Geodesic
Estimating point-of-interest rating based on visitors geospatial behaviour
Computer Science and Information Systems, Tome 16 (2019) no. 1 Cet article a éte moissonné depuis la source Computer Science and Information Systems website

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Rating of different services, products and experiences plays an important role in our digitally assisted day-to-day life. It helps us make decisions when we are indecisive, uninformed or inexperienced. Traditionally, ratings depend on the willingness of existing customers to provide them. This often leads to biased (due to the insufficient number of votes) or nonexistent ratings. This was the motivation for our research, which aims to provide automatic star rating. The paper presents an approach to extracting points-of-interest from various sources and a novel approach to estimating point-of-interest ratings, based on geospatial data of their visitors. Our research is applied to campsite dataset where the community is still developing and more than thirty percent of camps are unrated. Our study use case addresses a realword problem of motorhome users visiting campsites in European countries. The dataset includes GPS traces from 10 motorhomes that were collected over a period of 2 years. To estimate star ratings of points-of-interest we applied machine learning methods including support vector machine, linear regression, random forest and decision trees. Our experimental results show that the duration of visit, which is a crucial part of the proposed approach, is an indicative feature for predicting camp ratings.
Keywords: machine learning, geospatial analysis 
@article{CSIS_2019_16_1_a7,
     author = {Matej Seno\v{z}etnik and Luka Brade\v{s}ko and Tine \v{S}ubic and Zala Herga and Jasna Urban\v{c}i\v{c} and Primo\v{z} \v{S}kraba and Dunja Mladeni\'c},
     title = {Estimating point-of-interest rating based on visitors geospatial behaviour},
     journal = {Computer Science and Information Systems},
     year = {2019},
     volume = {16},
     number = {1},
     url = {http://geodesic.mathdoc.fr/item/CSIS_2019_16_1_a7/}
}
TY  - JOUR
AU  - Matej Senožetnik
AU  - Luka Bradeško
AU  - Tine Šubic
AU  - Zala Herga
AU  - Jasna Urbančič
AU  - Primož Škraba
AU  - Dunja Mladenić
TI  - Estimating point-of-interest rating based on visitors geospatial behaviour
JO  - Computer Science and Information Systems
PY  - 2019
VL  - 16
IS  - 1
UR  - http://geodesic.mathdoc.fr/item/CSIS_2019_16_1_a7/
ID  - CSIS_2019_16_1_a7
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%0 Journal Article
%A Matej Senožetnik
%A Luka Bradeško
%A Tine Šubic
%A Zala Herga
%A Jasna Urbančič
%A Primož Škraba
%A Dunja Mladenić
%T Estimating point-of-interest rating based on visitors geospatial behaviour
%J Computer Science and Information Systems
%D 2019
%V 16
%N 1
%U http://geodesic.mathdoc.fr/item/CSIS_2019_16_1_a7/
%F CSIS_2019_16_1_a7
Matej Senožetnik; Luka Bradeško; Tine Šubic; Zala Herga; Jasna Urbančič; Primož Škraba; Dunja Mladenić. Estimating point-of-interest rating based on visitors geospatial behaviour. Computer Science and Information Systems, Tome 16 (2019) no. 1. http://geodesic.mathdoc.fr/item/CSIS_2019_16_1_a7/