Improving restaurant recommendation transparency through feature selection

Abstract

[Abstract]: Recommender systems are widely used to suggest items (i.e., products or services) based on user preferences. However, personalized recommender systems incorporating information beyond user ratings can provide more accurate and relevant recommendations. This paper introduces a hybrid personalized recommender system that utilizes several characteristics of items in addition to user ratings. The focus of this study is on the restaurant industry, and the dataset used is sourced from TripAdvisor, one of the most popular travel and tourism websites. The attributes include price range, cuisines, special diets, meals, and features like free WiFi and wheelchair accessibility. To improve the transparency of the recommendation process and understand the variables that influence the system’s output, we propose the use of feature selection techniques. By analyzing the impact of each variable, this study aims to help readers understand the recommendation process and identify the factors to consider when choosing to visit a restaurant.