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

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.