Finding the reviews on yelp that actually matter to me: Innovative approach of improving recommender systems. (October 2020)

• Record Type: Journal Article
• Authors: Luo, Yi; Tang, Liang (Rebecca); Kim, Eojina; Wang, Xi
• Is Part Of: International journal of hospitality management. Volume 91(2020)
• Publication Date: 2020-10
• Subjects: Recommender systems -- Yelp -- Latent aspect rating analysis (LARA) -- Natural language processing (NLP) -- Machine learning; Hotel management -- Periodicals; Restaurant management -- Periodicals; Food service management -- Periodicals; Hôtels -- Gestion -- Périodiques; Restaurants -- Gestion -- Périodiques; Services alimentaires -- Gestion -- Périodiques; Food service management; Hotel management; Restaurant management; Periodicals; 647.94
• DOI: 10.1016/j.ijhm.2020.102697
• Languages: English
• ISSNs: 0278-4319

Abstract

Highlights: Evaluate how the primary attributes, their importance weights and sentiment ratings in textual reviews deconstruct generic numerical rating. A modified machine learning model provide high accuracy in prediction the dynamic relationship between generic numerical rating and primary attributes. The extent research proposed a sophisticated three layers' recommendation system for major review websites in the hospitality and tourism industry. Abstract: As many readers struggle with massive textual information on review websites, developing optimized recommender systems that assist readers in identifying relevant reviews is critical. The present study aims to explore and predict the relationship between a reviewer's evaluation of distinct attributes (i.e., importance and sentiment of a restaurant aspect) 2 and overall satisfaction (i.e., generic numerical rating of a restaurant). Latent Aspect Rating Analysis is modified to achieve the goal. The study identifies five restaurant attributes : food & drinks, customer service, dining atmosphere, restaurant value, and location. Restaurant value contributes most from the importance perspective and food & drinks contributes most from the sentiment perspective. Restaurant value ranks the first as the overall satisfaction of attributes (i.e., combination of importance and sentiment). Accordingly, the present study suggests a supplement of the "dynamic" recommender systems. This study offers scholars and practitioners a refined approach to analyze wealthy review content.