Research Article
Travel Destination Recommendation Based on Probabilistic Spatio-temporal Inference
366 downloads
@INPROCEEDINGS{10.1007/978-3-319-29236-6_10, author={Chang Choi and Junho Choi and Htet Lynn and Pankoo Kim}, title={Travel Destination Recommendation Based on Probabilistic Spatio-temporal Inference}, proceedings={Context-Aware Systems and Applications. 4th International Conference, ICCASA 2015, Vung Tau, Vietnam, November 26-27, 2015, Revised Selected Papers}, proceedings_a={ICCASA}, year={2016}, month={4}, keywords={Recommendation system Ontology Markov Logic Networks}, doi={10.1007/978-3-319-29236-6_10} }- Chang Choi
Junho Choi
Htet Lynn
Pankoo Kim
Year: 2016
Travel Destination Recommendation Based on Probabilistic Spatio-temporal Inference
ICCASA
Springer
DOI: 10.1007/978-3-319-29236-6_10
Abstract
Recently, a lot of users are increasing for searching travel information through smart devices such as, tourist attractions, accommodation, entertainment, local gourmet food and so on. A general method for recommendation system has a data sparseness and the first rate problem. This problem can be solved by ontology and inference rules. In this paper, we propose the travel destination recommendation using Markov Logic Networks based on probabilistic spatio-temporal inference. The most inference engines determine simply if there is a result from inference or not. However, probabilistic inference methods have emerged and classified problems that cannot be defined easily in the probabilistic way, which provides better results.
Copyright © 2015–2024 ICST
