Applying Internet information technology combined with deep learning to tourism collaborative recommendation system.
Recently, more personalized travel methods have emerged in the tourism industry, such as individual travel and self-guided travel. The service models of traditional tourism limit the diversity of service options and cannot fully meet the individual needs of tourists anymore. The aim is to integrate...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2020-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0240656 |
| _version_ | 1828919610862206976 |
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| author | Meng Wang |
| author_facet | Meng Wang |
| author_sort | Meng Wang |
| collection | DOAJ |
| description | Recently, more personalized travel methods have emerged in the tourism industry, such as individual travel and self-guided travel. The service models of traditional tourism limit the diversity of service options and cannot fully meet the individual needs of tourists anymore. The aim is to integrate sparse tourism information on the Internet, thereby providing more convenient, faster, and more personalized tourism services. Based on the shortcomings of the traditional tourism recommendation system, a deep learning-based classification processing method of tourism product information is proposed. This method uses word embedding in the data preprocessing stage. The Convolutional Neural Network (CNN) is used to process review information of users and tourism service items. The Deep Neural Network (DNN) is used to process the necessary information of users and tourism service items. Also, factorization machine technology is used to learn the interaction between the extracted features to improve the prediction model. The results show that the proposed model can maintain an excellent precision of 64.2% when generating personalized recommendation lists for users. The sensitivity and accuracy of the recommendation list are better than other algorithms. By adding DNN, the word embedding method, and the factorization machine model, the precision is improved by 30%, 33.3%, and 40%, respectively. The model accuracy is the highest with 40 hidden factors, 100 convolutions, and a 100+50 combination hidden layer. Compared with traditional methods, the proposed algorithm can provide users with personalized travel products more accurately in personalized travel recommendations. The results have enriched and developed the theory of tourism service supply chain, providing a reference for constructing a personalized tourism service system. |
| first_indexed | 2024-12-13T21:31:16Z |
| format | Article |
| id | doaj.art-775db97c22cd41679b6ec8ca32acc164 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-13T21:31:16Z |
| publishDate | 2020-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-775db97c22cd41679b6ec8ca32acc1642022-12-21T23:30:49ZengPublic Library of Science (PLoS)PLoS ONE1932-62032020-01-011512e024065610.1371/journal.pone.0240656Applying Internet information technology combined with deep learning to tourism collaborative recommendation system.Meng WangRecently, more personalized travel methods have emerged in the tourism industry, such as individual travel and self-guided travel. The service models of traditional tourism limit the diversity of service options and cannot fully meet the individual needs of tourists anymore. The aim is to integrate sparse tourism information on the Internet, thereby providing more convenient, faster, and more personalized tourism services. Based on the shortcomings of the traditional tourism recommendation system, a deep learning-based classification processing method of tourism product information is proposed. This method uses word embedding in the data preprocessing stage. The Convolutional Neural Network (CNN) is used to process review information of users and tourism service items. The Deep Neural Network (DNN) is used to process the necessary information of users and tourism service items. Also, factorization machine technology is used to learn the interaction between the extracted features to improve the prediction model. The results show that the proposed model can maintain an excellent precision of 64.2% when generating personalized recommendation lists for users. The sensitivity and accuracy of the recommendation list are better than other algorithms. By adding DNN, the word embedding method, and the factorization machine model, the precision is improved by 30%, 33.3%, and 40%, respectively. The model accuracy is the highest with 40 hidden factors, 100 convolutions, and a 100+50 combination hidden layer. Compared with traditional methods, the proposed algorithm can provide users with personalized travel products more accurately in personalized travel recommendations. The results have enriched and developed the theory of tourism service supply chain, providing a reference for constructing a personalized tourism service system.https://doi.org/10.1371/journal.pone.0240656 |
| spellingShingle | Meng Wang Applying Internet information technology combined with deep learning to tourism collaborative recommendation system. PLoS ONE |
| title | Applying Internet information technology combined with deep learning to tourism collaborative recommendation system. |
| title_full | Applying Internet information technology combined with deep learning to tourism collaborative recommendation system. |
| title_fullStr | Applying Internet information technology combined with deep learning to tourism collaborative recommendation system. |
| title_full_unstemmed | Applying Internet information technology combined with deep learning to tourism collaborative recommendation system. |
| title_short | Applying Internet information technology combined with deep learning to tourism collaborative recommendation system. |
| title_sort | applying internet information technology combined with deep learning to tourism collaborative recommendation system |
| url | https://doi.org/10.1371/journal.pone.0240656 |
| work_keys_str_mv | AT mengwang applyinginternetinformationtechnologycombinedwithdeeplearningtotourismcollaborativerecommendationsystem |