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.
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Acknowledgment
This work was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (No. 2013R1A1A2A10011667) and Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (2015R1C1A1A02037515)
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© 2016 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Choi, C., Choi, J., Lynn, H.M., Kim, P. (2016). Travel Destination Recommendation Based on Probabilistic Spatio-temporal Inference. In: Vinh, P., Alagar, V. (eds) Context-Aware Systems and Applications. ICCASA 2015. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 165. Springer, Cham. https://doi.org/10.1007/978-3-319-29236-6_10
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DOI: https://doi.org/10.1007/978-3-319-29236-6_10
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