Preference learning for eco-friendly hotels recommendation: A multi-criteria collaborative filtering approach

Nilashi, Mehrbakhsh; Ahani, Ali; Esfahani, Mohammad Dalvi; Yadegaridehkordi, Elaheh; Samad, Sarminah; Ibrahim, Othman; Sharef, Nurfadhlina Mohd; Akbari, Elnaz

doi:10.1016/j.jclepro.2019.01.012

Cited by 109 publications

(69 citation statements)

References 71 publications

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“…These results contribute significantly to our understanding of social and consumer-generated media's effect on the management of tourism in overtouristed cities and destinations. Although digital technologies are expected to improve tourists' access to data and information on local destinations (Munar & Jacobsen, 2014;Nilashi et al, 2019;Peeters et al, 2019;UNWTO, 2018;Warren et al, 2018), numbers of likes and positive reviews in already-overtouristed cities or specific sites attract even more excursionists and foreign tourists (Oklevik et al, 2019). The power to influence these signals is greater for those who are less acquainted with a tourist destination, so these systems will steer a growing number of excursionists and foreigners toward tourist restaurants instead of restaurants that offer an authentic, local food experience, potentially eliciting negative effects on the overall local food chain's quality and development.…”

Section: Resultsmentioning

confidence: 99%

The influence of foreigners’ buzzing on TripAdvisor ranking of restaurants in Venice: implications for the sustainability of over-touristed heritage cities

Ganzaroli

Noni

Bonera

2020

Current Issues in Tourism

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Local and global coexist in any tourist destination, and the challenge of tourism sustainability is what remains in the balance between these two dimensions. Social and consumer-generated media are changing how local and global are coming together in the local system. These media's effects are even more important in overtouristed places, where a significant imbalance between local and tourist development already exists. In this paper, the effects from foreigners' reviews in crowd-based ranking systems in overtouristed cities are investigated. Based on a case study of TripAdvisor's restaurant rankings in Venice, our findings suggest that foreigners' reviews systematically rate tourist experiences' quality higher than non-foreigners' reviews, and that their attitude significantly affects the ranking of these experiences. This discloses these systems' limited reliability in overtouristed cities when it comes to rank quality, as well as the potential risks deriving from their large and uncontrolled adoption. The study concludes that a governance system is needed that is more respectful and inclusive of these places' specific requirements.

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Section: Resultsmentioning

confidence: 99%

The influence of foreigners’ buzzing on TripAdvisor ranking of restaurants in Venice: implications for the sustainability of over-touristed heritage cities

Ganzaroli

Noni

Bonera

2020

Current Issues in Tourism

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“…In the CF technique, a forecast is achieved in three steps. The main step is estimating the values of similarity amongst common clients and the target customer with the use of similarity functions, such as the Cosine function (Nilashi et al, 2019;Alhijawi & Kilani, 2020). The rating scores supplied by the target client and the similarity values are applied in the next procedure to estimate the expected score of the product using the prediction function.…”

Section: Related Work Collaborative Filteringmentioning

confidence: 99%

“…A method based on ensemble divide and conquer (Al-Hadi et al, 2016) was adopted to solve the misplacement problem besides addressing the customers' preferences drift and popularity decay. Integration of temporal preferences with factorization methods to solve the sparsity issue has yielded a better performance compared to basic factorization approaches (Al-Hadi et al, 2017b;Li, Xu & Cao, 2016;Nilashi et al, 2019;Nilashi, bin Ibrahim & Ithnin, 2014). The temporal dynamics approach (Koren, 2009) separates the time period of preferences into static digit of bins and extracts a universal weight according to the stochastic gradient descent method to reduce overfitting.…”

Section: Introductionmentioning

confidence: 99%

Latent based temporal optimization approach for improving the performance of collaborative filtering

Al-Hadi

Sharef

Sulaiman

et al. 2020

PeerJ Computer Science

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Recommendation systems suggest peculiar products to customers based on their past ratings, preferences, and interests. These systems typically utilize collaborative filtering (CF) to analyze customers’ ratings for products within the rating matrix. CF suffers from the sparsity problem because a large number of rating grades are not accurately determined. Various prediction approaches have been used to solve this problem by learning its latent and temporal factors. A few other challenges such as latent feedback learning, customers’ drifting interests, overfitting, and the popularity decay of products over time have also been addressed. Existing works have typically deployed either short or long temporal representation for addressing the recommendation system issues. Although each effort improves on the accuracy of its respective benchmark, an integrative solution that could address all the problems without trading off its accuracy is needed. Thus, this paper presents a Latent-based Temporal Optimization (LTO) approach to improve the prediction accuracy of CF by learning the past attitudes of users and their interests over time. Experimental results show that the LTO approach efficiently improves the prediction accuracy of CF compared to the benchmark schemes.

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“…giglio, Bertacchini, Bilotta y Pantano (2019) realizan algo similar para seis ciudades de italia usando las redes sociales. Específicamente, para el caso de aplicaciones en los hoteles se encuentran los trabajos de Ku, Chang, Wang, Chen y Hsiao (2019), Nilashi et al, (2019), Shin, Du y Xiang (2019) y Hassan y Abdulwahhab (2019) que buscan predecir la recomendación de los visitantes a los diferentes hoteles.…”

Section: Algoritmos Utilizadosunclassified

La percepción de calidad de servicio como determinante de la recomendación: una predicción mediante inteligencia artificial para los hoteles en Cartagena

Ochoa¹,

Lis-Gutiérrez²,

Nieto³

et al. 2019

Diálogos Sobre Investigación: Avances Científicos Konrad Lorenz

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Preference learning for eco-friendly hotels recommendation: A multi-criteria collaborative filtering approach

Cited by 109 publications

References 71 publications

The influence of foreigners’ buzzing on TripAdvisor ranking of restaurants in Venice: implications for the sustainability of over-touristed heritage cities

The influence of foreigners’ buzzing on TripAdvisor ranking of restaurants in Venice: implications for the sustainability of over-touristed heritage cities

Latent based temporal optimization approach for improving the performance of collaborative filtering

La percepción de calidad de servicio como determinante de la recomendación: una predicción mediante inteligencia artificial para los hoteles en Cartagena

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