A hybrid ensemble learning method for tourist route recommendations based on geo-tagged social networks. Issue 11 (2nd November 2018)

• Record Type: Journal Article
• Authors: Wan, Lin; Hong, Yuming; Huang, Zhou; Peng, Xia; Li, Ran
• Is Part Of: International journal of geographical information science. Volume 32:Issue 11(2018)
• Page Start: 2225
• Page End: 2246
• Publication Date: 2018-11-02
• Subjects: Tour recommendations -- spatial data mining -- volunteered geographic information -- location-based social networks -- ensemble learning method; Geography -- Data processing -- Periodicals; Information storage and retrieval systems -- Periodicals; Géomatique -- Périodiques; Systèmes d'information -- Périodiques; 910.285
• DOI: 10.1080/13658816.2018.1458988
• Languages: English
• ISSNs: 1365-8816

Abstract

ABSTRACT: Geo-tagged travel photos on social networks often contain location data such as points of interest (POIs), and also users' travel preferences. In this paper, we propose a hybrid ensemble learning method, BAyes-Knn, that predicts personalized tourist routes for travelers by mining their geographical preferences from these location-tagged data. Our method trains two types of base classifiers to jointly predict the next travel destination: (1) The K-nearest neighbor (KNN) classifier quantifies users' location history, weather condition, temperature and seasonality and uses a feature-weighted distance model to predict a user's personalized interests in an unvisited location. (2) A Bayes classifier introduces a smooth kernel function to estimate a-priori probabilities of features and then combines these probabilities to predict a user's latent interests in a location. All the outcomes from these subclassifiers are merged into one final prediction result by using the Borda count voting method. We evaluated our method on geo-tagged Flickr photos and Beijing weather data collected from 1 January 2005 to 1 July 2016. The results demonstrated that our ensemble approach outperformed 12 other baseline models. In addition, the results showed that our framework has better prediction accuracy than do context-aware significant travel-sequence-patterns recommendations and frequent travel-sequence patterns.