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 cause the one or more processors to perform acts of: in response to receiving search queries at a search engine, storing search event data and ranking features in one or more databases; generating, using the search event data and the ranking features, a training dataset comprising training event samples; executing a hybrid labeling procedure that assigns labels to the training event samples based, at least in part, on individual engagement information associated with the training event samples; and training a personalized ranking model to rank search results using the training event samples and the labels. Other embodiments are disclosed herein.

Abstract: According to an embodiment of the present disclosure, a system is provided. The system includes a processor and a non-transitory computer-readable medium storing computing instructions. The instructions, when executed on the processor, perform: receiving user search queries and product items, the product items including metadata corresponding to product types, the product types comprising an error, and the error corresponding to one of the product items having a non-compliant product type; modifying the metadata corresponding to the product types based on a product type group to mitigate a computing system from displaying non-compliant material to a user; and in response to a product type of the product type group being the non-compliant product type, performing a computer vision prediction on an image of a product corresponding to the product type to detect a misclassification in the product type group. Other embodiments are disclosed.

Abstract: Systems and methods 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, perform: receiving user search queries and product items, the product items including metadata corresponding to product types; determining a data relationship between the user search queries and the product items based on a weighting factor, the data relationship including user search query nodes and product item nodes; determining a first vector representation for first ones of the product item nodes in the data relationship; determining a second vector representation for second ones of the product item nodes in the data relationship; grouping the first vector representation for first ones of the product item nodes and the second vector representation for second ones of the product item nodes into a product type group based on a proximity search; and modifying the metadata corresponding to the product types based on the p

Abstract: A method including extracting queries from historical search query data. The method also can include spellchecking each of the queries (i) using a first spellcheck model and (ii) using a second spellcheck model. A latency of the first spellcheck model is lower than a latency of the second spellcheck model, and an overall accuracy of the second spellcheck model is higher than an overall accuracy of the first spellcheck model. The method additionally can include determining first queries of the queries in which, for each of the first queries, a first respective spellcheck output from the first spellcheck model is different from a second respective spellcheck output from the second spellcheck model. The method further can include adding at least a portion of the first queries to a spellcheck cache for runtime spellchecking. 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 cause the one or more processors to perform acts of: in response to receiving search queries at a search engine, storing search event data and ranking features in one or more databases; generating, using the search event data and the ranking features, a training dataset comprising training event samples; executing a hybrid labeling procedure that assigns labels to the training event samples based, at least in part, on individual engagement information associated with the training event samples; and training a personalized ranking model to rank search results using the training event samples and the labels. Other embodiments are disclosed herein.

Abstract: Systems and methods 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, perform: receiving user search queries and product items, the product items including metadata corresponding to product types; determining a data relationship between the user search queries and the product items based on a weighting factor, the data relationship including user search query nodes and product item nodes; determining a first vector representation for first ones of the product item nodes in the data relationship; determining a second vector representation for second ones of the product item nodes in the data relationship; grouping the first vector representation for first ones of the product item nodes and the second vector representation for second ones of the product item nodes into a product type group based on a proximity search; and modifying the metadata corresponding to the product types based on the p