2023
DOI: 10.1108/jhtt-01-2023-0019
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Restaurant recommendation model using textual information to estimate consumer preference: evidence from an online restaurant platform

Abstract: Purpose Textual information about restaurants, such as online reviews and food categories, is essential for consumer purchase decisions. However, previous restaurant recommendation studies have failed to use textual information containing essential information for predicting consumer preferences effectively. This study aims to propose a novel restaurant recommendation model to effectively estimate the assessment behaviors of consumers for multiple restaurant attributes. Design/methodology/approach The author… Show more

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Cited by 6 publications
(2 citation statements)
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“…Consequently, this study employed the SemEval dataset, which was extensively recognized for its utility in prior investigations (Pontiki et al, 2016). To assess the performance of the recommendation model, this study utilized the http://yelp.com dataset, which is widely used in the restaurant domain (Lee et al, 2021; Li et al, 2023). The dataset allocation for this study was as follows: 60% served as the training dataset, facilitating the optimization of the model's various parameters; 20% was dedicated to validation sets for fine‐tuning these parameters; and the final 20% constituted the test dataset, aimed at evaluating the model's recommendation efficacy.…”
Section: Methodsmentioning
confidence: 99%
“…Consequently, this study employed the SemEval dataset, which was extensively recognized for its utility in prior investigations (Pontiki et al, 2016). To assess the performance of the recommendation model, this study utilized the http://yelp.com dataset, which is widely used in the restaurant domain (Lee et al, 2021; Li et al, 2023). The dataset allocation for this study was as follows: 60% served as the training dataset, facilitating the optimization of the model's various parameters; 20% was dedicated to validation sets for fine‐tuning these parameters; and the final 20% constituted the test dataset, aimed at evaluating the model's recommendation efficacy.…”
Section: Methodsmentioning
confidence: 99%
“…Li et al (2022) developed a four-layer neural network for Tokyo restaurant reviews, showing promise but with complexity limiting its broader application. Finally, Li et al (2023) developed a model using CNN to extract semantic meanings from reviews and predict consumer evaluations of restaurants by learning the interactions between consumer preferences and restaurant attributes.…”
Section: Literature Reviewmentioning
confidence: 99%