Traditional Recommender Systems recommend items on the basis of a single criterion whereas rates of hotel food take many different criteria for each item. Although rating system of food recommender Systems have a promising accuracy, approaches used by them require many previous users to first rate items with respect to criteria. This paper presents a rating Criteria Recommendation System for food Recommendations to choose the best suited hotel in a city according to a users’ preference and other user’s ratings. In order to determine the food rating of a hotel from previous users uses various Natural Language Processing approaches on a hotel of food review corpus and builds a user-item-feature database. At time of hotel leaving the user give rating for next recommendation, if both user give same rating to one hotel food then and if next time one user can rate to other hotel that hotel will recommend to other user.
