# Classification of Retail Products: From Probabilistic Ranking to Neural Networks
## 3 Dataset  
The dataset used in this work is provided by _Midiadia_, which consists of a collection of 31,193 products. The list of attributes considered in this study are shown in Table 1. The _European Article Number_ (EAN) is a standard describing a barcode symbology and numbering system used in global trade to identify a specific retail product type, in a specific packaging configuration, from a specific manufacturer. _Category_, _Subcategory_, and _Variety_ represent the 3-levels (hierarchy) the company considers to organize the catalog into a taxonomy. _Brand_ is an identifying name for a product manufactured by a particular company. Finally, the other terms are coherent to the EU regulation, as follows. Just to clarify the meaning of these terms, a sample extract (Although product attributes are in Spanish, in the following, the translated version of fields and values are used to improve readability) of the dataset is shown in Table 2:  
* 'Customary name' means a name which is accepted as the name of the food by consumers in the Member State in which that food is sold, without that name needing further explanation. * 'Legal name' means the name of food prescribed in the Union provisions applicable to it, or, in the absence of such Union provisions, the name provided for in the laws, regulations, and administrative provisions applicable in the Member State in which the food is sold to the final consumer or to mass caterers. * An 'Ingredient' means any substance or product, including flavorings, food additives, and food enzymes, and any constituent of a compound ingredient, used in the manufacture or preparation of food and still present in the finished product, even if in an altered form; residues shall not be considered as 'ingredients'. Moreover, the EU regulation on the provision of food information to consumers. * 'List of ingredients' (suitable heading which includes the word 'ingredients') shall include all the ingredients of the food, in descending order of weight, as recorded at the time of their use in the manufacture of the food. The dataset also includes other attributes, such as packaging size, healthy claims, calories, etc. However, these attributes are aimed to recommend alternative or replacement products in a specific variety, but the problem we are trying to solve is a different one: when a new product is incorporated into the company food catalog, all the information in the product labeling and packaging is automatically extracted, but the decision of which Variety is the one that corresponds to the new product is currently manually done. Thus, we need to automatically decide to which Variety a new product belongs, so we have focused only on attributes directly related to the kind of food, discarding the others. According to this selection, rows with blank fields in all the selected columns of Table 1 are removed. This entails that we have worked with a dataset with 20,888 products, hereinafter the MDD-DS (_Midiadia_ DataSet). \begin{table}
\begin{tabular}{l l l} \hline \hline
**Field** & **Levels** & **Description** \\ \hline _EAN_ & & Unique Product Number \\ _Category_ & 16 & 1 st Level Category \\ _Subcategory_ & 62 & 2 nd Level Category \\ _Variety_ & 159 & 3 rd Level Category \\ _Brand_ & 3015 & Product Brand \\ _Name_ & 11,139 & Product Customary Name \\ _Legal Name_ & 8442 & Official product denomination regarding the European \\ _Ingredients_ & & Union provisions \\  & & List of Ingredients in the product \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Product attributes in the dataset. Table 1 also includes the number of different values that exist in the variable considered MDD-DS. Thus, as a categorical variable, the dataset shows 159 levels for _Variety_. The fact of knowing in which upper level of categorization is each variety can be helpful for classification purposes. _Brand_ can bring useful information apart from the _Name_ and _Legal Name_ since they are defining the kind of product.