A Personalized Travel Route Recommendation Model Using Deep Learning in Scenic Spots Intelligent Service Robots

This paper proposes a personalized tourist interest demand recommendation model based on deep neural network. Firstly, the basic information data and comment text data of tourism service items are obtained by crawling the relevant website data. Furthermore, word segmentation and word vector transfor...

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Main Author: Qili Tang
Format: Article
Language:English
Published: Wiley 2022-01-01
Series:Journal of Robotics
Online Access:http://dx.doi.org/10.1155/2022/3851506
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author Qili Tang
author_facet Qili Tang
author_sort Qili Tang
collection DOAJ
description This paper proposes a personalized tourist interest demand recommendation model based on deep neural network. Firstly, the basic information data and comment text data of tourism service items are obtained by crawling the relevant website data. Furthermore, word segmentation and word vector transformation are carried out through Jieba word segmentation tool and Skip-gram model, the semantic information between different data is deeply characterized, and the problem of very high vector sparsity is solved. Then, the corresponding features are obtained by using the feature extraction ability of DNN’s in-depth learning. On this basis, the user’s score on tourism service items is predicted through the model until a personalized recommendation list is generated. Finally, through simulation experiments, the recommendation accuracy and average reciprocal ranking of the proposed algorithm model and the other two algorithms in three different databases are compared and analyzed. The results show that the overall performance of the proposed algorithm is better than the other two comparison algorithms.
format Article
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institution Kabale University
issn 1687-9619
language English
publishDate 2022-01-01
publisher Wiley
record_format Article
series Journal of Robotics
spelling doaj-art-7980c4a918da42f1970203aba96e24632025-02-03T01:22:25ZengWileyJournal of Robotics1687-96192022-01-01202210.1155/2022/3851506A Personalized Travel Route Recommendation Model Using Deep Learning in Scenic Spots Intelligent Service RobotsQili Tang0School of Economics and ManagementThis paper proposes a personalized tourist interest demand recommendation model based on deep neural network. Firstly, the basic information data and comment text data of tourism service items are obtained by crawling the relevant website data. Furthermore, word segmentation and word vector transformation are carried out through Jieba word segmentation tool and Skip-gram model, the semantic information between different data is deeply characterized, and the problem of very high vector sparsity is solved. Then, the corresponding features are obtained by using the feature extraction ability of DNN’s in-depth learning. On this basis, the user’s score on tourism service items is predicted through the model until a personalized recommendation list is generated. Finally, through simulation experiments, the recommendation accuracy and average reciprocal ranking of the proposed algorithm model and the other two algorithms in three different databases are compared and analyzed. The results show that the overall performance of the proposed algorithm is better than the other two comparison algorithms.http://dx.doi.org/10.1155/2022/3851506
spellingShingle Qili Tang
A Personalized Travel Route Recommendation Model Using Deep Learning in Scenic Spots Intelligent Service Robots
Journal of Robotics
title A Personalized Travel Route Recommendation Model Using Deep Learning in Scenic Spots Intelligent Service Robots
title_full A Personalized Travel Route Recommendation Model Using Deep Learning in Scenic Spots Intelligent Service Robots
title_fullStr A Personalized Travel Route Recommendation Model Using Deep Learning in Scenic Spots Intelligent Service Robots
title_full_unstemmed A Personalized Travel Route Recommendation Model Using Deep Learning in Scenic Spots Intelligent Service Robots
title_short A Personalized Travel Route Recommendation Model Using Deep Learning in Scenic Spots Intelligent Service Robots
title_sort personalized travel route recommendation model using deep learning in scenic spots intelligent service robots
url http://dx.doi.org/10.1155/2022/3851506
work_keys_str_mv AT qilitang apersonalizedtravelrouterecommendationmodelusingdeeplearninginscenicspotsintelligentservicerobots
AT qilitang personalizedtravelrouterecommendationmodelusingdeeplearninginscenicspotsintelligentservicerobots