Abstract: This disclosure describes systems and methods for matching user provided images that include representations of items with sellers of those items. A management service, as described herein, may provide a web site where users can post images, view images, share images, correspond with other users, etc. The management service may identify items represented in the images and determine one or more sellers that offer those items for sale. When another user requests to view the user provided image, the image, seller information identifying the seller determined to sell the item represented in the image, and/or a purchase control that may be selected by a user to initiate a purchase with the seller is presented.

Abstract: Systems and methods for determining one or more topics that may be associated and/or grounded with a node of a taxonomy to facilitate the creation and/or modification of the taxonomy. The one or more topics can be determined by generating two layers of associations and/or groundings. In a first layer, tokens can be associated and/or grounded in a corpus of queries, and in a second layer, topics can be associated and/or grounded in the tokens. The topics can then be associated with and/or grounded in nodes of a taxonomy which can facilitate access to content items stored and maintained by an online service. Further, in exemplary implementations where the content items include associations and/or mappings to the corpus of queries, the nodes of the taxonomy (which are associated with one or more topics) can be transitively mapped to the content items.

Abstract: This disclosure describes, in part, systems and methods that enable users to manage, search for, share and discover objects based on a context of the object from the user's perspective. The same object may have vastly different meanings (context) to different individuals based on how they experience the object. Rather than managing objects solely based on information about the object, the implementations described allow users to specify a context for the object and manage objects based on that context. In addition, external sources may provide supplemental information about objects and/or representations of objects.

Abstract: Disclosed are systems and methods that generate a natural language prompt that is configured to be processed by a generative model, such as a large language model (LLM), and includes certain user information to facilitate the determination and/or generation of customized content for users of an online platform. For example, textual information associated with certain user information may be extracted and aggregated and incorporated into one or more natural language prompts, which may be processed by a generative model, such as an LLM, to generate a particular output based on the type of customized content being sought and/or generated for the user. The output may then be processed to determine and/or generate the customized content or the user.

Abstract: Systems and methods for generating embeddings for nodes of a corpus graph are presented. More particularly, operations for generation of an aggregated embedding vector for a target node is efficiently divided among operations on a central processing unit and operations on a graphic processing unit. With regard to a target node within a corpus graph, processing by one or more central processing units (CPUs) is conducted to identify the target node's relevant neighborhood (of nodes) within the corpus graph. This information is prepared and passed to one or more graphic processing units (GPUs) that determines the aggregated embedding vector for the target node according to data of the relevant neighborhood of the target node.

Abstract: Systems and methods for determining whether a linked content page may include spamming, malicious, and/or otherwise undesirable content. The linked content page may be crawled, scraped, and/or parsed to extract various information associated with the text, media items, and/or structure of the linked content page. The text, media, and/or structure information may be analyzed and processed to generate one or more textual features, media features, and/or structural features, which may then be processed by a trained machine learning model to determine whether the content page includes spamming, malicious, and/or otherwise undesirable content.

Abstract: Described are systems and methods to determine a content item recommendation that is included in a content item bid request, recommending to a third party, a third party content item to include in a content item bid that is responsive to the bid request. The content item recommendation may indicate a particular third party product or third party content item that is predicted to perform well in a content item slot and for a specific user without disclosing user information to the third party.

Abstract: Systems and methods for identifying relevant content within a corpus of visual content items in response to a user's text-based query are presented. In response to a text-based query, the query is mapped to a most-engaged content item of the corpus of visual content items included in responses to the query from a plurality of users. At least one text-based term associated with the most-engaged content item is identified and combined with the query from an expanded query. The expanded query is mapped to an interest node of an interest taxonomy and content items associated with the mapped interest node are identified. At least some of the content items associated with the mapped interest node are selected and returned as response content to the received query.

Abstract: Described are systems and methods of identifying complementary image segments and generating collages of the complementary image segments. Based on an initial image segment, the complementary image segments may first be determined. Then, a layout of the collage may be determined based on the initial image segment and the complementary image segments. The collage may then be generated using the initial image segment, the complementary image segments, and the layout. The origin information, such as the source image, source image location, etc., from which the extracted image segment is generated is maintained as metadata so that interaction with the extracted image segment on the collage can be used to determine and/or return to the origin of the extracted image segment. Collages may be updated, shared, adjusted, etc.

Abstract: Disclosed are systems and methods that process a dataset to determine data anomalies in the dataset. The process may receive a query to create the dataset. At least one known data anomaly may be identified in the dataset. An algorithm that models a pattern of the dataset may be selected. The dataset, known data anomaly, and/or algorithm may be sent to a Large Language Model (LLM) with instructions to determine configuration information for data anomaly detection including at least one threshold that indicates additional anomalies in the dataset. The algorithm may create a reference dataset that is compared to the dataset to determine deviations. The threshold may determine which deviations indicate additional anomalies. The LLM may send configuration data, including at least the threshold, to an anomaly detection application, which may be configured with the configuration data and used to determine data anomalies in other, similar, datasets generated with the query or a similar query.

