Product recommendations based on characteristics from end user-generated text

A system for generating a related product interface portion in an electronic user interface based on user text, the system comprising:

• a non-transitory, computer-readable medium storing instructions;
• and
• a processor configured to execute the instructions to:
• (i) apply, to respective end user-generated text associated with each product of a plurality of products, a machine learning model trained to output portions of the end user-generated text that include a plurality of characteristics associated with the plurality of products;
• (ii) generate a plurality of embeddings vectors, each of the plurality of embeddings vectors representative of a respective one of the plurality of characteristics;
• (iii) identify an initial vector of the plurality of embeddings vectors as having a shortest average distance to the plurality of embeddings vectors;
• (iv) define an initial cluster of embeddings vectors as the plurality of embeddings vectors within a threshold distance of the initial vector, the initial cluster being a first cluster in one or more clusters;
• (v) identify a second vector of the plurality of embeddings vectors having a shortest average distance to the plurality of embeddings vectors not in the first cluster;
• (vi) define a further cluster of embeddings vectors as the plurality of embeddings vectors within a threshold distance of the further vector and add the further cluster to the one or more clusters;
• (vii) repeat steps (v) and (vi) until each of the plurality of embeddings vectors is within a cluster of the one or more clusters;
• (viii) receive an input related to a product category;
• and
• (ix) generate and present a graphical user interface that includes a respective product from the defined cluster associated with the product category.

A non-transitory computer-readable storage medium storing program instructions that, when executed by a processor, cause a computer system to perform operations comprising:

• (i) applying, to respective end user-generated text associated with each product of a plurality of products, a machine learning model trained to output portions of the end user-generated text that include a plurality of characteristics associated with the plurality of products;
• (ii) generating a plurality of embeddings vectors, each of the plurality of embeddings vectors representative of a respective one of the plurality of characteristics;
• (iii) identifying an initial vector of the plurality of embeddings vectors as having a shortest average distance to the plurality of embeddings vectors;
• (iv) defining an initial cluster of embeddings vectors as the plurality of embeddings vectors within a threshold distance of the initial vector, the initial cluster being a first cluster in one or more clusters;
• (v) identifying a further vector of the plurality of embeddings vectors having a shortest average distance to the plurality of embeddings vectors not in a cluster of the one or more clusters;
• (vi) defining a further cluster of embeddings vectors as the plurality of embeddings vectors within a threshold distance of the further vector and adding the further cluster to the one or more clusters;
• (vii) repeating steps (v) and (vi) until each of the plurality of embeddings vectors is within a cluster of the one or more clusters;
• (viii) receiving an input related to a product category;
• and
• (ix) generating and presenting a graphical user interface that includes a respective product from the defined cluster associated with the product category.