Abstract: A system including 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: determining, using a weight determination model of a machine learning architecture, (a) token weights for tokens included in a search query and (b) extraneous tokens from among the tokens based on at least the token weights; comparing the token weights for the tokens to a probability threshold; segregating, based on at least the token weights, the extraneous tokens from a remainder of the tokens; after segregating, identifying first tokens of the remainder of the tokens that satisfy the probability threshold; and identifying, based on the first tokens of the remainder of the tokens that satisfy the probability threshold, search results for the search query. Other embodiments are described.

Abstract: Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and execute functions comprising: receiving, at a search engine, a search query comprising tokens; determining, using a weight determination model, token weights for the tokens included in the search query; comparing the token weights associated with the tokens to a probability threshold; identifying the tokens that satisfy the probability threshold; and utilizing the tokens that satisfy the probability threshold to identify the search results. Other embodiments are disclosed herein.

Abstract: Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and execute functions comprising: receiving, at a search engine, a search query comprising tokens; determining, using a weight determination model, token weights for the tokens included in the search query; comparing the token weights associated with the tokens to a probability threshold; identifying the tokens that satisfy the probability threshold; and utilizing the tokens that satisfy the probability threshold to identify the search results. Other embodiments are disclosed herein.

Abstract: Systems and methods for retrieving a set of items are disclosed. A target string is received and a set of candidate items is selected from a pool of items based on the target string. The set of candidate items is ranked based on a scoring function. The scoring function includes a plurality of simultaneously determined coefficients. The plurality of simultaneously determined coefficients are generated by a trained ranking model. A set of N items selected from the set of candidate items based on the ranking is output.