Open AccessHeterogeneous inter-Clue designing of POI Popularity Analysis with discrepancy Tourism Data
Author(s) -
Mahesh Akarapu,
G Sunil,
Koteshwar Rao Donthamala,
M. Mrutyunjaya,
D. Praveen
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/981/2/022033
Subject(s) - point of interest , computer science , tourism , variety (cybernetics) , popularity , construct (python library) , point (geometry) , data science , representation (politics) , information retrieval , economic shortage , data mining , artificial intelligence , geography , psychology , social psychology , linguistics , philosophy , geometry , mathematics , archaeology , government (linguistics) , politics , political science , law , programming language
The prevalence of Predicting Point of Interest (POI) has been extremely important to location-based applications, such as reviews on POIs. Many current approaches are rarely able to achieve adequate efficiency due to the shortage of POI knowledge. This tendentious restricts the advice to famous locations and lacks equally important qualities in unlikely attractions. This paper introduces a novel method to forecasting the performance of POIs, dubbed Hierarchical Multi-Clue Fusion (HMCF). In general, to address sparsity issues, it is proposed that POIs be defined in a simple way usage different method of User-Generated Content (UGC) By different origin. And there is construct a hierarchically powerful POI modeling framework that concurrently injects semantonal Awareness and multiple layer representation regulation of POIs. Users are building a multi-view POI database for assessment by compiling both text and visual information from four conventional tourism channels from many separate provinces in China during 2006 to 2017. Extensive experimental findings indicate that the new technique will substantially improve the output of forecasting the success of attractions relative to a variety of reference methodologies.