Abstract: A computerized method, system and computer program product may comprise: generating or receiving a search result in response to a user query by a user that searches for an item, the search result depicting the item; and modifying the search result by generating a combination image that depicts user-defined subject matter engaging with the item. The present invention may provide other and/or additional benefits or advantages.

Abstract: Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions executed by the one or more processors can perform functions comprising: storing a category classification hierarchy that classifies items into a plurality of browse categories; monitoring user engagement metrics for each of the items; using the user engagement metrics to compute shelf importance signals for the items; executing a ranking engine that generates a ranked item listing for a browse category based, at least in part, on the shelf importance signals for the items; and transmitting the ranked item listing for the browse category to a computing device. Other embodiments are disclosed herein.

Abstract: A method including obtaining an optimization request at a coordinating engine. The method also can include triggering engines to process the optimization request. At least one of the engines divides the optimization request into subproblems. At least a portion of the engines solve the subproblems. Respective instances of at least one of the engines are triggered to handle respective ones of the subproblems. Each of the engines provides a dynamic algorithmic flow using modularized algorithmic solvers. The dynamic algorithmic flow is adjusted based on a respective input to each of the engines. The method additionally can include outputting, from the coordinating engine, one or more results in response to the optimization request, based on results for the subproblems generated by the engines. Other embodiments are described.

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: obtaining a first load plan, wherein the first load plan comprises a set of stacks assigned to floor spots of a trailer; determining that at least one of (a) there is an empty floor spot in the first load plan for the trailer or (b) the first load plan is infeasible; determining a target number of stacks for an updated load plan; iterating, via simulated annealing, through modifications to the updated load plan; terminating the iterating when a predetermined termination criteria is satisfied; and outputting the updated load plan. Other embodiments are disclosed.

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: 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 perform functions relating to detecting trending search queries that are popular in a short-term time period. User engagement metrics are derived from historical search engine data. A trending analysis function executes statistical analyses on the user engagement to identify the trending search queries. Other embodiments are disclosed herein.

Abstract: 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 perform functions comprising: generating pairs of identities from a plurality of sources; for each respective pair of identities of the pairs of identities: determining a match probability for the respective pair of identities using a deep-learning transformer-based binary classification model; and linking the respective pair of identities as nodes on a graph when the match probability meets a predetermined threshold, wherein a linkage between the nodes represents a match for the respective pair of identities; generating, using a connected component algorithm, clusters each containing identities representing a respective user; and generating a respective user profile for the respective user for each cluster. Other embodiments are disclosed.

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: A method including obtaining an index message at both (a) an index updater for an index and (b) a subindex updater for a subindex. The method also can include updating, by the index updater, the index based on the index message. The method additionally can include determining, at the subindex updater, whether the index message is eligible for the subindex. The method further can include updating, by the subindex updater, the subindex based on the index message when the index message is eligible for the subindex. Other embodiments are described.

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, cause the one or more processors to perform: receiving historical marketplace information for a user in a marketplace corresponding to products previously purchased by the user; processing the products to group the products into one or more product-type clusters; analyzing the one or more product-type clusters to determine respective inter-purchase interval (IPI) likelihood scores for each product in each of the one or more product-type clusters; identifying one or more candidate products from the one or more product-type clusters that have respective IPI likelihood scores that satisfy one or more thresholds; determining a respective time and a respective duration for a respective re-purchase notification for the user based on the respective IPI likelihood score for each of the one or more candidate products; ranking the o

Abstract: A system including one or more processors and one or more non-transitory computer-readable storage devices storing computing instructions that, when run on the one or more processors, cause the one or more processors to execute operations: storing image hashes corresponding to images for items offered via an electronic platform; receiving, from a computing device, a search query via a search engine included on the electronic platform; executing a relevance function; executing a visual diversity ranking function that reorders the relevant set of search results to generate a ranked set of search results, wherein the visual diversity ranking function utilizes the image hashes corresponding to the subset of the items to reorder the relevant set of search results; and transmitting the ranked set of search results to the computing device. Other embodiments are disclosed herein.

Abstract: 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 perform functions comprising: receiving, by an application programing interface (API), a change request for a route plan, wherein the change request is subject to a scenario specification and a load feasibility specification; determining an updated route plan based on whether the change request for the route plan is a feasible route based on the scenario specification and the load feasibility specification, and the route stop time is the route start time is flexible; and outputting the updated route plan and, when the route stop time is flexible, outputting the route start time. 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 perform operations: generating, using at least two different search algorithms, combined search results based on a search query, each search result of the combined search results having a respective rank based on at least one of the at least two different searching algorithms; determining a set of search results from the combined search results, wherein each result within the set of search results was respectively interacted with by at least one user; and determining a traffic impact for each result rank in the set of search results. Other embodiments are described 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 perform: receiving a first set of facet information corresponding to a first marketplace; receiving a second set of facet information corresponding to a second marketplace; analyzing the first set of facet information to reduce a number of attributes to process; building a machine learning model to map candidate facets from the first set of facet information to facets from the second set of facet information; training the machine learning model based on the first set of facet information, the second set of facet information, and the mapping of candidate facets from the first set of facet information to facets from the second set of facet information; and displaying a selected number of re-ranked facets based on an output from the machine learning model. 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, cause the one or more processors to perform: receiving historical interaction information corresponding to a user in a marketplace; identifying a shopping journey and a basket type for the user based on the cart context and items in a cart for the user for a current user session; identifying a price threshold for the cart for the user; building a machine learning model for the current user session in real-time based on the historical interaction information, the cart context, the basket type and the price threshold to determine a ranking of new items to display to the user to add to the cart for the current user session, wherein the new items satisfy the price threshold; re-ranking the ranking of the new items to display to the user in the current user session based on item attributes of the new items; and transmitting the

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 method including receiving a request from a user to view a page. The page is one of a search results page or a browse shelf page. The method also can include obtaining a respective brand affinity score for the user for each of one or more product types associated with the request. The method additionally can include generating a respective brand affinity signal for the user for each respective item in a baseline list of items to be displayed on the page, based on the request and the respective brand affinity score for the user for a product type of the one or more product types associated with the respective item. The method further can include generating a reranking of the items to be displayed on the page, based on a machine learning model and based on factors comprising the respective brand affinity signals for the user for the items and other rerank signals. The method additionally can include outputting the reranking of the items. 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 perform: receiving historical driver search information corresponding to a first offer publish time criterion, the first offer publish criterion including a driver lag time; building a machine learning model based on the driver search information to determine a first metric and a second metric; analyzing the first metric and the second metric with an optimization model to determine a second offer publish time criterion that reduces the driver lag time; receiving an order for a delivery for an item, the order including a delivery time window; transmitting the order to a driver search platform subject to the second offer publish time criterion to reduce the driver lag time and mitigate delivery outside of the delivery time window. 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, cause the one or more processors to perform: receiving historical engagement information for products in a marketplace; clustering a first subset of the products based on at least one clustering criterion and based on a set of attributes; identifying a second subset of the products that are similar to the first subset of the products based on at least one similarity criterion; determining a third subset of the products by filtering the second subset of the products based on the historical engagement information for the second subset of the products; determining at least one benchmark for the first subset of the products based on the historical engagement information for the first subset of the products; and determining a lift score for the third subset of the products based on the at least one benchmark.

Abstract: A computer-implemented method including determining an anchor product type for an anchor item. The method further can include determining at least one associated product type for the anchor product type.