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: Described are systems and methods for determining session intent of a user. Different users can use a network-based application in many different ways based on, for example, the user's purpose for using the application, the device on which the user is executing the application, the user themselves, date, time, location, etc. Through the collection of user activities during a user session, the intent of a user session can be determined. Once determined, content provided through the application can be further personalized to correspond to the determined session intent.

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: 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. 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 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: A system and method that facilitates and effectuates making an inference related to objects of interest within a context. A web projection component can generate sub graphs by projecting objects of interest onto a web graph representing a superset or partially overlapping set of objects to the objects of interest. An inference component can provide inferences relating to the quality of the objects of interest, adjustments to the context, and patterns recognized from graphical properties of the sub graphs.

Abstract: A system and method that facilitates and effectuates making an inference related to objects of interest within a context. A web projection component can generate sub graphs by projecting objects o interest onto a web graph representing a superset or partially overlapping set of objects to the objects of interest. An inference component can provide inferences relating to the quality of the objects of interest, adjustments to the context, and patterns recognized from graphical properties of the sub graphs.

Abstract: A browser sessions search tool allows a user to search through records of previously viewed resource pages, even across multiple browser sessions. Each visit to a resource page results in creation of a visit data structure that references the resource page content. A rich assortment of data is recorded for each visited resource page, such as full text and layout data, time of access and navigation type that lead to the page. In one embodiment, color aspects of each resource page are stored to assist in a search, although other recorded resource page characteristics may additionally or alternatively include without limitation other visual, audio, navigational, or metadata aspects.

Abstract: A resource browser session navigator includes a navigation manager module and a resource page manager module. The navigation manager module detects navigation events indicating visits to a resource pages. The resource page manager module populates a visit data structure representing the visits to the resource page and references in the visit data structure a page data structure that references content of the resource pages.

Abstract: A browser session navigation tool allows a user to browse previously viewed resource pages during a browser session. Each visit to a resource page results in creation of a visit data structure that references (directly or indirectly) the resource page content. The previously viewed resource pages are graphically represented by navigationally related visit nodes displayed in one or more trails or trees, which graphically illustrate the navigation from resource page to resource page. Resource page content may be displayed in the visit nodes, such as a thumbnail image of the resource page. In contrast to typical browser history lists, navigation branches are not truncated. Instead, all previously viewed resource pages are recorded in archive data storage and are displayed in linear trails and branching tree structures of visit nodes.

Abstract: A browser sessions search tool allows a user to search through records of previously viewed resource pages, even across multiple browser sessions. Each visit to a resource page results in creation of a visit data structure that references the resource page content. A rich assortment of data is recorded for each visited resource page, such as full text and layout data, time of access and navigation type that lead to the page.