Abstract: Disclosed are systems and methods to enhance user engagement and personalization on digital platforms by employing Generative Artificial Intelligence (GenAI) techniques to suggest new nodes for a taxonomy. The process involves a GenAI technique to extract hypernyms from search query terms and a Retrieval-Augmented Generation (RAG) technique to expand a seed concept (i.e., node in a taxonomy), retrieve hyponyms (i.e., sub-categories) and come up with new node suggestions. This structured approach enables the automated extension of a taxonomy that enhances access to items maintained by an online service. The taxonomy nodes can be mapped to items, improving search accuracy and user interaction.

Abstract: Described are systems and methods for providing a multi-tasked trained machine learning model that may be configured to generate product embeddings from multiple types of product information. The exemplary product embeddings may be generated for a corpus of products (e.g., products included in a product catalog, etc.) based on both image information and text information associated with each respective product. Accordingly, the generated product embeddings may be compatible with learned representations of the different types of product information (e.g., image information, text information, etc.) and may be used to create a product index, which can be used to determine and serve product recommendations in connection with multiple different recommendation services that may be configured to receive different types of inputs (e.g., a single image, multiple images, text-based information, etc.).

Abstract: Disclosed are systems and methods that connect an input image, such as an individual product image, to lifestyle images that include multiple products, and then to one or more complementary product images. For example, when a user provides or selects a product image, the disclosed implementations determine one or more lifestyle images of multiple products that include the product or include another, visually similar, product. Still further, the disclosed implementations may also determine, for each of the multiple products in the lifestyle image, a set of complementary product images that may work well together and with the product in the input image.

Abstract: Disclosed are systems and methods for unified content item ranking across multiple output channels that provide personalized content item recommendations while adapting to different output channel contexts. A machine learning model receives four sets of data for each candidate content item: user engagement history data, content item popularity data, content item information data, and contextual relevance data. The contextual relevance data includes both a query context associated with an output channel and a content relevance based on existing content. The model generates engagement scores for multiple types of engagement actions, which are then weighted according to output channel-specific parameters to produce final ranking scores.

Abstract: System and methods are presented for associating a user-posted content item with an interest node of an interest taxonomy. A corpus of content items and an interest taxonomy are maintained. The interest taxonomy comprises interest nodes organized in a hierarchical organization, each node having a text label descriptive of the interest node. Additionally, the content items of the corpus are associated with one or more interest nodes of the interest taxonomy. Upon receiving a user-posted content item, feature sets of the received content item are generated, these feature sets based on features and/or aspects of the received content item. After generating at least one feature set, the at least one feature set is provided to an interest prediction model that generates candidate interest nodes for the user-posted content item. At least some of the candidate interest nodes are associated with the user-posted content item in the corpus.

Abstract: Described are systems and methods for determining and/or generating a queue of content items to improve the playback experience of the content items for a user. The content items may be obtained by a client device from an online service in response to a query, a request for content items, etc. Relevance rankings and/or scores associated with the content items may be replaced and/or augmented with a playability score, which can represent a quality of the playback experience associated with the content item. The playability score may be aggregated with the relevance and/or user engagement score to determine an overall playback score for each content item. The content items may be ranked, ordered, arranged, and/or presented in accordance with the overall playback scores associated with the content items to facilitate an improved playback experience for a user associated with the client device.

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 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: 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.