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Browse type service awareness indicator prediction method based on multiple label learning

A technology of multi-label learning and business perception, applied in the field of prediction of browsing business perception indicators based on multi-label learning

Active Publication Date: 2017-10-24
BEIJING UNION UNIVERSITY
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  • Abstract
  • Description
  • Claims
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AI Technical Summary

Problems solved by technology

The customer service department only discovers the problem of service experience when it receives complaints from users, and then coordinates with the network operation and maintenance and optimization department to troubleshoot and solve the problem, which is often very passive

Method used

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  • Browse type service awareness indicator prediction method based on multiple label learning
  • Browse type service awareness indicator prediction method based on multiple label learning
  • Browse type service awareness indicator prediction method based on multiple label learning

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Embodiment Construction

[0057] Such as figure 1 , 2 As shown, the present invention proposes a browsing class service perception index prediction method based on multi-label learning, comprising the following steps:

[0058] Step S1: Construct a training sample set

[0059] It is known that under the local mobile network of a certain city (such as the LTE network of Beijing Mobile), when a user uses a web browsing service app (such as UCweb, QQ browser, etc.) , Sohu homepage, etc.), the “webpage browsing service perception sample” at that time is obtained by means of data collection software deployed on user terminals; All samples constitute the "browsing business perception sample set".

[0060] The information contained in the web browsing service perception sample (i.e. the sample field) should at least include: date, time, network standard, cell ID, current latitude and longitude of the terminal, field strength (the name is different under different standards: such as RxLevel of GSM network, L...

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Abstract

The invention discloses a browse type service awareness indicator prediction method based on multiple label learning. The browse type service awareness indicator prediction method based on multiple label learning solves the problem about how to timely and accurately predict the KQI of the webpage browsing service of a user according to the scene where the user is. The browse type service awareness indicator prediction method based on multiple label learning can make prediction and early warning for good and bad of the service experience in a special scene for the user according to the service awareness historical data of a large number of users, that is, good and bad of the service awareness indicator in different scenes, and is conductive to discovery of the service experience problem as soon as possible so as to make corresponding measures to make improvement, and can effectively reduce the complaint rate and the rate of off-network.

Description

technical field [0001] The invention belongs to the technical field of network services, and in particular relates to a method for predicting perception indicators of browsing services based on multi-label learning. Background technique [0002] When mobile network users use OTT services (such as web browsing, video playback, etc.), their service experience can generally be evaluated by a set of KQI (Key Quality Indicators) indicators, such as webpage opening delay, download rate, etc. The quality of this experience is affected by many factors, including the quality of the terminal, the quality of the mobile network where the service is used, the quality of the APP, the bandwidth and load of the SP website server cluster, and so on. [0003] As the transmission channel provider of various services and the key link of service experience assurance, telecom operators need to ensure the user's service experience as much as possible, otherwise it may cause user complaints or even...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/06G06Q50/30
CPCG06Q10/06393G06Q50/40
Inventor 李克徐小龙王海
Owner BEIJING UNION UNIVERSITY
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