Estimating point-of-interest rating based on visitors geospatial behaviour.

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 real-word 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.