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

A Place Personalized Semantic Recognition Method Based on Multi-Context Data and Cost-sensitive Integrated Model

A cost-sensitive, semantic recognition technology, applied in the field of place semantic recognition, which can solve problems such as poor model performance, failure to consider high context level place features, and insufficient training data.

Active Publication Date: 2020-06-05
ZHEJIANG HONGCHENG COMP SYST
View PDF2 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, none of the existing methods take into account the high contextual level of place characteristics
In addition, due to the semantic similarity of different types of places, the cost loss caused by different misidentifications varies, but existing methods rarely consider this indicator when evaluating model performance.
It is expensive for users to label place semantics, so the method of place personalized semantic recognition generally has the problem of insufficient training data leading to poor model performance

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
  • A Place Personalized Semantic Recognition Method Based on Multi-Context Data and Cost-sensitive Integrated Model
  • A Place Personalized Semantic Recognition Method Based on Multi-Context Data and Cost-sensitive Integrated Model
  • A Place Personalized Semantic Recognition Method Based on Multi-Context Data and Cost-sensitive Integrated Model

Examples

Experimental program
Comparison scheme
Effect test

Embodiment

[0056] Example: such as figure 1 As shown, a place-based personalized semantic recognition method based on multi-context data and cost-sensitive integrated model, the method is divided into three stages: preprocessing, model training and semantic recognition, the specific steps are as follows:

[0057] The preprocessing stage realizes the functions of data preprocessing, feature extraction and cost matrix construction, which can be mainly divided into two parts: multi-context feature extraction and cost matrix construction:

[0058] The specific steps of multi-context feature extraction are as follows:

[0059] Step 1. All the access records v of the user in the same place form the visit record set V of the place, and V is regarded as a place in the identification.

[0060] Each access record can be expressed as v=(t in , t out , data), where t in and t out They are the start time and end time of the site visit respectively, and data is a collection of multi-context data....

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 relates to a multi-situational data and cost-sensitive integrated model-based place personalized semantic identification method. The method is specifically implemented by the following steps of 1) extracting effective features from various situational data of use logs of a smart phone, discovering user activities in acceleration data through clustering, and establishing user activity features of high-situational-level places; 2) according to activity distribution of the places, calculating semantic similarity of the places to obtain a cost matrix; 3) performing modeling on the features of the places in combination with the cost matrix, and introducing label-free place data for performing semi-supervised learning to obtain a plurality of cost-sensitive base classifiers; and 4) integrating the base classifiers to output an identification model, and performing personalized semantic identification on the places accessed by users. According to the method, the personalized semantic identification of the places is performed in combination with situational perception, cost-sensitive learning and semi-supervised learning; and the method has a wide application prospect in the fields of pervasive computing, location-based services and the like.

Description

technical field [0001] The invention relates to the field of place semantic recognition, in particular to a place personalized semantic recognition method based on multi-context data and a cost-sensitive integrated model. Background technique [0002] With the popularity of smart devices and the development of the mobile Internet, more and more location-based services have brought great convenience to life. Above "location" lies another concept with a higher level of situation and stronger expressive power, namely "place". In addition to basic geographic location information, places often have semantics, usually in the form of tags, such as home, company, restaurant, etc. Place semantics is a user-centric representation of location that enables smarter location-based services. For example, a reminder service based on place semantics can associate to-do items with places with specific semantics. Therefore, place semantic recognition has broad application space in fields su...

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
Patent Type & Authority Patents(China)
IPC IPC(8): G06F40/30
CPCG06F40/30
Inventor 王敬昌陈岭吴晓杰张圣
Owner ZHEJIANG HONGCHENG COMP SYST
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