Collaborative Filtering and Regression Techniques based location Travel Recommender System based on social media reviews data due to the COVID-19 Pandemic

Sagar, K. V. Daya; Arunasri, P. S. G.; Sakamuri, Sridevi; Kavitha, J; Kamesh, D. B. K.

doi:10.1088/1757-899x/981/2/022009

Cited by 6 publications

(3 citation statements)

References 10 publications

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“…Consider case studies of the quality of recommender algorithms in non-stationary conditions during the COVID-19 period. Daya Sagar et al (2020) examine the hospitality and tourism sector, analyze reviews and provide solutions to increase user satisfaction using collaborative filtering and regression techniques. Nilashi et al (2021) show how information from social networks and online reviews can help make decisions for travelers during COVID-19.…”

Section: Related Workmentioning

confidence: 99%

Dynamic recommendation algorithms for a COVID-19 restrictions scenario in the restaurant industry

Glukhov,

Derevitskii,

Severiukhina

et al. 2023

JHTT

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Purpose Using the data set about the restaurants from different countries and their customer's feedback, the purpose of this paper is to address the following issues: in the restaurant industry, how have user behavior and preferences changed during the COVID-19 restrictions period, how did these changes influence the performance of recommendation algorithms and which methods can be proposed to improve the quality of restaurant recommendations in a lockdown scenario. Design/methodology/approach To assess changes in user behavior and preferences, quantitative and qualitative data analysis was performed to assess the changes in user behavior and preferences. The authors compared the situation before and during the COVID-19 restrictions period. To evaluate the performance of restaurant recommendation systems in a non-stationary setting, the authors tested state-of-the-art collaborative filtering algorithms. This study proposes and investigates a filtering-based approach to improve the quality of recommendation algorithms for a lockdown scenario. Findings This study revealed that during the COVID-19 restrictions period, the average rating values and the number of reviews have changed. The experimental study confirmed that: the performance of all state-of-the-art recommender systems for the restaurant industry has significantly degraded during the COVID-19 restrictions period; and the accuracy and the stability of restaurant recommendations in non-stationary settings may be improved using the sliding window and post-filtering methods. Practical implications The authors propose two novel methods: the sliding window and closed restaurants post-filtering method based on the CatBoost classification model. These methods can be applied to classical collaborative recommender algorithms and increase the value of metrics under non-stationary conditions. These methods can be helpful for developers of recommender systems and massive aggregators of restaurants and hotels. Thus, it benefits both the app end-user and business owners because users honestly rate restaurants when they receive good recommendations and do not downgrade because of external factors. Originality/value To the best of the authors’ knowledge, this paper provides the first extensive and multifaceted experimental study of the impact of COVID-19 restrictions on the effectiveness of restaurant recommendation systems in different countries. Two novel methods to tackle restaurant recommendations' performance degradation are proposed and validated.

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Section: Related Workmentioning

confidence: 99%

Dynamic recommendation algorithms for a COVID-19 restrictions scenario in the restaurant industry

Glukhov,

Derevitskii,

Severiukhina

et al. 2023

JHTT

View full text Add to dashboard Cite

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“…The final topic discussion is about Recommender Systems according to the selected papers: [7], [41], [42], [43], and [44]. In [7], it is offered a travel recommender system for the effects of automating Word-of-Mouth (WOM) and established personalized travel-planning services to tourists through Collaborative Filtering (CF)-based recommender using WOM communication.…”

Section: Recommender Systemsmentioning

confidence: 99%

“…The author in [41] provide a framework of a travel recommender system by combining knowledgebased filtering and hybrid recommendation methods with decision-making theory in China in 2020. [42] developed a hotel recommender system based on collaborative filtering and regression. Whereas Roy and Dietz in [43] develop a travel recommender system based on the content-based recommender system method.…”

Section: Recommender Systemsmentioning

confidence: 99%

Smart Tourism Recommendation Model: A Systematic Literature Review

Huda¹,

Ramadhan²,

Trisetyarso³

et al. 2021

IJACSA

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The tourism industry has become a potential sector to leverage economic growth. Many attractions are detected on several platforms. Machine learning and data mining are some potential technologies to improve the service of tourism by providing recommendations for a specific attraction for tourists according to their location and profile. This research applied for a systematic literature review on tourism, digital tourism, smart tourism, and recommender system in tourism. This research aims to evaluate the most relevant and accurate techniques in tourism that focused on recommendations or similar efforts. Several research questions were defined and translated into search strings. The result of this research was promoting 41 research that discussed tourism, digital tourism, smart tourism, and recommender systems. All of the literature was reviewed on some aspects, in example the problem addressed, methodology used, data used, strength, and the limitation that can be an opportunity for improvement in future research. This study proposed some references for further study based on reviewed papers regarding tourism management, tourist experience, tourist motivation, and tourist recommendation system. The opportunities for a further research study can be conducted with more data usage especially for a smart recommender system in tourism through many types of recommendation techniques such as content-based, collaborative filtering, demographic, knowledge-based, community-based, and hybrid recommender systems.

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Smart Tourism Recommender System Modeling Based on Hybrid Technique and Content Boosted Collaborative Filtering

Huda,

Heryadi,

Lukas

et al. 2024

IEEE Access

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Collaborative Filtering and Regression Techniques based location Travel Recommender System based on social media reviews data due to the COVID-19 Pandemic

Cited by 6 publications

References 10 publications

Dynamic recommendation algorithms for a COVID-19 restrictions scenario in the restaurant industry

Dynamic recommendation algorithms for a COVID-19 restrictions scenario in the restaurant industry

Smart Tourism Recommendation Model: A Systematic Literature Review

Smart Tourism Recommender System Modeling Based on Hybrid Technique and Content Boosted Collaborative Filtering

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