Location-Based Hotel Recommendation System

Chen, Chien-Liang; Wang, Ching-Sheng; Chiang, Ding-Jung

doi:10.1007/978-3-030-06158-6_23

Cited by 3 publications

(4 citation statements)

References 9 publications

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“…The studies can be classified into two classes: The first class includes the hotel recommendation systems that utilize numerical rating information. The second class comprises the hotel recommendation systems that utilize text review information [1,11,12,14,18,20,[33][34][35][36].…”

Section: Related Workmentioning

confidence: 99%

“…Experimental results verify the effectiveness of the proposed recommendation framework. Chen et al [11] proposed a hotel recommender system that uses item-based collaborative filtering and user location.…”

Section: Related Workmentioning

confidence: 99%

“…Lee et al [20] combine term-frequency k-nearest neighbor, a content-based method, and a popularity measure to recommend hotels to users that they would like to reserve. Chen et al [11] proposed a hotel recommender system that uses item-based CF and user location. However, due to the inherent sparsity problem in the hotel recommendation domain, using explicit rating information is not always practical [6,33].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Hotel Recommender System Based on Multi-Criteria Collaborative Filtering

Shambour

Abu-Shareha

Abualhaj

2022

ITC

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Recommendation systems have lately gained popularity in a variety of applications due to their ability to operate as information filters, thus delivering useful suggestions to users based on the processing of a variety of information from various sources. The tourism industry, on the other hand, is becoming increasingly popular, with significant growth in the usage of online services for hotel selection and reservation. Potential travelers may, however, find that using such online services is inconvenient and time-consuming. This paper aims to develop a novel fusion-based multi-criteria collaborative filtering model that provides more effective and personalized hotel recommendations. The proposed model enhances the prediction accuracy of hotel recommendations by the deployment of multi-criteria ratings that precisely express travelers’ complex preferences and addresses the insufficiency of rating information in the hotel domain by the exploitation of the users’ and items’ implicit similarity, users’ similarity propagation, and user/item reputation concepts. The experimental results demonstrate that the proposed model provides higher recommendation accuracy and coverage compared to other benchmark recommendation algorithms.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Hotel Recommender System Based on Multi-Criteria Collaborative Filtering

Shambour

Abu-Shareha

Abualhaj

2022

ITC

View full text Add to dashboard Cite

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“…The results confirmed that the presence of airports, public transport, attractions, universities, etc., are significant determinants. Chen et al [17] combined the conventional recommendation technology with location-based services to provide recommendations. They considered price, service, the location of the tourist, etc., to provide recommendations.…”

Section: Related Workmentioning

confidence: 99%

An Effective Hotel Recommendation System through Processing Heterogeneous Data

et al. 2021

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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.

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