{"title":"An online review-driven two-stage hotel recommendation model considering customers’ risk attitudes and personalized preferences","authors":"","doi":"10.1016/j.omega.2024.103197","DOIUrl":null,"url":null,"abstract":"<div><div>Hotel recommendation models provide crucial references for customers to select their ideal hotels and help them overcome information overload. However, previous models primarily focus on capturing public preferences, neglecting personalized preferences or different risk attitudes among customers. To address this gap, this paper proposes a novel two-stage hotel recommendation model driven by online reviews, incorporating customers’ risk attitudes and personalized preferences. Firstly, this paper utilizes the Latent Dirichlet Allocation (LDA) topic extraction model and the sentiment analysis tool to extract public and personalized preferences from hotel reviews and customers’ historical reviews respectively. Secondly, in the first stage of hotel recommendation, this paper constructs a hotel filtering mechanism to cater to customers with different risk attitudes, ensuring that the recommended hotels align with customers’ individual risk tolerance. In the second stage of hotel recommendation, this paper introduces the cosine similarity algorithm of probabilistic linguistic term sets, enabling more accurate and tailored recommendations. Finally, to verify the applicability of the proposed model, a case study is conducted using real data from TripAdvisor.com. The results of the comparative analysis indicate that the proposed model outperforms other recommendation models.</div></div>","PeriodicalId":19529,"journal":{"name":"Omega-international Journal of Management Science","volume":null,"pages":null},"PeriodicalIF":6.7000,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Omega-international Journal of Management Science","FirstCategoryId":"91","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0305048324001622","RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MANAGEMENT","Score":null,"Total":0}
引用次数: 0
Abstract
Hotel recommendation models provide crucial references for customers to select their ideal hotels and help them overcome information overload. However, previous models primarily focus on capturing public preferences, neglecting personalized preferences or different risk attitudes among customers. To address this gap, this paper proposes a novel two-stage hotel recommendation model driven by online reviews, incorporating customers’ risk attitudes and personalized preferences. Firstly, this paper utilizes the Latent Dirichlet Allocation (LDA) topic extraction model and the sentiment analysis tool to extract public and personalized preferences from hotel reviews and customers’ historical reviews respectively. Secondly, in the first stage of hotel recommendation, this paper constructs a hotel filtering mechanism to cater to customers with different risk attitudes, ensuring that the recommended hotels align with customers’ individual risk tolerance. In the second stage of hotel recommendation, this paper introduces the cosine similarity algorithm of probabilistic linguistic term sets, enabling more accurate and tailored recommendations. Finally, to verify the applicability of the proposed model, a case study is conducted using real data from TripAdvisor.com. The results of the comparative analysis indicate that the proposed model outperforms other recommendation models.
期刊介绍:
Omega reports on developments in management, including the latest research results and applications. Original contributions and review articles describe the state of the art in specific fields or functions of management, while there are shorter critical assessments of particular management techniques. Other features of the journal are the "Memoranda" section for short communications and "Feedback", a correspondence column. Omega is both stimulating reading and an important source for practising managers, specialists in management services, operational research workers and management scientists, management consultants, academics, students and research personnel throughout the world. The material published is of high quality and relevance, written in a manner which makes it accessible to all of this wide-ranging readership. Preference will be given to papers with implications to the practice of management. Submissions of purely theoretical papers are discouraged. The review of material for publication in the journal reflects this aim.