Analyzing the Impact of Components of Yelp.com on Recommender System Performance: Case of Austin

Abstract

As people’s demand for eating out is steadily increasing, the number of restaurants is continuously increasing, and catering industry platforms such as Yelp, Open Table, and Zomato provide basic information and evaluation information of restaurants and restaurant recommendation services suitable for users. Existing research on recommending restaurants mainly uses only evaluation information to find neighbors, and the use of user and restaurant information is still in its infancy. In addition, there is little study on how various types of input information affect the performance of the recommender system. This study examines the influence of three component information provided by Yelp.com on the performance of the recommender system using various real restaurants, reviews, and users dataset provided by Yelp.com. For this purpose, Two Phase Experiment was designed, and restaurant data located in Austin, Texas, USA, which has the largest number of review data, was collected. As a result of the experiment, elite status, the cumulative number of reviews, price range and average rating of restaurants could improve the recommendation performance.

Keywords

• Restaurant recommender systems
• yelp
• review data
• experimental design