Tourism Information Push System Based on Convolutional Neural Network
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 learni...
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Published in | E3S web of conferences Vol. 53; p. 3048 |
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Main Authors | , , , , |
Format | Journal Article |
Language | English |
Published |
EDP Sciences
01.01.2018
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Abstract | 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. |
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AbstractList | 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. |
Author | Li, Jingwen Han, Xiaoqiang Yuan, Jiao Hu, Yao Ye, Suxian |
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