Recommender System for Food Startup

Abstract

Automatic suggesting models have been available already for item buying, films, and amusements. Demand for one such system for food delivery startup is picking up now. Main aim of this research work is to come up with design of one such recommender system for food delivery startup makes it to sustain in the business on long run through better understanding as well as retention of potential customers automatically. Zomato reviews data are taken for analyzing using some selected algorithms of machine learning. From them the best predictor model is picked up for building the final proposed engine. Underlying methodology goes like this, it first knows and learns about customers using past and near past data (training data) and using that knowledge it tries to draw results of current data. Accuracy of this result is then observed and pruned, if not up to considered bench mark, by employing bias-variance tradeoff as well as considering more reliable characteristics. Once classifier design with required level of accuracy is obtained and it can then be developed. The focus of the research is to design a system that facilitates automatic recommendation for food startup to know and retain its good restaurants aggregation as well as potential users.