Sigeon Yang , Qinglong Li , Dongsoo Jang , Jaekyeong Kim
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Deep learning mechanism and big data in hospitality and tourism: Developing personalized restaurant recommendation model to customer decision-making
With the increasing ubiquity of booking restaurants through online platforms, the need for restaurant recommender systems that satisfy individual preferences has grown. Previous studies have found it challenging to reflect preferences in multiple aspects because customers' restaurant experiences were approached from a single aspect. This study proposes a novel personalized recommender system that uses the aspect-based sentiment analysis (ABSA) technique to derive granular customer preferences and recommend restaurants accordingly. The proposed model’s performance was empirically validated using customer review data from the global review platform Yelp. Initially, the ABSA technique was used to elaborately analyze sentiment scores for five major aspects of restaurants. Subsequently, aspect-specific sentiment scores were applied to a deep learning prediction model to learn the latent interactions between customers and restaurants. The proposed restaurant recommendation model demonstrated superior prediction compared to the five previous proposed recommendation model, especially yielding improved performance instead of models reflecting overall sentiment scores. Additionally, the impact of various aspect sentiments for the restaurant recommender system was empirically validated, and the results were presented from multiple perspectives based on the model configuration and parameters.
期刊介绍:
The International Journal of Hospitality Management serves as a platform for discussing significant trends and advancements in various disciplines related to the hospitality industry. The publication covers a wide range of topics, including human resources management, consumer behavior and marketing, business forecasting and applied economics, operational management, strategic management, financial management, planning and design, information technology and e-commerce, training and development, technological developments, and national and international legislation.
In addition to covering these topics, the journal features research papers, state-of-the-art reviews, and analyses of business practices within the hospitality industry. It aims to provide readers with valuable insights and knowledge in order to advance research and improve practices in the field.
The journal is also indexed and abstracted in various databases, including the Journal of Travel Research, PIRA, Academic Journal Guide, Documentation Touristique, Leisure, Recreation and Tourism Abstracts, Lodging and Restaurant Index, Scopus, CIRET, and the Social Sciences Citation Index. This ensures that the journal's content is widely accessible and discoverable by researchers and practitioners in the hospitality field.