Hotel recommendation mechanism based on online reviews considering multi-attribute cooperative and interactive characteristics

IF 6.7 2区 管理学 Q1 MANAGEMENT
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引用次数: 0

Abstract

Online reviews of hotels provide important information to consumers. The process of extracting useful information from diverse online reviews is crucial for making the best final decisions. To explore the hidden intrinsic information behind online reviews, this paper optimizes information extraction by integrating multiple sources, and gives the recommendation alternative. First, to meet quantitative requirements, the probabilistic linguistic term set is introduced to demonstrate the massive number of comments crawled. Second, considering preference and fluctuation, the relative importance of multiple attributes is determined. Because multiple attributes typically have cooperative or mutually exclusive relationships, a novel model is presented by introducing such relationship to modify relative importance. Third, inspired by the 2-additive Choquet integral operator and the Mahalanobis-Taguchi System, a bi-objective optimization model is proposed to illustrate the interactive effect of comments and develop an attribute correlation network. The specific relationships between attributes are reflected, including the positive and negative interactions. The relative importance, interactive imporantce and subgroup utility can be obtained. Fourth, to guarantee the operability and interpretability of the recommendation results, this paper presents a new information fusion operator and an probabilistic linguistic three-way recommendation process. Finally, a case study is used to demonstrate the complete procedures, and the parameter and comparative analyses highlight the effectiveness of the new operator and recommendation method.

基于在线评论的酒店推荐机制,考虑多属性合作与互动特性
酒店的在线评论为消费者提供了重要信息。从各种在线评论中提取有用信息的过程对于做出最佳最终决策至关重要。为了挖掘在线评论背后隐藏的内在信息,本文通过整合多种信息源来优化信息提取,并给出推荐备选方案。首先,为了满足定量要求,本文引入了概率语言术语集,以展示所抓取的海量评论。其次,考虑偏好性和波动性,确定多个属性的相对重要性。由于多个属性通常具有合作或相互排斥的关系,因此通过引入这种关系来修改相对重要性,从而提出了一个新颖的模型。第三,受二加法 Choquet 积分算子和 Mahalanobis-Taguchi 系统的启发,提出了一个双目标优化模型来说明评论的交互影响,并建立了一个属性相关网络。该模型反映了属性之间的具体关系,包括正向和负向交互。可以得到相对重要性、交互重要性和子群效用。第四,为保证推荐结果的可操作性和可解释性,本文提出了一种新的信息融合算子和概率语言三向推荐过程。最后,本文通过一个案例研究展示了完整的程序,并通过参数和对比分析强调了新算子和推荐方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Omega-international Journal of Management Science
Omega-international Journal of Management Science 管理科学-运筹学与管理科学
CiteScore
13.80
自引率
11.60%
发文量
130
审稿时长
56 days
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.
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