Systems and methods for determining and utilizing search token importance using machine learning architectures

A system comprising:

• one or more processors;
• and
• one or more non-transitory computer-readable media storing computing instructions, that when executed on the one or more processors, cause the one or more processors to execute operations comprising:
• receiving, at a search engine, a search query comprising tokens;
• determining, using a weight determination model of a machine learning architecture, token weights for the tokens included in the search query, wherein each token weight predicts a probability that a corresponding token will match metadata of a positively engaged item included in search results that are presented in response to the search query, wherein the weight determination model is configured to identify extraneous tokens based on at least the token weights or the metadata, wherein the metadata comprises at least one of names, titles, or keywords associated with items of a catalog, and wherein the positively engaged item comprises an item in a set of items that was at least one of selected, viewed, ordered, purchased, or added to a cart by a user who submitted a previous search query;
• comparing the token weights associated with the tokens to a probability threshold;
• segregating the extraneous tokens based on at least the token weights or the metadata;
• identifying the tokens that satisfy the probability threshold;
• and
• utilizing the tokens that satisfy the probability threshold to identify the search results.