Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that distribute item-based digital content across digital platforms using trend setting participants of those digital platforms. For instance, in one or more embodiments, the disclosed systems generate affinity metrics for digital items from a catalog of digital items with respect to a plurality of trend setting participants of a plurality of digital platforms using attributes of digital posts by the plurality of trend setting participants on the plurality of digital platforms and corresponding attributes of the digital items. The disclosed systems further determine predicted demand metrics for the digital items on the plurality of digital platforms using the affinity metrics. Using the predicted demand metrics, the disclosed systems distribute digital content related to the digital items for display on a plurality of client devices via the plurality of digital platforms.

Abstract: A search system employs arrival times with associated confidence scores as search facets for identifying items. The search system identifies a plurality of items based on search input. An arrival time and associated confidence score are determined for each item from the plurality of items. Search results are provided for the plurality of items in response to the search input. The search results are provided based at least in part on the arrival times and associated confidence scores for the plurality of items.

Abstract: Embodiments provide systems, methods, and computer storage media for prediction and computation of electronic shopping carts. In an example embodiment, for each interaction between an e-shopper and an e-commerce application, one or more predicted electronic shopping carts that represent a combination of items the e-shopper is likely to purchase are generated based on current items in the e-shopper's electronic shopping cart and recent interactions with the e-shopper. For some or all of the predicted electronic shopping carts (e.g., those with top predicted confidence levels), corresponding shopping cart computations (e.g., identifying application promotions, determining a price total for the items in the predicted shopping cart) are executed and cached prior to the e-shopping adding the predicted items. As such, a page configured to visualize the predicted electronic shopping cart with a value retrieved from the cached shopping cart computations (e.g.

Abstract: A search system generates custom attributes for use as search facets. User input associated with an image of a target item available on a listing platform is received. The image is analyzed to determine an attribute of the target item as a custom attribute. A value for the custom attribute is determined for each of a number of other items available on the listing platform that are of the same item type as the target item. Search results are provided based at least in part on the values of the custom attribute for the other items.

Abstract: Embodiments provide systems, methods, and computer storage media for prediction and computation of electronic shopping carts. In an example embodiment, for each interaction between an e-shopper and an e-commerce application, one or more predicted electronic shopping carts that represent a combination of items the e-shopper is likely to purchase are generated based on current items in the e-shopper's electronic shopping cart and recent interactions with the e-shopper. For some or all of the predicted electronic shopping carts (e.g., those with top predicted confidence levels), corresponding shopping cart computations (e.g., identifying application promotions, determining a price total for the items in the predicted shopping cart) are executed and cached prior to the e-shopping adding the predicted items. As such, a page configured to visualize the predicted electronic shopping cart with a value retrieved from the cached shopping cart computations (e.g.

Abstract: The present technology provides for facilitating removal of undesired search results. In one embodiment, a search request including a search term(s) to use for performing a search is obtained. Thereafter, a search query is generated to execute the search. The search query includes the search terms and a removal parameter indicating a particular search result to exclude from search results returned in response to the search request. A set of search results are provided for display via a user device. Such a set of search results can be identified based on execution of the search query and exclude the particular search result.

Abstract: Embodiments of the technology described herein, provide improved visual search results by combining a visual similarity and a textual similarity between images. In an embodiment, the visual similarity is quantified as a visual similarity score and the textual similarity is quantified as a textual similarity score. The textual similarity is determined based on text, such as a title, associated with the image. The overall similarity of two images is quantified as a weighted combination of the textual similarity score and the visual similarity score. In an embodiment, the weighting between the textual similarity score and the visual similarity score is user configurable through a control on the search interface. In one embodiment, the aggregate similarity score is the sum of a weighted visual similarity score and a weighted textual similarity score.

Abstract: Systems and methods use machine learning models with content editing tools to prevent or mitigate inadvertent disclosure and dissemination of sensitive data. Entities associated with private information are identified by applying a trained machine learning model to a set of unstructured text data received via an input field of an interface. A privacy score is computed for the text data by identifying connections between the entities, the connections between the entities contributing to the privacy score according to a cumulative privacy risk, the privacy score indicating potential exposure of the private information. The interface is updated to include an indicator distinguishing a target portion of the set of unstructured text data within the input field from other portions of the set of unstructured text data within the input field, wherein a modification to the target portion changes the potential exposure of the private information indicated by the privacy score.

Abstract: Methods and systems disclosed herein relate generally to increasing visibility of pixel patterns of an image. The system includes a pattern-detection application accessing an image depicting an object. The pattern-detection application determines a set of colors from the transformed image. The pattern-detection application identifies a set of pixels depicting a particular color of the set of colors. For the set of pixels depicting the particular color, the pattern-detection application converts an initial set of pixel values of the set of pixels at an initial color space to another set of pixel values that define the particular color of the set of pixels in another color space. The pattern-detection application modifies one or more values of the other set of pixel values to generate a modified set of pixel values. The modification includes causing the set of pixels visually indicate a simulated color that is different from the particular color.

Abstract: In implementations of item transfer control systems, a computing device implements a transfer system to receive input data describing types of requested items and corresponding quantities of the types of requested items to receive at each of a plurality of destination sites and types of available items and corresponding quantities of the types of available items that are available at each of a plurality of source sites. The transfer system constructs a flow network having a source node for each of the plurality of the source sites and a destination node for each of the plurality of the destination sites. An integral approximate solution is generated that transfers the corresponding quantities of the types of requested items to each of the plurality of the destination sites using a maximum flow solver and the flow network.

