A restaurant recommendation system by analyzing ratings and aspects in reviews

Abstract

Recommender systems are widely deployed to predict the preferences of users to items. They are popular in helping users find movies, books and products in general. In this work, we design a restaurant recommender system based on a novel model that captures correlations between hidden aspects in reviews and numeric ratings. It is motivated by the observation that a user’s preference against an item is affected by different aspects discussed in reviews. Our method first explores topic modeling to discover hidden aspects from review text. Profiles are then created for users and restaurants separately based on aspects discovered in their reviews. Finally, we utilize regression models to detect the user-restaurant relationship. Experiments demonstrate the advantages.