• Information Systems Review
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• v.23 no.3
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• pp.51-75
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• 2021

Global e-commerce websites offer personalized recommendation services to gain sustainable competitiveness. Existing studies have offered personalized recommendation services using quantitative preferences such as ratings. However, offering personalized recommendation services using only quantitative data has raised the problem of decreasing recommendation performance. For example, a user gave a five-star rating but wrote a review that the user was unsatisfied with hotel service and cleanliness. In such cases, has problems where quantitative and qualitative preferences are inconsistent. Recently, a growing number of studies have considered review data simultaneously to improve the limitations of existing personalized recommendation service studies. Therefore, in this study, we identify review and rating mismatches and build a new user profile to offer personalized recommendation services. To this end, we use deep learning algorithms such as CNN, LSTM, CNN + LSTM, which have been widely used in sentiment analysis studies. And extract sentiment features from reviews and compare with quantitative preferences. To evaluate the performance of the proposed methodology in this study, we collect user preference information using real-world hotel data from the world's largest travel platform TripAdvisor. Experiments show that the proposed methodology in this study outperforms the existing other methodologies, using only existing quantitative preferences.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.21-41
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• 2019

Thanks to the rapid development of information technologies, the data available on Internet have grown rapidly. In this era of big data, many studies have attempted to offer insights and express the effects of data analysis. In the tourism and hospitality industry, many firms and studies in the era of big data have paid attention to online reviews on social media because of their large influence over customers. As tourism is an information-intensive industry, the effect of these information networks on social media platforms is more remarkable compared to any other types of media. However, there are some limitations to the improvements in service quality that can be made based on opinions on social media platforms. Users on social media platforms represent their opinions as text, images, and so on. Raw data sets from these reviews are unstructured. Moreover, these data sets are too big to extract new information and hidden knowledge by human competences. To use them for business intelligence and analytics applications, proper big data techniques like Natural Language Processing and data mining techniques are needed. This study suggests an analytical approach to directly yield insights from these reviews to improve the service quality of hotels. Our proposed approach consists of topic mining to extract topics contained in the reviews and the decision tree modeling to explain the relationship between topics and ratings. Topic mining refers to a method for finding a group of words from a collection of documents that represents a document. Among several topic mining methods, we adopted the Latent Dirichlet Allocation algorithm, which is considered as the most universal algorithm. However, LDA is not enough to find insights that can improve service quality because it cannot find the relationship between topics and ratings. To overcome this limitation, we also use the Classification and Regression Tree method, which is a kind of decision tree technique. Through the CART method, we can find what topics are related to positive or negative ratings of a hotel and visualize the results. Therefore, this study aims to investigate the representation of an analytical approach for the improvement of hotel service quality from unstructured review data sets. Through experiments for four hotels in Hong Kong, we can find the strengths and weaknesses of services for each hotel and suggest improvements to aid in customer satisfaction. Especially from positive reviews, we find what these hotels should maintain for service quality. For example, compared with the other hotels, a hotel has a good location and room condition which are extracted from positive reviews for it. In contrast, we also find what they should modify in their services from negative reviews. For example, a hotel should improve room condition related to soundproof. These results mean that our approach is useful in finding some insights for the service quality of hotels. That is, from the enormous size of review data, our approach can provide practical suggestions for hotel managers to improve their service quality. In the past, studies for improving service quality relied on surveys or interviews of customers. However, these methods are often costly and time consuming and the results may be biased by biased sampling or untrustworthy answers. The proposed approach directly obtains honest feedback from customers' online reviews and draws some insights through a type of big data analysis. So it will be a more useful tool to overcome the limitations of surveys or interviews. Moreover, our approach easily obtains the service quality information of other hotels or services in the tourism industry because it needs only open online reviews and ratings as input data. Furthermore, the performance of our approach will be better if other structured and unstructured data sources are added.

• Journal of Korea Entertainment Industry Association
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• v.14 no.7
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• pp.161-173
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• 2020

In-depth interview in the field of hospitality industry services was conducted in COVID- 19. Introduction of kiosks for non-face-to-face services using untact technology, reservation, pay systems, self-service, service improvement using room service should be carried out. It is also necessary to implement Instagram, Facebook, YouTube, P-blogs, online broadcasting and live commerce through the establishment of m-channel system through untact marketing sales channels in the hospitality industry now that the product composition to solve the pro -blem of untact marketing is drawing attention due to diversification of online sales channe -ls. Now, the recognition of important elements of service education and a establishment of differentiated system of untact marketing, expansion of untact sale channel, implementation of non-face-to-face counseling service and introduction of pre-booking, telecommuting were recognized as urgent parts. In particular, a service differentiation and importance of human services, which were recognized free of charge, have re-recognized as premium, and quality service aspect of the hospitality industry in untact and the direction to diversify marketing channels are presented.

• Information Systems Review
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• v.18 no.3
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• pp.51-71
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• 2016

Tourism products are intangible goods. Given this nature, tourist experience should be recorded and visualized through media, such as pictures, videos, and souvenir. Online platforms played the role of media given the growth of information and communication technology. Tourists post their travels for real-time documentation of their experiences, but they also tend to reminisce about past experiences that they posted on social media. Social media is not only a channel of self-presentation or a means of communication with other people, but it also serves as an archive of electronic records to bring back memories. Given this finding, we investigated the impact of social media on the autobiographical memory (recollection and vividness) of tourists and their intention to revisit a certain destination. The results showed social media interface and the impact of display quality on the recollection and vivid memory. The predictor of memory recollection of tourists is intention to revisit a destination. Social media is considered an archive of travel memory that indulges people to reminisce. Theoretical and practical implications were provided based on these results.