Developing a deep learning-based recommendation model using online reviews for predicting consumer preferences: Evidence from the restaurant industry
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 김동언 | - |
dc.contributor.author | 장동수 | - |
dc.contributor.author | 엄금철 | - |
dc.contributor.author | LI JIAEN | - |
dc.date.accessioned | 2024-01-02T08:00:15Z | - |
dc.date.available | 2024-01-02T08:00:15Z | - |
dc.date.issued | 2023-12 | - |
dc.identifier.issn | 2288-4866 | - |
dc.identifier.issn | 2288-4882 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/89909 | - |
dc.description.abstract | With the growth of the food-catering industry, consumer preferences and the number of dine-in restaurants are gradually increasing. Thus, personalized recommendation services are required to select a restaurant suitable for consumer preferences. Previous studies have used questionnaires and star-rating approaches, which do not effectively depict consumer preferences. Online reviews are the most essential sources of information in this regard. However, previous studies have aggregated online reviews into long documents, and traditional machine-learning methods have been applied to these to extract semantic representations; however, such approaches fail to consider the surrounding word or context. Therefore, this study proposes a novel review textual-based restaurant recommendation model (RT-RRM) that uses deep learning to effectively extract consumer preferences from online reviews. The proposed model concatenates consumer-restaurant interactions with the extracted high-level semantic representations and predicts consumer preferences accurately and effectively. Experiments on real-world datasets show that the proposed model exhibits excellent recommendation performance compared with several baseline models. | - |
dc.format.extent | 19 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | 한국지능정보시스템학회 | - |
dc.title | Developing a deep learning-based recommendation model using online reviews for predicting consumer preferences: Evidence from the restaurant industry | - |
dc.title.alternative | 딥러닝 기반 온라인 리뷰를 활용한 추천 모델 개발: 레스토랑 산업을 중심으로 | - |
dc.type | Article | - |
dc.identifier.doi | 10.13088/jiis.2023.29.4.031 | - |
dc.identifier.bibliographicCitation | 지능정보연구, v.29, no.4, pp 31 - 49 | - |
dc.identifier.kciid | ART003031970 | - |
dc.description.isOpenAccess | N | - |
dc.citation.endPage | 49 | - |
dc.citation.startPage | 31 | - |
dc.citation.title | 지능정보연구 | - |
dc.citation.volume | 29 | - |
dc.citation.number | 4 | - |
dc.publisher.location | 대한민국 | - |
dc.subject.keywordAuthor | restaurant recommender system | - |
dc.subject.keywordAuthor | online review | - |
dc.subject.keywordAuthor | deep learning | - |
dc.subject.keywordAuthor | CNN-LSTM | - |
dc.subject.keywordAuthor | text mining | - |
dc.subject.keywordAuthor | 레스토랑 추천 시스템 | - |
dc.subject.keywordAuthor | 온라인 리뷰 | - |
dc.subject.keywordAuthor | 딥러닝 | - |
dc.subject.keywordAuthor | CNN-LSTM | - |
dc.subject.keywordAuthor | 텍스트 마이닝 | - |
dc.description.journalRegisteredClass | kci | - |
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