Multi-situational data and cost-sensitive integrated model-based place personalized semantic identification method

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 popularization of smart devices and the development of mobile Internet, more and more location-based services have brought great convenience to life. Above "location" is another concept with a higher level of context and more expressive power, namely "place". In addition to basic geographic location information, places often have semantics, usually in the form of labels, such as home, company, restaurant, etc. Place semantics is a user-centric representation of location that can make location-based services smarter. For example, a reminder service based on place semantics can associate to-do items with places of a particular semantics. Therefore, place semantic recognition has broad application space in the fields o...

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

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