An Empirical Study on Hybrid Recommender System with Implicit Feedback

Lee, Sunhwan; Chandra, Anca; Jadav, Divyesh

doi:10.1007/978-3-319-31753-3_41

Cited by 5 publications

(5 citation statements)

References 14 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%

“…Ex-periments conducted on the Ctrip dataset prove the effectiveness of the proposed framework by outperforming other latent factor recommendation models. Lee et al [20] proposed a hybrid recommender system for hotel recommendations. The proposed system combines term-frequency k-nearest neighbor, a content-based method, and a popularity measure, as well as utilizes implicit profiles of users and items to recommend hotels to users that they like to reserve.…”

Section: Related Workmentioning

confidence: 99%

“…From these studies, Zhang et al [33] proposed a novel hotel recommendation hybrid framework by combining latent factor models with a content-based method. 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.…”

Section: Introductionmentioning

confidence: 99%

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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

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“…In [11] the authors address the issue of over-information of users, as a result of an interconnected world through the IoT and propose a recommendation system. The proposed system is a kind of decision support system for the ordinary citizen, including the recommendation of hotels.…”

Section: Marketingmentioning

confidence: 99%

Internet of Things for the Hotel Industry: A Review

Magalhães¹,

Magalhães²,

Revett³

2017

Proceedings of the SmartCity360 2016

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The Internet of Things (IoT) represents an opportunity for the hotel industry to increase customer satisfaction while simultaneously reducing operational costs. This paper analyses the existing knowledge on this subject, through a review of the relevant publications indexed by Scopus and/or ISI Web of Science, concluding that, despite the existence of many relevant patents, registered in the past few years, the published research is very limited on this topic. The restriction to publication prior to the conclusion of the patent registration process may be a justification for this fact, and, if so, the near future will bring many novelties that will help the development of the hotel industry. It is also possible to conclude from this work that the potential of IoT is not yet well explored in the hotel industry, once authors frequently theorize on the use of IoT for applications that could easily be of interest for the hotel industry, but fail to identify that opportunity as a major market.

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“…Generally, recommendation methods are often based on either explicit feedback or implicit feedback. The former methods leverage users' ratings or reviews to predict users' potential interest [4,5,6,7,8], while the latter exploits users' actions (e.g., purchase, forward, view, like) to estimate user preference [9,10,11]. In real-world applications, since explicit feedback is not always available, the algorithms based on implicit feedback [12,13,14,15] have received much attention recently.…”

Section: Introductionmentioning

confidence: 99%