Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Man-machine conversation satisfaction degree prediction method and system

A forecasting method, forecasting system technology, applied in special data processing applications, instruments, electrical digital data processing, etc., can solve problems such as complex satisfaction forecasting

Active Publication Date: 2015-07-01
BEIJING JINGDONG SHANGKE INFORMATION TECH CO LTD +1
View PDF5 Cites 16 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] Based on this, it is necessary to provide a method and system for predicting the satisfaction of human-machine conversations in order to solve the technical problems of the prior art that predict the satisfaction with customers is relatively complicated.

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Man-machine conversation satisfaction degree prediction method and system
  • Man-machine conversation satisfaction degree prediction method and system
  • Man-machine conversation satisfaction degree prediction method and system

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0018] The present invention will be described in further detail below in conjunction with the accompanying drawings and specific embodiments.

[0019] Such as figure 1 Shown is a work flow diagram of a method for predicting satisfaction of man-machine conversation in the present invention, including:

[0020] Step S101, including: acquiring satisfaction training data of a plurality of man-machine training sessions, each of the satisfaction training data includes the satisfaction result of the user on the man-machine training session and the results extracted in the man-machine training session. session features;

[0021] Step S102, including: using the conversation features of the training man-machine conversation as training samples, and using the corresponding satisfaction results as target values ​​to perform classification model training to obtain a classification model. The classification model uses the conversation features of the training samples and the target values...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention discloses a man-machine conversation satisfaction degree prediction method and system. The method comprises the steps that satisfaction degree training data of a plurality of training man-machine conversations are obtained; conversation characteristics of the training man-machine conversations are adopted as training samples, corresponding satisfaction degree results are adopted as target values for classification model training, and a classification model is obtained; an actual man-machine conversation is obtained, the conversation characteristics of the actual man-machine conversation are extracted, and the conversation characteristics of the actual man-machine conversation are input into the classification model to obtain the satisfaction degree result predicted by the classification model. When the satisfaction degree is predicted, the conversation characteristics influencing prediction can be added or deleted easily, through a statistical mode, the weights, needing to be specifically set, of all the characteristics do not need to be directly considered any more, only the proper classification model needs to be selected, and weight setting is automatically completed through the study of the classification model on the training samples.

Description

technical field [0001] The present invention relates to the technical field related to man-machine conversation, in particular to a method and system for predicting satisfaction degree of man-machine conversation. Background technique [0002] A question answering system is a computer program that can recognize natural language text sentences entered by users and make corresponding answers. Through the processing and analysis of user input sentences, it finally executes user requests and returns the information that users care about, including sentence segmentation, entity recognition, semantic recognition, task processing and response and other natural language processing related technologies. [0003] In the question answering system, the accuracy, timeliness, and surprise of answers are relatively important indicators, and the ultimate goal of improving these performances is to serve customers and satisfy customers. In order to better serve customer service, it is necess...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
IPC IPC(8): G06F17/27
Inventor 罗欢李杰汤鹏飞王智
Owner BEIJING JINGDONG SHANGKE INFORMATION TECH CO LTD
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com