Italian gastronomic recipes dataset

USAGE LICENSE
Creative Commons Attribution 4.0 International Public License

FILE CONTENTS
dataset
├─ foods                                                      folder containing files of food dataset (starting data from giallozafferano.it)
│  ├─ CSV                                                   folder containing files in .csv format
│  │  ├─ categories.csv                                
│  │  ├─ foodDataset.xlsx                            each of other .csv files derived from sheet of this excel file
│  │  ├─ ingredients.csv                               
│  │  ├─ ingredientsClasses.csv                  
│  │  ├─ ingredientsMetaclasses.csv           
│  │  ├─ preparations.csv                            
│  │  └─ recipes.csv                                     
│  └─ TXT                                                     folder containing files in .txt format
│     ├─ scorpored values                             folder containing values in textData scorpored by type of data
│     │  ├─ category-cost-difficulty.txt
│     │  ├─ ingredients.txt
│     │  ├─ names.txt
│     │  ├─ preparations.txt
│     │  └─ preparationTime.txt
│     └─ textData.txt                                       .txt version of dataset/foods/CSV/foodDataset.xlsx file
└─ survey_answers                                      folder containing results of the surveys on food preferences of the dataset
│  ├─ sorts                                                    folder containing results of the three surveys' questions where users sort foods
│  │  ├─ sort1.csv                                         the three csv files contain the survey id, and then the food ordered by the user
│  │  ├─ sort2.csv
│  │  └─ sort3.csv
│  ├─ answers.csv                                        results of the surveys
│  ├─ labels.txt                                              labels of the samples in samples.txt 
│  └─ samples.txt                                          couples of food in pairwise comparison form * extracted from the sorts
└─ readme.md        

* Each sample has the form <IDfood1, IDfood2> and has label 1 if IDfood1 is preferred over IDfood2, -1 if IDfood2 is preferred over IDfood1, 0 if there is indifference relationship over IDfood1 and IDfood2
  The preference sorts have been translated in two phases:

1. search of inconsistencies among the three sorts in order to get the "certain" preference relationships.
2. search of transitivity of preferences among the "certain" couples, giving indifference relationship to those for which no transitivity has been found.

RECIPES DATASET DESCRIPTION
the description refer to dataset/foods/CSV/foodDataset.xlsx

in other sheet of the file are reportet all the ingredients, divided in classes and metaclasses, preparations and categories

NOTE: in dataset/foods/TXT/textData.txt ingredients and preparation has been vectorized as follow:

• each element of the ingredient vector represent the weight of the ingredient class in the recipe. The weight of an ingredient class in a recipe is collected by sum up the weight of the ingredients owned by that particular ingredient class in the recipe.
• each element of the preparation vector represent the weight of the preparation in the recipe.

PREFERENCES DATASET DESCRIPTION
In the file dataset/survey_answer/samples.txt are reported 54 user's orderings in the form of pairwise comparison. Thus for each row correspond the ordering of a user. The ordering is written in the form of pairwise comparison, so each element of the ordering are paired with all others (avoiding simmetries). 
For instance, given the recipes as their ID:

1; 2; 3

becomes:

1,2; 1,3; 2,3

The file is written following the .csv format.
In the file dataset/survey_answer/lables.txt are written the correspongin labels of the couples.
The label has value 1 if the first element of the couple is preferred over the second, -1 if the second element is preferred over the first, 0 if there is uncertainity about which of the element is preferred.

HOW TO CITE
D.Fossemò, F.Mignosi, L.Raggioli, M.Spezialetti, F.A.D'Asaro. Using Inductive Logic Programming to globally approximate Neural Networks for preference learning: challenges and preliminary results. Proceedings of BEWARE-22, co-located with AIxIA 2022, November 28-December 2, 2022, University of Udine, Udine, Italy. CEUR Workshop proceedings. Vol. 3319. 67:83. 2023.