MATEC Web Conf.
Volume 292, 201923rd International Conference on Circuits, Systems, Communications and Computers (CSCC 2019)
|Number of page(s)||5|
|Section||Circuits and Systems|
|Published online||24 September 2019|
Personalized Tour Recommender through Geotagged Photo Mining and LSTM Neural Networks
1 Department of Industrial Engineering and Management, Yuan Ze University, Taoyuan City, 32003, Taiwan, R.O.C.
2 Department of Applied Informatics, Fo Guang University, Yilan County 26247, Taiwan, R.O.C.
3 Key Laboratory of Advanced Manufacturing Technology, Ministry of Education, Guizhou University, Guiyang 550025, China
* Corresponding author: firstname.lastname@example.org
In this study, a tour recommendation system based on social media photos is proposed. The proposed recommendation system can generate trip tours considering both the user’s current location and interests. First, we exploited the geotagged photo dataset from social media websites, which includes photo related information such as user ID numbers, timestamps, hashtags, and GPS coordinates. With this information, the second step is to group photos and identify those places that could be considered relevant for travellers using clustering algorithms. The third step characterizes the resulting clusters by grouping them into different categories using latent dirichlet allocation (LDA) topic modelling approach. The last step is the generation of tours using a long-short term memory neural network (LSTM). The experiments show that the proposed system can be efficient to advise future travellers about the places they would be more likely to visit and arrange trips for them.
© The Authors, published by EDP Sciences, 2019
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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