Restaurant Recommendation System for User Preference and Services Based on Rating and Amenities

Abstract

Recommendation systems are being enforced to offer personalized set of services to the users. They are basically build to produce recommendations or suggestions (like restaurants, places…) that comply with user’s concern and that can be applied to multiple fields. To enhance the quality and service of Recommendation systems and to resolve any issues related to it, various effective techniques linked to data management can be made use of. The current paper proposes a machine learning algorithms to resolve the issue of personalized Restaurant selection relying upon tripadvisor.com search data. The facilities provided by the hotel along with user’s comments are being utilized. The NLP - Natural Language Processing is imbibed for examining and tagging all the previous user’s comments (whether positive or negative) for every hotel, thereafter computing the overall % of the comments and storing the output. In the process of Restaurant recommendation, first the user chooses the hotel’s features according to his interest and centered on this, the corresponding hotels are fetched and the user comments are examined to identify the hotel with the highest ranking. Eventually, the highest rated hotel is being recommended to the user by the restaurant recommended system. The proposed sentimental score measure NLP algorithm is used for finding the aspect and sentiments of the user comments. Natural language processing (NLP) is one of the machines learning technique to analyze, understand, and derive meaning from human language in a smart and useful way. The evaluation results reveal that the proposed NLP algorithm improves the performance when compared to existing algorithms. The focus of the research work is to offer list of recommended restaurants that is more precise and accessible. The conclusion and results reveal that the suggested approach yields high accuracy.