The buying behavior of the consumer is
affected by the suggestions given to the items.
Recommendations can be made in the form of a review
or ranking given to a specific product. Calories
consumed by people contains carbohydrates, fats,
proteins, minerals and vitamins, and any malnutrition
causes severe health problems. In this paper, we propose
a recommendation system which is trained on the basis
of the recommendations received by the customer who
has already used the product. Software recommends the
product to the customer on the basis of the experience of
the consumer using the same product. Each person has
his or her own eating patterns, based on the preferences
and dislikes of the user, indicating that personalized diet
is important to sustain the success and health of the user.
The proposed recommendation method uses a deep
learning algorithm and a genetic algorithm to provide
the best possible advice.
Keywords : Deep learning algorithm, Genetic algorithm, Optimized Nutrition, Recommendation System, RESTFul web services, TESCO database, Web crawler.