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Service demand dynamic prediction method and system based on context awareness

A demand forecasting and situational awareness technology, applied in business, instrumentation, data processing applications, etc., can solve the problems of reducing the accuracy of service demand, the impact of models that cannot fully learn the impact of service demand, and the impact of not fully considering the impact of user service demand. Effect of improving interpretability and precision, improving accuracy

Active Publication Date: 2021-03-19
YANTAI UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The inventors found that most of the existing research work is based on collaborative filtering, support vector machines, matrix decomposition and machine learning methods to realize the prediction of user service needs; although the above research work has achieved good results, the existing research work is usually Considering the impact of different scenarios on user service demand as equally important, the model cannot fully learn the impact of different scenarios on service demand, thereby reducing the accuracy of service demand prediction; at the same time, the existing research work does not fully consider the user's The impact of the scene at the location on its service demand, resulting in low service demand forecast accuracy

Method used

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  • Service demand dynamic prediction method and system based on context awareness
  • Service demand dynamic prediction method and system based on context awareness
  • Service demand dynamic prediction method and system based on context awareness

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Experimental program
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Embodiment 1

[0037] The purpose of this embodiment is to provide a method for dynamic forecasting of service demand based on situation awareness.

[0038] A method for dynamic forecasting of service demand based on situation awareness, including:

[0039] Obtain the relevant data when the user puts forward the service demand, including the user's characteristic information, the scene information and the proposed service demand information; use the deep interactive neural network model enhanced by the pre-trained attention mechanism to dynamically predict the service demand;

[0040] Wherein, the network model includes an interaction unit, an influence weight learning module and a service demand prediction module, and the interaction relationship between different scenarios and service demands is captured by the interaction unit; and then the influence weight learning module learns different The influence weight of the scenario on the service demand; finally, the service demand prediction i...

Embodiment 2

[0119] The purpose of this embodiment is to provide a dynamic service demand forecasting system based on situation awareness

[0120] A dynamic service demand forecasting system based on situation awareness, including:

[0121] The data acquisition unit is configured to acquire relevant data when the user puts forward a service demand, including user characteristic information, scene information and proposed service demand information;

[0122] The service demand prediction unit is configured to use the pre-trained attention mechanism enhanced deep interactive neural network model to perform dynamic prediction of service demand;

[0123] Wherein, the network model includes an interaction unit, an influence weight learning module and a service demand prediction module, and the interaction relationship between different scenarios and service demands is captured by the interaction unit; and then the influence weight learning module learns different The influence weight of the sc...

Embodiment 3

[0125] The purpose of this embodiment is to provide an electronic device.

[0126] An electronic device, comprising a memory, a processor, and a computer program stored and run on the memory, when the processor executes the program, the described system for dynamic forecasting of service demand based on situational awareness is implemented, including:

[0127] Obtain the relevant data when the user puts forward the service demand, including the user's characteristic information, the scene information and the proposed service demand information; use the deep interactive neural network model enhanced by the pre-trained attention mechanism to dynamically predict the service demand;

[0128] Wherein, the network model includes an interaction unit, an influence weight learning module and a service demand prediction module, and the interaction relationship between different scenarios and service demands is captured by the interaction unit; and then the influence weight learning modul...

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Abstract

The invention provides a service demand dynamic prediction method and system based on context awareness, and the method comprises the steps: adaptively capturing the interaction relation between different scenes and service demands through an interaction unit of an AMEDIN model, and carrying out the explicit modeling of the impact on the service demands from the different scenes; then, combining the scene features, the interaction relationship and the service demand features, and obtaining influence weights of different scenes on the service demand based on an attention mechanism; finally, anAMEDIN model is trained on the basis of the user features, the weighted scene features and the service demand features, and dynamic prediction of the service demand of context awareness is achieved onthe basis of the trained AMEDIN model. A large number of experiments are carried out on the basis of a real data set provided by Moviels and Alibaba, and experimental results show that the method provided by the invention is feasible and effective.

Description

technical field [0001] The present disclosure relates to the field of computer application technology, and in particular to a method and system for dynamic forecasting of service demand based on situation awareness. Background technique [0002] In recent years, with the rapid development and popularization of service computing, Internet of Things, smart terminals, and 5G networks, more and more users can access feature-rich services from different fields anytime, anywhere to complete work and daily affairs. With the rapid increase in the number of available services on the network, it is difficult for users to quickly and timely find services that meet their needs, which seriously affects user satisfaction and reduces the utilization of service resources. Active service recommendation has gradually become a key technology for realizing intelligent services, and service demand dynamic prediction is the basis for realizing active service recommendation. How to realize the dy...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q30/02
CPCG06Q30/0202
Inventor 刘志中齐永波丰凯初佃辉王莹洁
Owner YANTAI UNIV