Item embeddings for machine learning systems

A non-transitory computer-readable medium having program instructions stored thereon that are executable to cause a first computer system to perform operations comprising:

• accessing item information that identifies a set of items;
• generating, based on categorical data about the set of items, an initial representation of the set of items that positions the set of items in an embedding space as embeddings;
• obtaining correlation information for at least two items of the set of items, wherein the correlation information is based on recorded user behavior and identifies, for a particular one of the at least two items, one or more similar items to that particular item, and wherein the recorded user behavior is based on a monitoring of a set of user interactions that includes search queries issued by a set of users in connection with the particular item and the one or more similar items;
• and
• generating, based on the correlation information, an updated version of the initial representation that decreases a distance between an embedding of at least one of the set of items that is included in the one or more similar items and an embedding of the particular item in the embedding space and increases a distance between an embedding of at least one of the set of items that is not included in the one or more similar items and the embedding of the particular item in the embedding space;
• and
• generating, based on the updated version, a recommendation based on two or more items of the set of items being indicated as similar based on embeddings of the two or more items being positioned within a proximity threshold in the embedding space.

A system, comprising:

• at least one processor;
• and
• a memory having program instructions stored thereon that are executable by the at least one processor to perform operations comprising:
• accessing item information that identifies a first set of items;
• performing an initial embedding of the first set of items into a vector space as a set of embeddings based on categorical data collected about the first set of items;
• sending a request to a different system for information relating to an item selected from the first set of items;
• receiving, from the different system, correlation information that identifies recorded user behavior that is indicative of correlations between a second set of items and the selected item;
• and
• based on the correlation information, modifying a particular one of the set of embeddings that corresponds to the selected item such that the particular embedding is moved closer in the vector space to ones of the set of embeddings that correspond to the second set of item and farther away from ones of the set of embeddings that correspond to those ones of the first set of items that are not included in the second set of items;
• and
• producing a recommendation based on two or more items of the first set of items being indicated as similar based on embeddings of the two or more items being positioned within a proximity threshold in the vector space.