La Trobe

File(s) stored somewhere else

Please note: Linked content is NOT stored on La Trobe and we can't guarantee its availability, quality, security or accept any liability.

An Effective Hotel Recommendation System through Processing Heterogeneous Data

journal contribution
posted on 13.08.2021, 01:52 by Md Shafiul Alam Forhad, Mohammad Shamsul Arefin, A S M KayesA S M Kayes, Khandakar Ahmed, Mohammad Jabed Morshed Chowdhury, Indika Kumara
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.

History

Publication Date

10/08/2021

Journal

Electronics

Volume

10

Issue

16

Pagination

(p. 1920-1920)

Publisher

MDPI AG

Rights Statement

The Author reserves all moral rights over the deposited text and must be credited if any re-use occurs. Documents deposited in OPAL are the Open Access versions of outputs published elsewhere. Changes resulting from the publishing process may therefore not be reflected in this document. The final published version may be obtained via the publisher’s DOI. Please note that additional copyright and access restrictions may apply to the published version.

Usage metrics

Categories

Licence

Exports