Abstract: Systems and methods for identifying relevant content within a corpus of visual content items in response to a user's text-based query are presented. In response to a text-based query, the query is mapped to a most-engaged content item of the corpus of visual content items included in responses to the query from a plurality of users. At least one text-based term associated with the most-engaged content item is identified and combined with the query from an expanded query. The expanded query is mapped to an interest node of an interest taxonomy and content items associated with the mapped interest node are identified. At least some of the content items associated with the mapped interest node are selected and returned as response content to the received query.

Abstract: This disclosure describes systems and methods that facilitate purchase of objects from merchants. For example, a user may browse a website available from an object management service and identify objects that they desire to purchase. Rather than having to locate the seller of those objects to make a purchase, the implementations described herein facilitate a connection between the user and the merchant so that the merchant's sales are increased and the user is provided an efficient and safe shopping experience.

Abstract: Described are systems and methods for determining complementary and/or matching objects based on an input query object. The described systems and methods can generate an embedding representative of the provided object, which can be transformed to generate a style embedding by a trained system, such as a machine learning system. The style embedding can then be used to identify one or more complementary objects from a corpus of classified objects. Aspects of the present disclosure also relate to creation of the training dataset, as well as training the machine learning system.

Abstract: Systems and methods for generating user notifications to a set of users of a social networking service is presented. For each user of a set of users of the social networking service, one or more machine learning models selects an optimal notification channel, an optimal notification template, and optimal personalization content for configurable elements of a selected notification template. Each of these determinations/selections is made according to and based on a likelihood of increased user engagement with the social networking service. Upon determining the notification channel, notification template, and personalizations to the template, the notification is generated and sent to the corresponding user.

Abstract: Systems and methods for identifying content for an input query are presented. A mapping model is trained to map elements of an input query embedding vector for a received query into one or more elements of a destination embedding vector. In response to receiving an input query, an input query embedding vector is generated that projects into an input query embedding space. The input query embedding vector is processed by the mapping model to map the input query embedding vector into one or more elements of a destination embedding vector in a destination embedding space, resulting in a partial destination embedding vector. Items of a corpus of content are projected into the destination embedding space and the partial destination embedding vector is also projected into the destination embedding space. A similarity measure determines the most-similar items to the partial destination embedding vector and at least some of the most-similar items are returned in response to the input query.

Abstract: Systems and methods are presented for training a second machine learning model according to aspects of a trained first machine learning model. Processing features utilized by a training framework to train the first machine learning model are identified, and at least some of the processing features are combined with an initial set of training features to form updated training features. The updated training features are presented to a user for customization, resulting in customized training features. An executable training framework is configured with the customized training features and executed to train the second machine learning model.

Abstract: Described is a system and method for enabling visual search for information. With each selection of a search term, additional search terms are dynamically selected and presented to the user in conjunction with results matching the currently selected search terms. Likewise, a selected search term may be tokenized and a graphical token presented to the user to represent the selected search term.

Abstract: Described is a system and method for enabling dynamic selection of a search input. For example, rather than having a static search input box, the search input may be dynamically positioned such that it encompasses a portion of displayed information. For example, a user may touch a touch-based display using two fingers to invoke the dynamic search input and then determine a size and a position of the dynamic search input by moving their fingers on the display. An image segment that includes a representation of the encompassed portion of the displayed information is generated and processed to determine an object represented in the portion of the displayed information. Additional images with visually similar representations of objects are then determined and presented to the user.

Abstract: Disclosed are implementations that enable the linking or connection of objects and different scenes in which those objects are represented. For example, a corpus of scenes (e.g., digital images) that include a representation of one or more objects may be processed using the disclosed implementations to segment from those scenes the individual objects represented in those scenes. The disclosed implementations may further determine clusters of visually similar object segments and form object clusters for those object segments. The scenes that include those object segments are also linked to the object cluster. With scenes linked to different object clusters, a user may select one or more query objects or a query scene and be presented with other scenes that include visually similar objects, even though the overall scenes may be visually different.

Abstract: A computer system extracts product data from a website and correlates product records from multiple sources to one another as corresponding to the same product. A website is crawled efficiently by rendering webpages using a virtual browser that ignores blacklisted elements, extracts data from objects without rendering, and suppressing retrieval of remote resources. Data is extracted according to engine control statements including a selector and extractor. A website may be crawled repeatedly and changes in extracted data may be detected and flagged. Engine control statements may be automatically changed in response to detecting a change in the configuration of the website. Images of product records may be correlated with one another by first comparing text of the product records and selecting images for comparison based on composition. Images are compared using a machine learning model. Images determined to be similar may be presented to a human for a correlation decision.