Cuisine Recommendation, Classification and Review Analysis using Supervised Learning

To help in growth of businesses it is necessary to do detailed analysis about the customer preferences as well as analysis of sales, products purchase and suggestions of right contents to the user. There are many recommendations systems are available from product recommendation to content recommendations. This works presents a meal classification and recommendation system involving restaurant-related reviews obtained from the real world. Now a day a huge range of options are available for the user to order their foods. There are lots of recommendation systems are available from shopping to recreations. Cuisine is one territory where there is a major chance to suggest meal of customer's choices which helps to save their lot of efforts, time and money. In this work restaurant review analysis and cuisine recommendation proposed using SVM supervised learning algorithm and the functioning of the system analyzed. The proposed method implemented, evaluated on the real world data set and an experimental results gives an average precision, recall and F1-score around 91% which shows the effectiveness of the system in recommendation of meal.