Abstract: Systems and methods use machine learning models with content editing tools to prevent or mitigate inadvertent disclosure and dissemination of sensitive data. Entities associated with private information are identified by applying a trained machine learning model to a set of unstructured text data received via an input field of an interface. A privacy score is computed for the text data by identifying connections between the entities, the connections between the entities contributing to the privacy score according to a cumulative privacy risk, the privacy score indicating potential exposure of the private information. The interface is updated to include an indicator distinguishing a target portion of the set of unstructured text data within the input field from other portions of the set of unstructured text data within the input field, wherein a modification to the target portion changes the potential exposure of the private information indicated by the privacy score.

Abstract: Methods, systems, and non-transitory computer readable media are disclosed for utilizing machine learning models to extract digital signals from low-results web queries and generate item demand deficiency predictions for digital item lists corresponding to websites. In one or more embodiments, the disclosed systems identify a low-results query submitted by client devices navigating a website. The disclosed systems generate features for the low-results query and the digital item list to generate a deficiency prediction relative to demand indicated by the low-results query. In some embodiments, the disclosed system utilizes a deficiency prediction model to process the extracted signals and generate a deficiency confidence score corresponding to the low-results query. Based on the deficiency confidence score, the disclosed system can generate and provide demand notifications via one or more graphical user interfaces.

Abstract: A system leverages reinforcement learning techniques to determine distribution of items to listing platforms and search ranking rules for each listing platform. Using historical listing data regarding items listed at one or more listing platforms, a machine learning model generates item interaction data, and a reinforcement learning agent is initialized using the item interaction data. The reinforcement learning agent is trained to optimize a function for selecting item distributions and search ranking rules across listing platforms. At each epoch of a series of epochs, the function is used to select an action including a new distribution of items to listing platforms and new search ranking rules to use at each listing platform. After the action from an epoch is implemented, the reinforcement learning agent updates the function, for instance, based on an impact of the action.

Abstract: An effective stock keeping unit (SKU) management system encodes catalog data into an embedding per catalog item. An embedding space is created by encoding catalog item data into an embedding per catalog item. The embedding is created by generating an index, where a number of rows represents a number of catalog items and a number of columns represents a number of fields associated with each catalog item. The index is then denormalized using customer groups and transformed by compressing the number of columns, to create the embedding space. In some configuration, a machine learning model is trained using catalog data. In the embedding space, item similarity is encoded by clustering catalog SKUs into groups in the embedding space, by placing similarly related items close to each other in the embedding space. Catalog items are then searched for in the embedding, with the closest clusters searched for a particular catalog item.

Abstract: Certain aspects and features of this disclosure relate to chromatic undertone detection. For example, a method involves receiving an image file and producing, using a color warmth classifier, an image warmth profile from the image file. The method further involves applying a surface-image-trained machine-learning model to the image warmth profile to produce an inferred undertone value for the image file. The method further involves comparing, using a recommendation module, and the inferred undertone value, an image color value to a plurality of pre-existing color values corresponding to a database of production images, and causing, in response to the comparing, interactive content including the at least one production image selection from the database of production images to be provided on a recipient device.

Abstract: Methods and systems are provided for facilitating identification of sensitive content. In embodiments described herein, a set of sensitive topics is obtained. Each sensitive topic in the set of sensitive topics can include subject matter that may be deemed sensitive to one or more individuals. Thereafter, the set of sensitive topics is expanded to an expanded set of sensitive topics using a first machine learning model. The expanded set of sensitive topics is used to train a second machine learning model to predict potential sensitive content in relation to input content.

Abstract: The present technology provides for facilitating removal of undesired search results. In one embodiment, a search request including a search term(s) to use for performing a search is obtained. Thereafter, a search query is generated to execute the search. The search query includes the search terms and a removal parameter indicating a particular search result to exclude from search results returned in response to the search request. A set of search results are provided for display via a user device. Such a set of search results can be identified based on execution of the search query and exclude the particular search result.

Abstract: In implementations of item transfer control systems, a computing device implements a transfer system to receive input data describing types of requested items and corresponding quantities of the types of requested items to receive at each of a plurality of destination sites and types of available items and corresponding quantities of the types of available items that are available at each of a plurality of source sites. The transfer system constructs a flow network having a source node for each of the plurality of the source sites and a destination node for each of the plurality of the destination sites. An integral approximate solution is generated that transfers the corresponding quantities of the types of requested items to each of the plurality of the destination sites using a maximum flow solver and the flow network.

Abstract: This disclosure describes one or more implementations of systems, non-transitory computer-readable media, and methods that utilize a consumption cadence model to predict customized consumption cadences for user accounts across a wide variety of consumable items and generate dynamic selectable consumption scheduling options for the consumable items using the customized consumption cadences. For example, the disclosed systems predict a consumption cadence for consuming a consumable item that is customized for a user account that interacted with the consumable item. Additionally, in some embodiments, the disclosed systems utilize collective user behavior from user accounts that are similar to the user account to determine a predicted consumption cadence using a consumption cadence model.

Abstract: An item recommendation system receives a set of recommendable items and a request to select, from the set of recommendable items, a contrast group. The item recommendation system selects a contrast group from the set of recommendable items by applying a recommendation model to the set of recommendable items. The recommendation model includes an item selection model configured to determine an unbiased conversion rate for each item of the set of recommendable items and select a recommended item from the set of recommendable items having a greatest unbiased conversion rate. The recommendation model includes a contrast group selection model configured to select, for the recommended item, a contrast group comprising the recommended item and one or more contrast items. The item recommendation system transmits the contrast group responsive to the request.