Systems and methods for automatic analysis of text-based food-recipes

A method of identifying at least one similar recipe for a text-based food-recipe by a computer system in communication with a trained neural network, with a food-recipe dataset, and with a client terminal, via a network, comprising:

• receiving from the client terminal via a GUI, a target text-based food-recipe including a text-based set of instructions for preparation of a certain food-recipe according to a text-based set of ingredients;
• converting the target text-based food-recipe to a target food-recipe data structure storing an indication of the set of ingredients, and the text-based instructions;
• receiving a plurality of sample food-recipe data structures each storing a set of ingredient indications, and text-based instructions for preparation;
• computing for the target food-recipe data structure, by the trained neural network, coordinates of a target point within a multi-dimensional food-recipe space;
• computing for each of the sample food-recipe data structures, by the trained neural network, coordinates of a respective sample point within the multi-dimensional food-recipe space;
• computing a statistical distance between the target point and each respective sample point;
• selecting at least one sample food-recipe data structure according to a requirement of the statistical distance;
• and
• providing a text-based version of the selected at least one sample food-recipe data structure for presentation within the GUI presented on the display of the client terminal.

A method of computing a classifier for classification of a text-based food-recipe by a computer system in communication with a trained neural network, and a food-recipe dataset, comprising:

• receiving a plurality of sample food-recipe data structures each storing a set of ingredient indications, and text-based instructions for preparation;
• computing for each of the sample food-recipe data structures, by the trained neural network, coordinates of a respective sample point within a multi-dimensional food-recipe space;
• receiving an indication of a classification type for each of the plurality of sample food-recipe data structures;
• and
• training a statistical classifier for computing a target classification type for a target food-recipe data structure according to the sample points and associated classification types.

A computer implemented method of generating a neural network for computation of a multi-dimensional representation of a text-based food-recipe, comprising:

• receiving by a computer system in communication with:
• a food-recipe dataset, an ingredient dataset, and an ingredient-substitution dataset:
• a plurality of food-recipe data structures each storing a set of ingredient indications and text of instructions for preparation,
• a plurality of ingredient data structures each storing values of parameters of a respective ingredient indication of the set of ingredient indications, represented as an ingredient point in a multi-dimensional ingredient space,
• a plurality of ingredient substitution data structures each storing at least two indications of ingredients stored by the ingredient dataset that are substitutes for at least one ingredient indication stored in at least one food-recipe data structure, and
• training a neural network according to the plurality of food-recipe data structures, the plurality of ingredient data structures, and the plurality of ingredient substitution data structures, wherein initial network weights of the neural network are randomly selected and adjusted according to a plurality of iterations, and comprising for each iteration:
• randomly selecting a certain food-recipe data structure,
• creating an adjusted food-recipe data-structure from the certain food-recipe data-structure by replacing a randomly selected certain ingredient indication with a predefined keyword in the set of ingredients and in the instructions of the certain food-recipe,
• feeding the adjusted food-recipe data-structure into the current neural network, and computing an objective loss function indicative of a statistical distance between a set of coordinates of the adjusted food-recipe data structure denoting an adjusted point within a multi-dimensional food-recipe space, and a set of coordinates of the certain food-recipe data-structure denoting a certain food-recipe point within the multi-dimensional food-recipe space;
• wherein the neural network is trained by computation of a set of weights for layers of the neural network according to iterative minimization of the objective loss function until a stop condition is met;
• wherein the trained neural network computes coordinates of a food-recipe point within a multi-dimensional food-recipe space for an input of a target text-based food-recipe received by the computer system from a client terminal over a network.