E3S Web Conf.
Volume 53, 20182018 3rd International Conference on Advances in Energy and Environment Research (ICAEER 2018)
|Number of page(s)||4|
|Section||Environment Engineering, Environmental Safety and Detection|
|Published online||14 September 2018|
Tourism Information Push System Based on Convolutional Neural Network
Guilin University of Technology, Guilin,Guangxi, China
2 Guangxi Key Laboratory of Spatial Information and Geomatics, Guilin,Guangxi, China
* Corresponding author: firstname.lastname@example.org
In the context of the current hot tourism, personalized travel information push is the focus of major travel technology companies. For tourists, an intelligent and humane tourism push system has greatly improved tourism planning. this paper proposes a new tourism push system based on the deep learning technology of the recent hot convolutional neural network. It can satisfy the big data era by acquiring the user's image and text information for convolutional neural network analysis. The intelligent extraction of various network data and personal information and speculation of personal preferences, with a new way of self-learning to reform the current active statistics of the travel push system. The results show that the tourism information pushed by this method is ideal for satisfying the travel preferences of individual users, more humanized and intelligent, and has achieved good results.
© The Authors, published by EDP Sciences, 2018
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.