A Machine Learning Model for Recommending Restaurants based on User Ratings
B. Muni Lavanya1, K.Kalyan Kumar2, H.Shagufta Kayanath3, D.Pushpalatha Bai4

1B. Muni Lavanya*, Assistant Professor , Department. of Computer Science and Engineering, Jntuacep. Specialized in Computer Networks and Cloud Computing, India.
2K. Kalyan Kumar, Student, Department Of Computer Science and Engineering, IV B. Tech, India.
3H. Shagufta Kayanath, Student, Department Of Computer Science and Engineering, IV B. Tech, India.
4D.Pushpalatha Bai, Student, Department Of Computer Science Engineering, IV B. Tech, India.

Manuscript received on April 02, 2020. | Revised Manuscript received on April 15, 2020. | Manuscript published on May 30, 2020. | PP: 732-736 | Volume-9 Issue-1, May 2020. | Retrieval Number: A1189059120/2020©BEIESP | DOI: 10.35940/ijrte.A1189.059120
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: However, oftentimes people just search a restaurant by using word “restaurant”, while the word “restaurant” means differently to different individuals. For an Asian, it can mean a “Chinese restaurant” or “Thai restaurant”. How to correctly interpret search requests based on people’s preference is a challenge. Building a machine-learning model based on activity history of a registered user can solve this problem. The activity histories used by this research are reviews and ratings from users. This project introduces a data processing pipeline, which uses reviews from registered users to generate a machine-learning model for each registered user. This project also defines an architecture, which uses the generated machine-learning models to support real-time personalized recommendations for restaurant searching and type of foods good at those recommended restaurants. Finally, this project aims to develop a good machine learning model, different collaborative filtering methodologies are considered to predict restaurants using user ratings. Slope One, k-Nearest Neighbors algorithm and multiclass SVM classification are some of the collaborating methodologies are going to consider in this project. 
Keywords: Machine Learning, Regression, Training, predict, Accuracy.
Scope of the Article: Machine Learning