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Fuzzy rule-based system (FRBS), a soft computing method used for big data analysis, is used to determine which single hotel attribute or interrelated hotel attributes used in Travel 2.0 data play a role on price–performance (PP).

FRBS, based on fuzzy set theory, is used using the data set of four- and five-star hotels in the Alanya destination in Turkey collected from HolidayCheck.de website for the period between 2009 and 2016.

Single attributes do not have an impact on PP. At least two or more attributes are necessary to have an impact on PP. Compensations among attributes that are observed to be leading to PP not to change from their current level. Instead of assuming a linear relationship between hotel attributes and PP, non-linearity should often be assumed. In addition, some hotel attributes do not have an impact on PP until some other attribute reaches a certain level.

The limitations of this research can be grouped under two topics. While the first is related to data, which is German-speaking tourists staying at four- and five-star hotels between 2009 and 2016, the second is the limitation on generalizability. By implementing other types of data related to hotel attributes, new insights can be generated to shed light on different aspects of the relationship between hotel attributes and PP or other measures such as overall evaluation.

A data-driven model called FRBS is constructed using original verbal statements. Novel insights pertinent to relations between hotel attributes and PP have been extracted.

乏晰规则为本系统（FRBS）作为大数据分析的软计算方法, 接用旅游2.0数据来分析出哪个酒店属性或者相关酒店属性对于价格-效益（PP）有直接作用。

本论文采用FRBS以Fuzzy Set理论为基础, 采集2009年至2016年之间的HolidayCheck.de网站上的土耳其阿拉尼亚目的地的四星和五星酒店数据作为样本, 进行分析计算。

酒店个别属性并不对PP具有直接影响。至少两个或者以上属性加和对PP有直接影响。酒店属性与PP的关系并不是预想的那样线性关系, 而是非线性的。此外, 一些酒店属性起初并不对PP有影响, 而是直到其他属性达到一定的水平后才起反应。

本论文使用原始文本数据来构建以数据导向的FRBS模型。本论文给出了对于酒店属性和PP的关系相关的新型建议。