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 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: 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 methods for generating embeddings for nodes of a corpus graph are presented. The embeddings correspond to aggregated embedding vectors for nodes of the corpus graph. Without processing the entire corpus graph to generate all aggregated embedding vectors, a relevant neighborhood of nodes within the corpus graph are identified for a target node of the corpus graph. Based on embedding information of the target node's immediate neighbors, and also upon neighborhood embedding information from the target node's relevant neighborhood, an aggregated embedding vector can be generated for the target node that comprises both an embedding vector portion corresponding to the target node, as well as a neighborhood embedding vector portion, corresponding to embedding information of the relevant neighborhood of the target node. Utilizing both portions of the aggregated embedding vector leads to improved content recommendation to a user in response to a query.

Abstract: Systems and methods for efficiently training a machine learning model are presented. More particularly, using information regarding the relevant neighborhoods of target nodes within a body of training data, the training data can be organized such that the initial state of the training data is relatively easy for a machine learning model to differentiate. Once trained on the initial training data, the training data is then updated such that differentiating between a matching and a non-matching node is more difficult. Indeed, by iteratively updating the difficulty of the training data and then training the machine learning model on the updated training data, the speed that the machine learning model reaches a desired level of accuracy is significantly improved, resulting in reduced time and effort in training the machine learning model.

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 methods for recommending content to an online service subscriber are presented. For each subscriber, content items that were the subject of the subscriber's prior interactions are projected, via associated embedding vectors, into a content item embedding space. The content items, via their projections into the content item embedding space, are clustered to form a plurality of interest clusters for the subscriber. A representative embedding vector is determined for each interest cluster, and a plurality of these embedding vectors are stored as the representative embedding vectors for the subscriber. The online service, in response to a request for recommended content for a subscriber, selects a first representative embedding vector associated with the subscriber and identifies a new content item from a corpus of content items according to a similarity measure between the first representative embedding vector and an embedding vector associated with the new content item.

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 recommending content to an online service subscriber are presented. For each subscriber, content items that were the subject of the subscriber's prior interactions are projected, via associated embedding vectors, into a content item embedding space. The content items, via their projections into the content item embedding space, are clustered to form a plurality of interest clusters for the subscriber. A representative embedding vector is determined for each interest cluster, and a plurality of these embedding vectors are stored as the representative embedding vectors for the subscriber. The online service, in response to a request for recommended content for a subscriber, selects a first representative embedding vector associated with the subscriber and identifies a new content item from a corpus of content items according to a similarity measure between the first representative embedding vector and an embedding vector associated with the new content item.

Abstract: This disclosure describes systems and methods that facilitate reducing a data set that may be used to construct a node graph. For example, the data set may include collections, representations, and associations between the collections and the representations. Topic scores may be determined for the representations, and diversity scores for each collection may be determined based on the topic scores of representations that are associated with the respective collection. If the diversity score is too high, then the collection and its associations are excluded from being incorporated into a node graph that is subsequently constructed from the data set. Topic scores may also be determined for collections in the data set based on the topic scores of representations that are associated with each collection.

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 generating embeddings for nodes of a corpus graph are presented. More particularly, embedding information of a target node may be based on the node itself, as well as related, relevant nodes to the target node within a corpus graph. The information of various nodes among the relevant nodes to the target node can be used to weight or influence the embedding information. Disclosed systems and methods include generating neighborhood embedding information for a target node, where the neighborhood embedding information includes embedding information from neighborhood nodes of the target node's relevant neighborhood, and where certain nodes having more relevance to the target node can be weighted to influence the generation of the neighborhood embedding information over nodes having less relevance to the target node.

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 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: 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: This disclosure describes systems and methods that facilitate generating recommendations by traversing a node graph. For example, recommendations may be generated for a node in the node graph by running a plurality of walks through the node graph and tracking the nodes visited by the walks. For example, a visit count or score may be maintained and/or updated for each node as the walks traverse through the node graph. The walks may be terminated after a defined amount of nodes in the node graph have visit counts or scores that satisfy a criterion. Content corresponding to nodes with the highest visit counts or scores may be recommended.

Abstract: This disclosure describes systems and methods that facilitate generating recommendations by traversing a node graph. For example, a cluster of nodes in a node graph may be determined for a target node in the node graph based at least in part on a proximity of the nodes in the cluster to the target node in the node graph. A plurality of walks through a node graph may be tracked, and a visit count or score for the target node may be increased for each visit to a node in the cluster. The walks may be terminated after a defined amount of walks have been performed or a defined amount of nodes in the node graph have scores that satisfy a criterion. Content corresponding to nodes may be recommended based on scores or visit counts.

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 generating embeddings for nodes of a corpus graph are presented. More particularly, embedding information of a target node may be based on the node itself, as well as related, relevant nodes to the target node within a corpus graph. The information of various nodes among the relevant nodes to the target node can be used to weight or influence the embedding information. Disclosed systems and methods include generating neighborhood embedding information for a target node, where the neighborhood embedding information includes embedding information from neighborhood nodes of the target node's relevant neighborhood, and where certain nodes having more relevance to the target node can be weighted to influence the generation of the neighborhood embedding information over nodes having less relevance to the target node.

Abstract: Systems and methods for efficiently training a machine learning model are presented. More particularly, using information regarding the relevant neighborhoods of target nodes within a body of training data, the training data can be organized such that the initial state of the training data is relatively easy for a machine learning model to differentiate. Once trained on the initial training data, the training data is then updated such that differentiating between a matching and a non-matching node is more difficult. Indeed, by iteratively updating the difficulty of the training data and then training the machine learning model on the updated training data, the speed that the machine learning model reaches a desired level of accuracy is significantly improved, resulting in reduced time and effort in training the machine learning model.