Language representation of restaurants: Implications for developing online recommender systems

As a marketing tool recommender systems have the potential to provide relevant and highly personalized information to consumers. However, developing effective recommender systems requires a substantive understanding of consumers’ preferences as well as meaningful ways to represent hospitality and travel products. This paper argues that language holds the key to understanding consumer preferences and therefore developing effective online recommender systems. Specifically, it explores the nature of the language used by consumers to describe their dining experiences in contrast to the language used by restaurant websites. The findings indicate that consumers use substantially different vocabularies from restaurant websites to describe dining experiences. This study provides implications for developing online recommender systems for restaurants as well as general hospitality and travel products.     

Exploring the underlying factors of customer value in restaurants: A machine learning approach

This study explores the underlying factors of customer value in a restaurant setting by applying machine learning-based natural language processing techniques to the analysis of a vast amount of online customer reviews. The study identifies 14 factors that reflect a holistic view of previous research on customer value. The findings suggest that the comprehensive approach incorporating cognitive and affective aspects into the experiential perspective of value gain deeper insights into the nature of customer value. Moreover, the study uniquely finds new factors that prior research has rarely investigated. This study proposes a methodological framework that enables researchers to test the quantitative measure of customer perceptions derived from unstructured online reviews. Practitioners will be able to use the findings to understand customers better and enhance service operations.