Machine learning for product assortment analysis

A method comprising:

• accessing data regarding a plurality of sample products, the data including four or more attribute types and, for each product in the plurality of sample products, a value for each of the four or more attribute types;
• forming a vector for each product in the plurality of sample products, the vector being an array comprising the values for each of the four or more attribute types;
• training an embedding machine-learned model using a first machine learning algorithm to produce a trained embedding machine-learned model, the training comprising feeding the vectors for the plurality of sample products into the first machine learning algorithm, the first machine learning algorithm, in response to the feeding, learning an embedding in a three dimensional space for each vector, the embedding representing a set of parameters to apply to an input vector in order to map the input vector to a set of coordinates in the three dimensional space;
• accessing data regarding a plurality of products, the data regarding the plurality of products including the four or more attribute types and, for each product in the plurality of products, a value for each of the four or more attribute types;
• forming a vector for each product in the plurality of products, the vector being an array comprising the values for each of the four or more attribute types;
• and;
• feeding the vectors for the plurality of products into the trained embedding machine-learned model to obtain an embedding for each product in the plurality of products;
• and
• storing the embedding for each product in a data structure;
• and
• rendering a graphical user interface displaying a three-dimensional graph, with the embeddings for each product in the plurality of products in the data structure displayed as points in the three-dimensional graph corresponding to their respective set of coordinates.

A non-transitory machine-readable medium storing instructions which, when executed by one or more processors, cause the one or more processors to perform operations comprising:

• accessing data regarding a plurality of sample products, the data including four or more attribute types and, for each product in the plurality of sample products, a value for each of the four or more attribute types;
• forming a vector for each product in the plurality of sample products, the vector being an array comprising the values for each of the four or more attribute types;
• training an embedding machine-learned model using a first machine learning algorithm to produce a trained embedding machine-learned model, the training comprising feeding the vectors for the plurality of sample products into the first machine learning algorithm, the first machine learning algorithm, in response to the feeding, learning an embedding in a three dimensional space for each vector, the embedding representing a set of parameters to apply to an input vector in order to map the input vector to a set of coordinates in the three dimensional space;
• accessing data regarding a plurality of products, the data regarding the plurality of products including the four or more attribute types and, for each product in the plurality of products, a value for each of the four or more attribute types;
• forming a vector for each product in the plurality of products, the vector being an array comprising the values for each of the four or more attribute types;
• and;
• feeding the vectors for the plurality of products into the trained embedding machine-learned model to obtain an embedding for each product in the plurality of products;
• and
• storing the embedding for each product in a data structure;
• and
• rendering a graphical user interface displaying a three-dimensional graph, with the embeddings for each product in the plurality of products in the data structure displayed as points in the three-dimensional graph corresponding to their respective set of coordinates.