A hybrid ensemble learning method for tourist route recommendations based on geo-tagged social networks

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.

KEYWORDS:

• Tour recommendations
• spatial data mining
• volunteered geographic information
• location-based social networks
• ensemble learning method

Acknowledgments

We would like to thank the editor Dr Shawn Laffan and the anonymous reviewers for their valuable comments and suggestions on the paper.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was funded by the National Natural Science Foundation of China [41401449], [41771425], [41501162], [41625003]; National Key Research and Development Program of China [2017YFB0503602], and Beijing Philosophy and Social Science Foundation [17JDGLB002].