Abstract: Disclosed are systems and methods that authenticate non-fungible tokens (“NFT”) and/or digital data represented by or pointed to by an NFT. In some implementations, authentication may be with respect to an existing NFT. In other implementations, authentication may be with respect to an NFT that is being created. The disclosed implementations may compare a candidate and/or candidate NFT data with existing NFTs and/or existing NFT data to determine if the candidate NFT and/or candidate NFT data is similar to other NFTs and/or other NFT data of another NFT, which may exist on any of many different blockchains.

Abstract: Described are systems and methods for generating recommendation campaigns that optimize for both a desired short-term user behavior and a desired long-term user behavior. In comparison to existing techniques that focus on targeting advertisements or recommendations to specific individuals with a single goal of receiving an interaction with the advertisement from that individual (i.e., a desired short-term behavior), the disclosed implementations consider the long-term user behavior, such as increased visits to a website during a long-term rage, and generate a recommendation campaign that also optimizes for that desired long-term user behavior.

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: A system for and a method of using user-entered information to return more meaningful information in response to Internet search queries are disclosed. A method in accordance with the disclosed subject matter comprises managing a database in response to multiple user inputs and displaying search results from the database in response to a search query. The search results include a results list and supplemental data related to the search query. Managing the database includes, among other things, re-ranking elements in the results list, storing information related to relevancies of elements in the results list, blocking a link in the results list, storing links to documents related to the search query, or any combination of these. The supplemental data include descriptions of or indices to one or more concepts related to the search query.

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: 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, for example, by a client device from an online service in response to a query, a request for content items, and the like. 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 each content item. The playability score for each content item may be aggregated with the relevance and/or user engagement score associated with each content item 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 are presented for inferring an embedding vector of an item of a first type into the embedding space. Upon receiving a first time for which there is no embedding vector, documents of a document corpus that include (co-occurrence) both the received item and other items of the same type are identified. Of those other items that have embedding vectors, those embedding vectors are retrieved and averaged. The resulting averaged embedding vector is established as an inferred embedding vector for the received item.

Abstract: Systems and methods for providing auto-completion options to input characters are presented. In response to receiving input characters, a plurality of items of content (that are non-textual items of content) of a corpus of content are identified. These items of content are clustered into n clusters of content according to similarities among the items of content. From the items of content of each cluster, a descriptive title is determined for the cluster. This descriptive title is an auto-completion option for the cluster. The descriptive titles/auto-completion options are provided in response to receiving the input characters.

Abstract: The described implementations enable a seller to sell items through multiple e-commerce channels without having to maintain independent merchant accounts at each channel. For example, a seller may sell items directly and through a management service. When a user request to purchase an item from the seller through the management service is received, the management service sends the purchase information to the seller so that the seller can complete the purchase as if the purchase were being made directly with the seller. Upon completion of the purchase, the seller provides a confirmation back to the management service and provides the item directly to the user.

Abstract: Described are systems and methods for establishing a unit group dictionary based on user provided annotations. The unit group dictionary may be used to identify relationships between multiple items in a corpus. Those relationships may facilitate the display of object identifiers and/or other aspects used and/or provided by the object management service.

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 responding to a subscriber's text-based request for content items are presented. In response to a request from a subscriber, word pieces are generated from the text-based terms of the request. A request embedding vector of the word pieces is obtained from a trained machine learning model. Using the request embedding vector, a set of content items, from a corpus of content items, is identified. At least some content items of the set of content items are returned to the subscriber in response to the text-based request for content items.

Abstract: An online host for conducting a promotion campaign is presented. Online behaviors of subscribers exposed by the promotion campaign are tracked for determining conversion counts for the promotion campaign. For exposed subscribers whose online behaviors are not sufficiently available to the online host to determine conversion counts (non-measurable subscribers), a machine learning model is trained to predict conversion counts based on online behaviors that are conducted on internet locations under control of the online service, and further trained to determine an estimated error rate regarding the predicted conversion counts.

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: Described are systems and methods to determine hair patterns presented in content items. The determined hair patterns may be associated with the content items to facilitate indexing, filtering, etc. of the content items based on the determined hair patterns. In exemplary implementations, a corpus of content items may be associated with an embedding vector that includes a binary representation of the content item. The embedding vectors associated with each content item can be provided as inputs to a trained machine learning model, which can process the embedding vectors to determine one or more hair patterns presented in each content item while eliminating the need for performing image pre-processing prior to determination of the hair pattern(s) presented in the content item.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for providing content. One of the methods includes providing a plurality of image content items to an application interface of a client device for presentation; receiving a user selection of a particular image content item of the plurality of presented image content items; and responsive to the selection, providing a combination of native content and third party content associated with the selected image content item, wherein the native content includes a close up view of the selected image content item and the third party content includes a third party webpage.

Abstract: Systems and methods for responding to a subscriber's text-based request for content items are presented. In response to a request from a subscriber, word pieces are generated from the text-based terms of the request. A request embedding vector of the word pieces is obtained from a trained machine learning model. Using the request embedding vector, a set of content items, from a corpus of content items, is identified. At least some content items of the set of content items are returned to the subscriber in response to the text-based request for content items.

Abstract: Described is a system and method for enabling visual search for information. With each selection of an object included in an image, additional images that include visually similar objects are determined and presented to the user.

Abstract: This disclosure describes systems and methods that facilitate the generation of recommendations by traversing a graph. Walks that traverse the graph may be initiated from a plurality of different nodes in the node graph. In order to give greater or lesser weight to particular nodes, the walks may have different lengths depending on the nodes from which they are initiated, or an unequal amount of walks may be distributed between nodes from which walks are initiated. A plurality of walks through a node graph may be tracked, and visit counts or scores for nodes in the node graph may be determined. For example, scores may be increased for nodes that are visited by a walk initiated from a first node and a second walk initiated from a second node, or scores may be decreased for nodes that are not visited by a first walk initiated from a first node and a second walk initiated from a second node. Content corresponding to nodes may be recommended based on the scores or visit counts.

Abstract: Systems and method for determining a topic cohesion measurement between a content item and a hyperlinked landing page are presented. In one embodiment, a plurality of content item signals is generated for the content item and a corresponding plurality of signals are generated for the hyperlinked landing page. An analysis of the corresponding signals is conducted to determine a measurement of topic cohesion, a topic cohesion score, between the content item and the hyperlinked landing page. A cohesion predictor model is trained to generate the predictive topic cohesion score between an input content item and a hyperlinked landing page. Upon a determination that the topic cohesion score is less than a predetermined threshold, remedial actions are taken regarding the hyperlink of the content item. Alternatively, positive actions may be carried out, including promoting the content item to others, associating advertisements with the content item, and the like.