Abstract: Aspects of the present disclosure involve a system comprising a storage medium storing a program and method for predicting a conversion rate. The program and method provide for receiving, from an advertisement service, a bid to display a first advertisement at a computing device; determining, in response to receiving the bid, a set of features that relate to the first advertisement; providing the set of features to a machine learning model configured to output a predicted conversion rate for the first advertisement, the machine learning model having been trained based on multi-task learning using plural sets of features corresponding to plural second advertisements, the plural sets of features being associated with both click-through conversions and view-through conversions; and determining, based on the output of the machine learning model with respect to the set of features, the predicted conversion rate for the first advertisement.

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 cause the one or more processors to perform functions comprising, in response to a search query from an electronic device of a user, creating combined search results using at least two different searching algorithms, each search result of the combined search results having a respective combined search results rank; receiving, from the electronic device of the user, an interaction with a search result of the combined search results; storing, in the one or more non-transitory computer-readable storage devices, the interaction with the search result of the combined search results, wherein: the interaction with the search result of the combined search results, as stored, indicates whether the search result of the combined search results was a part of (a) results of a first search algorithm of the at least two different searching algo

Abstract: Machine learning architectures may predict the likelihood of interaction by users with content items that are accessible using a client application. The machine learning architectures may include one or more feature interaction layers that are coupled with one or more extraction layers. Content items may be selected to provide to users of the client application based on probabilities of users performing one or more actions with respect to the content items, where the probabilities for each action are determined by the machine learning architectures.

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 functions comprising, in response to a search query from an electronic device of a user, creating combined search results using at least two different searching algorithms, each search result of the combined search results having a respective combined search results rank; receiving, from the electronic device of the user, an interaction with a search result of the combined search results; storing, in the one or more non-transitory computer-readable storage devices, the interaction with the search result of the combined search results, wherein: the interaction with the search result of the combined search results, as stored, indicates whether the search result of the combined search results was a part of (a) results of a first search algorithm of the at least two different searching algo

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: performing a first search for the search query in a database using a first searching algorithm; assigning a respective first rank to each search result of the results of the first search; performing a second search for the search query in the database using a second searching algorithm; assigning a respective second rank to each search result of the results of the second search; interleaving the results of the second search between the results of the first search to create combined search results; facilitating a display of the combined search results; generating a report on an interaction with the search result of the combined search results, as stored; and facilitating a display-of the report. Other embodiments are disclosed herein.

Abstract: Systems and methods including one or more processing modules and one or more non-transitory storage modules storing computing instructions configured to run on the one or more processing modules and perform acts of training a source classifier with labeled source training data of a first product category from a website of an online retailer, clustering target data for a second product category into a plurality of clusters, inserting into each cluster labeled source training data of the first product category, assigning a domain discriminator score to each cluster, determining whether each cluster comprises an agreement cluster or a disagreement cluster using the domain discriminator score, receiving a product search request for a product of the second category from a user of the web site, and coordinating a display of the product on the web site to promote the product.

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 acts of receiving a search query from an electronic device of a user; performing a first search for the search query in a database using a first searching algorithm to retrieve results of the first search; assigning a respective first rank to each search result of the results of the first search; performing a second search for the search query in the database using a second searching algorithm to retrieve results of the second search, wherein the first searching algorithm and the second searching algorithm can be different searching algorithms; assigning a respective second rank to each search result of the results of the second search; interleaving the results of the second search between the results of the first search to create combined search results, wherein each search result of the combined search results can have

Abstract: Systems and methods including one or more processing modules and one or more non-transitory storage modules storing computing instructions configured to run on the one or more processing modules and perform acts of receiving an online search query entered into a search field of an online ecommerce website by a user using the online ecommerce website, determining a query response to the online search query by combining a nonparametric bootstrap distribution and a mixture sequential probability ratio test, the query response comprising one or more products, and coordinating a display of the query response to the user using the online ecommerce website. The query response can be based on one of a query success rate per user session of a plurality of previous user sessions or a revenue per user session of the plurality of previous user sessions.

Abstract: A system, method and computer product for displaying product information is described herein. Facet values that may cause bad customer experience(s) are identified and suppressed. The system, method, and computer product may use a randomization scheme to suppress and/or show “bad” facet values occasionally to gather recent data on the facet values have improved. The suppression process works as a robust noise filter on top of the search and browse faceting experience.

Abstract: Systems and methods including one or more processing modules and one or more non-transitory storage modules storing computing instructions configured to run on the one or more processing modules and perform acts of training a source classifier with labeled source training data of a first product category from a website of an online retailer, clustering target data for a second product category into a plurality of clusters, inserting into each cluster labeled source training data of the first product category, assigning a domain discriminator score to each cluster, determining whether each cluster comprises an agreement cluster or a disagreement cluster using the domain discriminator score, receiving a product search request for a product of the second category from a user of the web site, and coordinating a display of the product on the web site to promote the product.

Abstract: Systems and methods including one or more processing modules and one or more non-transitory storage modules storing computing instructions configured to run on the one or more processing modules and perform acts of receiving an online search query entered into a search field of an online ecommerce website by a user using the online ecommerce website, determining a query response to the online search query by combining a nonparametric bootstrap distribution and a mixture sequential probability ratio test, the query response comprising one or more products, and coordinating a display of the query response to the user using the online ecommerce website. The query response can be based on one of a query success rate per user session of a plurality of previous user sessions or a revenue per user session of the plurality of previous user sessions.

Abstract: A system, method and computer product for displaying product information is described herein. Facet values that may cause bad customer experience(s) are identified and suppressed. The system, method, and computer product may use a randomization scheme to suppress and/or show “bad” facet values occasionally to gather recent data on the facet values have improved. The suppression process works as a robust noise filter on top of the search and browse faceting experience.