An Effective Hotel Recommendation System through Processing Heterogeneous Data
Recommendation systems have recently gained a lot of popularity in various industries such as entertainment and tourism. They can act as filters of information by providing relevant suggestions to the users through processing heterogeneous data from different networks. Many travelers and tourists ro...
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MDPI AG
2021-08-01
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Series: | Electronics |
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Online Access: | https://www.mdpi.com/2079-9292/10/16/1920 |
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author | Md. Shafiul Alam Forhad Mohammad Shamsul Arefin A. S. M. Kayes Khandakar Ahmed Mohammad Jabed Morshed Chowdhury Indika Kumara |
author_facet | Md. Shafiul Alam Forhad Mohammad Shamsul Arefin A. S. M. Kayes Khandakar Ahmed Mohammad Jabed Morshed Chowdhury Indika Kumara |
author_sort | Md. Shafiul Alam Forhad |
collection | DOAJ |
description | Recommendation systems have recently gained a lot of popularity in various industries such as entertainment and tourism. They can act as filters of information by providing relevant suggestions to the users through processing heterogeneous data from different networks. Many travelers and tourists routinely rely on textual reviews, numerical ratings, and points of interest to select hotels in cities worldwide. To attract more customers, online hotel booking systems typically rank their hotels based on the recommendations from their customers. In this paper, we present a framework that can rank hotels by analyzing hotels’ customer reviews and nearby amenities. In addition, a framework is presented that combines the scores generated from user reviews and surrounding facilities. We perform experiments using datasets from online hotel booking platforms such as TripAdvisor and Booking to evaluate the effectiveness and applicability of the proposed framework. We first store the keywords extracted from reviews and assign weights to each considered unigram and bigram keywords and, then, we give a numerical score to each considered keyword. Finally, our proposed system aggregates the scores generated from the reviews and surrounding environments from different categories of the facilities. Experimental results confirm the effectiveness of the proposed recommendation framework. |
first_indexed | 2024-03-10T08:52:47Z |
format | Article |
id | doaj.art-94911ee3a006446894f5ecdbd224997c |
institution | Directory Open Access Journal |
issn | 2079-9292 |
language | English |
last_indexed | 2024-03-10T08:52:47Z |
publishDate | 2021-08-01 |
publisher | MDPI AG |
record_format | Article |
series | Electronics |
spelling | doaj.art-94911ee3a006446894f5ecdbd224997c2023-11-22T07:24:33ZengMDPI AGElectronics2079-92922021-08-011016192010.3390/electronics10161920An Effective Hotel Recommendation System through Processing Heterogeneous DataMd. Shafiul Alam Forhad0Mohammad Shamsul Arefin1A. S. M. Kayes2Khandakar Ahmed3Mohammad Jabed Morshed Chowdhury4Indika Kumara5Department of Computer Science and Engineering, Chittagong University of Engineering and Technology, Chattogram 4349, BangladeshDepartment of Computer Science and Engineering, Chittagong University of Engineering and Technology, Chattogram 4349, BangladeshDepartment of Computer Science and Information Technology, La Trobe University, Plenty Road, Bundoora, VIC 3086, AustraliaCollege of Engineering and Science, Victoria University, 70/104 Ballarat Road, Footscray, VIC 3011, AustraliaDepartment of Computer Science and Information Technology, La Trobe University, Plenty Road, Bundoora, VIC 3086, AustraliaJheronimus Academy of Data Science, Tilburg University, Warandelaan 2, 5037 AB Tilburg, The NetherlandsRecommendation systems have recently gained a lot of popularity in various industries such as entertainment and tourism. They can act as filters of information by providing relevant suggestions to the users through processing heterogeneous data from different networks. Many travelers and tourists routinely rely on textual reviews, numerical ratings, and points of interest to select hotels in cities worldwide. To attract more customers, online hotel booking systems typically rank their hotels based on the recommendations from their customers. In this paper, we present a framework that can rank hotels by analyzing hotels’ customer reviews and nearby amenities. In addition, a framework is presented that combines the scores generated from user reviews and surrounding facilities. We perform experiments using datasets from online hotel booking platforms such as TripAdvisor and Booking to evaluate the effectiveness and applicability of the proposed framework. We first store the keywords extracted from reviews and assign weights to each considered unigram and bigram keywords and, then, we give a numerical score to each considered keyword. Finally, our proposed system aggregates the scores generated from the reviews and surrounding environments from different categories of the facilities. Experimental results confirm the effectiveness of the proposed recommendation framework.https://www.mdpi.com/2079-9292/10/16/1920automated recommendationhotel booking systemheterogeneous network datadata processingpoints of interestreview analysis |
spellingShingle | Md. Shafiul Alam Forhad Mohammad Shamsul Arefin A. S. M. Kayes Khandakar Ahmed Mohammad Jabed Morshed Chowdhury Indika Kumara An Effective Hotel Recommendation System through Processing Heterogeneous Data Electronics automated recommendation hotel booking system heterogeneous network data data processing points of interest review analysis |
title | An Effective Hotel Recommendation System through Processing Heterogeneous Data |
title_full | An Effective Hotel Recommendation System through Processing Heterogeneous Data |
title_fullStr | An Effective Hotel Recommendation System through Processing Heterogeneous Data |
title_full_unstemmed | An Effective Hotel Recommendation System through Processing Heterogeneous Data |
title_short | An Effective Hotel Recommendation System through Processing Heterogeneous Data |
title_sort | effective hotel recommendation system through processing heterogeneous data |
topic | automated recommendation hotel booking system heterogeneous network data data processing points of interest review analysis |
url | https://www.mdpi.com/2079-9292/10/16/1920 |
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