Abstract: A method can include: storing, on an electronic platform, a vectorized item database corresponding to a plurality of items; tracking electronic interactions between the electronic platform and a user computer during a user session; generating, using an embedding model, a session context embedding based, at least in part, on the electronic interactions tracked during the user session; in response to receiving the search query from the user computer, querying the vectorized item database to identify search results corresponding to one or more of the plurality of items, at least in part, using the session context embedding; and transmitting, by the electronic platform, the search results to the user computer. Other embodiments are disclosed.

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: Example implementations relate to systems and methods for re-ranking products of a query. Example computer implemented methods may include receiving an intermediate recall set of products of a query and obtaining features of a product of the intermediate recall set of products of the query. The computer implemented method can further include performing dense cross interactions between the query and the features of the product of the intermediate recall set of products using a cross-encoder model to generate a class prediction. The computer-implemented method can additionally include generating a re-rank score for the product of the intermediate recall set of products of the query by performing a model inference based at least on the class prediction. The computer-implemented method can also include re-ranking the product of the intermediate recall set of products based on the re-rank score for the product. Other embodiments are described.

Abstract: Examples may be related to entity-aware multi-task machine learning. An example may involve: obtaining a search query; retrieving, using an entity retrieval model, at least one entity based on the search query; generating, using a neural network, query embedding data based on the search query and the at least one entity; generating, using a plurality of task-specific networks, task prediction data for a plurality of tasks based on the query embedding data, wherein each task of the plurality of tasks captures a different aspect of a user intent associated with the search query; and generating at least one search result for the search query based on the task prediction data for the plurality of tasks.

Abstract: A method can include determining, via a translatability classifier module, a translatability class label for a cross-lingual search query received, via a computer network, from a user device for a user. The translatability classifier module can be trained to determine the translatability class label among multiple translatability class labels associated with user intentions for the cross-lingual search query based on a respective probability of the cross-lingual search query being associated with each of the multiple translatability class labels. The method further can include determining, via a language translator module, a class-associated search query for the cross-lingual search query based on the translatability class label, as determined. The method additionally can include transmitting, via the computer network, the class-associated search query to a monolingual search engine. Other embodiments are disclosed.

Abstract: A method can include determining a translatability class label and a class-associated search query for a cross-lingual search query. Determining the translatability class label and the class-associated search query can include determining whether a first class-translation pair for the cross-lingual search query exists in a memory module. If the first class-translation pair exists in the memory module, the method further can include retrieving the first class-translation pair for the cross-lingual search query from the memory module. When no class-translation pair for the cross-lingual search query exists in the memory module, the method additionally can include: determining the translatability class label, and determining the class-associated search query based on the translatability class label.

Abstract: Examples may be related to entity-aware multi-task machine learning. An example may involve: obtaining a search query; retrieving, using an entity retrieval model, at least one entity based on the search query; generating, using a neural network, query embedding data based on the search query and the at least one entity; generating, using a plurality of task-specific networks, task prediction data for a plurality of tasks based on the query embedding data, wherein each task of the plurality of tasks captures a different aspect of a user intent associated with the search query; and generating at least one search result for the search query based on the task prediction data for the plurality of tasks.

Abstract: A method can include: storing, on an electronic platform, a vectorized item database corresponding to a plurality of items; tracking electronic interactions between the electronic platform and a user computer during a user session; generating, using an embedding model, a session context embedding based, at least in part, on the electronic interactions tracked during the user session; in response to receiving the search query from the user computer, querying the vectorized item database to identify search results corresponding to one or more of the plurality of items, at least in part, using the session context embedding; and transmitting, by the electronic platform, the search results to the user computer. Other embodiments are disclosed.

Abstract: A system including a processor and a non-transitory computer-readable media storing computing instructions that, when executed on the processor, cause the processor to perform certain operations: generating a first ambiguity score for a first query; propagating the first ambiguity score for the first query to generate a second ambiguity score for a second query; training a machine-learning classifier at least based on the first query and the second query; and generating, using the machine-learning classifier, a third ambiguity score for a third 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: storing historical session data pertaining to user sessions and archived search queries submitted by users to a search engine; analyzing the historical session data to identify ambiguous queries, including semantically ambiguous queries and multi-language queries; monitoring search queries submitted to the search engine to detect the ambiguous queries; and in response to detecting an ambiguous query, generating a query resolution interface that displays categorical groupings, each of which corresponds to a possible intention of the ambiguous query. Other embodiments are disclosed herein.

