Making platform recommendations more responsive to the expectations of different types of consumers: a recommendation method based on online reviews

Optimizing hotel recommendation systems based on consumer preferences is crucial for online hotel booking platforms. The purpose of this study is to reveal differences in hotel recommendation results for different types of consumers by considering consumer expectations. Specifically, this study introduces an online hotel recommendation method that considers three preferences for five types of consumers (business, couples, families, friends, and solo): attribute importance, consumer expectations, and actual hotel attribute performance. Here, consumer expectations are expressed in the form of the 2-tuple. 2-tuple expectations mean that customers can not only express specific demands but also express the probability of meeting the demands. Further, using three different consumer preferences, a similarity measurement model is constructed to recommend hotels for different types of consumers. This study puts this innovative method to the test using a dataset covering 40 hotels in the Beijing area and analyzes the impact of three preferences for different types of consumers on their hotel recommendation results. The method introduced in this study has two management implications. On the one hand, the recommendation method based on consumer preferences can optimize hotel recommendation systems and help online hotel booking platforms improve the accuracy of recommendation results. On the other hand, the proposed method can offer valuable insights to hotel managers, helping them measure their competitiveness and providing guidance for developing service improvement strategies.