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
N1 - Publisher Copyright:
© 2020 ACM.
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 - http://www.scopus.com/inward/record.url?scp=85092227229&partnerID=8YFLogxK
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 -