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: A method can include determining, via a translatability classifier module, a translatability class label for a cross-lingual search query received, via a computer network, from a user device for a user. The translatability classifier module can be trained to determine the translatability class label among multiple translatability class labels associated with user intentions for the cross-lingual search query based on a respective probability of the cross-lingual search query being associated with each of the multiple translatability class labels. The method further can include determining, via a language translator module, a class-associated search query for the cross-lingual search query based on the translatability class label, as determined. The method additionally can include transmitting, via the computer network, the class-associated search query to a monolingual search engine. Other embodiments are disclosed.

Abstract: A method can include determining a translatability class label and a class-associated search query for a cross-lingual search query. Determining the translatability class label and the class-associated search query can include determining whether a first class-translation pair for the cross-lingual search query exists in a memory module. If the first class-translation pair exists in the memory module, the method further can include retrieving the first class-translation pair for the cross-lingual search query from the memory module. When no class-translation pair for the cross-lingual search query exists in the memory module, the method additionally can include: determining the translatability class label, and determining the class-associated search query based on the translatability class label.

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 perform certain operations. The operations can include generating a first specificity score for a first query. The operations also can include propagating the first specificity score for the first query to generate a second specificity score for a second query. The operations additionally can include training a machine-learning classifier at least based on the first query and the second query. The operations further can include generating, using the machine-learning classifier, a third specificity score for a third 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: storing historical session data pertaining to user sessions and archived search queries submitted by users to a search engine; analyzing the historical session data to identify ambiguous queries, including semantically ambiguous queries and multi-language queries; monitoring search queries submitted to the search engine to detect the ambiguous queries; and in response to detecting an ambiguous query, generating a query resolution interface that displays categorical groupings, each of which corresponds to a possible intention of the ambiguous query. 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: A fast browsing architecture for exploring hierarchical lists of entities through a search user interface. A graphical UI operates to handle the hierarchical lists and sub-lists in different ways for different scenarios such as a hierarchical level is zero (only one list of entities associated with a query and the list cannot be further drilled down), a second scenario where the hierarchical level is one (a list of entities associated with the query and these entities can be further drilled down to a number of sub-lists) and the sub-lists cannot be further drilled down, and a third scenario where the hierarchical level is more than one (a list of entities associated with the query and these entities can be further drilled down to a number of sub-lists), sub-lists can be further drilled down to a number of lists, until there is no more drill down lists to be found.

Abstract: A fast browsing architecture for exploring hierarchical lists of entities through a search user interface. A graphical UI operates to handle the hierarchical lists and sub-lists in different ways for different scenarios such as a hierarchical level is zero (only one list of entities associated with a query and the list cannot be further drilled down), a second scenario where the hierarchical level is one (a list of entities associated with the query and these entities can be further drilled down to a number of sub-lists) and the sub-lists cannot be further drilled down, and a third scenario where the hierarchical level is more than one (a list of entities associated with the query and these entities can be further drilled down to a number of sub-lists), sub-lists can be further drilled down to a number of lists, until there is no more drill down lists to be found.

Abstract: A fast browsing architecture for exploring hierarchical lists of entities through a search user interface. A graphical UI operates to handle the hierarchical lists and sub-lists in different ways for different scenarios such as a hierarchical level is zero (only one list of entities associated with a query and the list cannot be further drilled down), a second scenario where the hierarchical level is one (a list of entities associated with the query and these entities can be further drilled down to a number of sub-lists) and the sub-lists cannot be further drilled down, and a third scenario where the hierarchical level is more than one (a list of entities associated with the query and these entities can be further drilled down to a number of sub-lists), sub-lists can be further drilled down to a number of lists, until there are no more drill down lists to be found.