligence. Association for Computing Machinery, 2020. p. 89-93 (ACM International Conference Proceeding Series).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Research on Search Intent Prediction for Big Data of National Grid System Standards

AU - Xueyong, Hu

AU - Bei, Wang

AU - Lei, Zhao

AU - Yang, Yang

AU - Aiyu, Hu

AU - Ge, Pan

AU - Baoxian, Zhou

PY - 2020/4/23

Y1 - 2020/4/23

N2 - Smart grids are becoming more complex due to the development of big data., and technical documents and institutional standards are constantly updated. As a result, It is difficult for workers in different positions to obtain the required information and data. This thesis is oriented towards this problem, and combined with deep learning algorithms to build a user intent prediction model based on the existing knowledge map. By extracting user characteristics and using a dynamic matching algorithm, the purpose of intent prediction is achieved. In this way, the required standards and requirements can be found faster and more directly in the work process, which effectively improves the working efficiency of employees and reduces the difficulty of learning and training.

AB - Smart grids are becoming more complex due to the development of big data., and technical documents and institutional standards are constantly updated. As a result, It is difficult for workers in different positions to obtain the required information and data. This thesis is oriented towards this problem, and combined with deep learning algorithms to build a user intent prediction model based on the existing knowledge map. By extracting user characteristics and using a dynamic matching algorithm, the purpose of intent prediction is achieved. In this way, the required standards and requirements can be found faster and more directly in the work process, which effectively improves the working efficiency of employees and reduces the difficulty of learning and training.

KW - Deep learning

KW - convolutional neural network

KW - dynamic matching

KW - knowledge map

KW - personalization

KW - search intent prediction

UR - https://www.scopus.com/pages/publications/85092227229

U2 - 10.1145/3404555.3404642

DO - 10.1145/3404555.3404642

M3 - Conference contribution

AN - SCOPUS:85092227229

T3 - ACM International Conference Proceeding Series

SP - 89

EP - 93

BT - ICCAI 2020 - Proceedings of the 2020 6th International Conference on Computing and Artificial Intelligence

PB - Association for Computing Machinery

T2 - 6th International Conference on Computing and Artificial Intelligence, ICCAI 2020

Y2 - 23 April 2020 through 26 April 2020

ER -

Xueyong H, Bei W, Lei Z, Yang Y, Aiyu H, Ge P et al. Research on Search Intent Prediction for Big Data of National Grid System Standards. In ICCAI 2020 - Proceedings of the 2020 6th International Conference on Computing and Artificial Intelligence. Association for Computing Machinery. 2020. p. 89-93. (ACM International Conference Proceeding Series). doi: 10.1145/3404555.3404642

Research on Search Intent Prediction for Big Data of National Grid System Standards

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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