Abstract: There is provided mechanisms for generating a security configuration profile for a network entity. A method is performed by a security configuration entity. The method comprises generating the security configuration profile for the network entity based on network entity information, deployment information, and feedback information for a previously generated security configuration profile. The method comprises determining, based on calculating a risk score for the generated security configuration profile, whether the security configuration profile is to be provided towards the network entity or not. The method comprises generating feedback information for the security configuration profile based on the risk score, the network entity information, and the deployment information.

Abstract: The disclosed embodiments include computer-implemented systems and methods that facilitate a prediction of future occurrences of redemption events using adaptively trained artificial intelligence processes. For example, an apparatus may generate an input dataset based on elements of first interaction data associated with a first temporal interval. Based on an application of a trained artificial intelligence process to the input dataset, the apparatus may generate output data representative of a predicted likelihood of an occurrence of each of a plurality of targeted events during a second temporal interval. The apparatus may also transmit at least a portion of the output data and explainability data associated with the trained artificial intelligence process to a computing system, which may perform operations based on the portion of the output data and the explainability data.

Abstract: One embodiment is a method executed by a computer system that applies collaborative filtering to provide a recommendation to a user. The method includes retrieving a binary matrix that includes rows and columns of binary data for preferences of users on items; applying a neighborhood-based approach to convert the binary matrix into a neighbor-score matrix; applying a factorization to approximate the neighbor-score matrix with a product of lower rank matrices; calculating a user factor and an item factor based on the factorization; calculating scores for user-item pairs by computing a dot product between the user factor and the item factor; sorting the scores of the user-item pairs to generate the recommendation to the user; and providing the recommendation to a general-purpose computer of the user.

Abstract: One embodiment is a method executed by a computer system that applies collaborative filtering to provide a recommendation to a user. The method includes retrieving a binary matrix that includes rows and columns of binary data for preferences of users on items; applying a neighborhood-based approach to convert the binary matrix into a neighbor-score matrix; applying a factorization to approximate the neighbor-score matrix with a product of lower rank matrices; calculating a user factor and an item factor based on the factorization; calculating scores for user-item pairs by computing a dot product between the user factor and the item factor; sorting the scores of the user-item pairs to generate the recommendation to the user; and providing the recommendation to a general-purpose computer of the user.

Abstract: In one embodiment, a data structure comprises: a primary index comprising one or more position-block references; and one or more position blocks sequentially following the primary index, wherein: each one of the position-block references corresponds to one of the position blocks; and each one of the position blocks comprises: a secondary index comprising one or more position-data references; and one or more sets of positions sequentially following the secondary index, wherein each one of the position-data references corresponds to of one of the sets of positions in the position block. In one embodiment, an instance of the data structure is stored in a computer-readable memory and accessible by an application executed by a process.

Abstract: In one embodiment, a data structure comprises: a primary index comprising one or more position-block references; and one or more position blocks sequentially following the primary index, wherein: each one of the position-block references corresponds to one of the position blocks; and each one of the position blocks comprises: a secondary index comprising one or more position-data references; and one or more sets of positions sequentially following the secondary index, wherein each one of the position-data references corresponds to of one of the sets of positions in the position block. In one embodiment, an instance of the data structure is stored in a computer-readable memory and accessible by an application executed by a process.

Abstract: One embodiment is a method executed by a computer system that applies collaborative filtering to provide a recommendation to a user. The method includes retrieving a binary matrix that includes rows and columns of binary data for preferences of users on items; applying a neighborhood-based approach to convert the binary matrix into a neighbor-score matrix; applying a factorization to approximate the neighbor-score matrix with a product of lower rank matrices; calculating a user factor and an item factor based on the factorization; calculating scores for user-item pairs by computing a dot product between the user factor and the item factor; sorting the scores of the user-item pairs to generate the recommendation to the user; and providing the recommendation to a general-purpose computer of the user.

Abstract: A method and apparatus for using network traffic logs for search enhancement is disclosed. According to one embodiment, network usage is tracked by generating log files. These log files among other things indicate the frequency web pages are referenced and modified. These log files or information from these log files can then be used to improve document ranking, improve web crawling, determine tiers in a multi-tiered index, determine where to insert a document in a multi-tiered index, determine link weights, and update a search engine index.

Abstract: A method and apparatus for using network traffic logs for search enhancement is disclosed. According to one embodiment, network usage is tracked by generating log files. These log files among other things indicate the frequency web pages are referenced and modified. These log files or information from these log files can then be used to improve document ranking, improve web crawling, determine tiers in a multi-tiered index, determine where to insert a document in a multi-tiered index, determine link weights, and update a search engine index.

Abstract: A method and apparatus for using network traffic logs for search enhancement is disclosed. According to one embodiment, network usage is tracked by generating log files. These log files among other things indicate the frequency web pages are referenced and modified. These log files or information from these log files can then be used to improve document ranking, improve web crawling, determine tiers in a multi-tiered index, determine where to insert a document in a multi-tiered index, determine link weights, and update a search engine index.

Abstract: In one embodiment, a tree traversal sequentially processes every level of two or more binary trees until reaching a leaf node for each the binary trees. Processing each level of the binary trees comprises: determining whether each traversed node of the remaining binary trees at the level is a non-leaf node or a leaf node; for the traversed leaf nodes at the level, remove the binary trees to which the leaf nodes belong from the remaining binary trees; and iteratively evaluating the traversed non-leaf nodes at the level of every n remaining binary trees until the non-leaf nodes at the level of all of the remaining binary trees are evaluated, where n?2 and evaluating the non-leaf nodes of each n binary trees comprises concurrently evaluating the n non-leaf nodes of the n binary trees to determine to which one of the two child nodes of each of the n non-leaf nodes to traverse.

Abstract: A method and apparatus for using network traffic logs for search enhancement is disclosed. According to one embodiment, network usage is tracked by generating log files. These log files among other things indicate the frequency web pages are referenced and modified. These log files or information from these log files can then be used to improve document ranking, improve web crawling, determine tiers in a multi-tiered index, determine where to insert a document in a multi-tiered index, determine link weights, and update a search engine index.

Abstract: A method and apparatus for using network traffic logs for search enhancement is disclosed. According to one embodiment, network usage is tracked by generating log files. These log files among other things indicate the frequency web pages are referenced and modified. These log files or information from these log files can then be used to improve document ranking, improve web crawling, determine tiers in a multi-tiered index, determine where to insert a document in a multi-tiered index, determine link weights, and update a search engine index.

Abstract: A method and apparatus for using network traffic logs for search enhancement is disclosed. According to one embodiment, network usage is tracked by generating log files. These log files among other things indicate the frequency web pages are referenced and modified. These log files or information from these log files can then be used to improve document ranking, improve web crawling, determine tiers in a multi-tiered index, determine where to insert a document in a multi-tiered index, determine link weights, and update a search engine index.

Abstract: A method and apparatus for using network traffic logs for search enhancement is disclosed. According to one embodiment, network usage is tracked by generating log files. These log files among other things indicate the frequency web pages are referenced and modified. These log files or information from these log files can then be used to improve document ranking, improve web crawling, determine tiers in a multi-tiered index, determine where to insert a document in a multi-tiered index, determine link weights, and update a search engine index.