Abstract: Systems, methods, and non-transitory computer-readable media can determine one or more respective topics of interest for at least some users of a social networking system. At least some of the topics can be propagated to at least a first user, wherein the propagated topics were determined to be of interest to users that follow the first user in the social networking system. At least one topic from the propagated topics for which the first user is a topical authority is determined.

Abstract: Systems, methods, and non-transitory computer-readable media can determine one or more respective topics of interest for at least some users of a social networking system. At least some of the topics can be propagated to at least a first user, wherein the propagated topics were determined to be of interest to users that follow the first user in the social networking system. At least one topic from the propagated topics for which the first user is a topical authority is determined.

Abstract: The present disclosure is directed to positioning a new item in search results that may additionally include previously-released items. Items in a search result may be ranked by popularity scores. In embodiments of the present disclosure, a new item may receive a popularity score boost to compensate for that item's relative lack of history in comparison to the previously-released items. Embodiments of the present disclosure utilize outside data sources to rank the items. Outside data sources may include social media, knowledge bases, and like platforms to leverage available outside data to complement internal data.

Abstract: A computer-implemented method is disclosed for cutting off listings or search results. In the method, a computer system may receive a query from a client computer. In response to the query, the computer system may compile listings ranked according to corresponding relevance scores. Thereafter, the computer system may calculate a cutoff score that is less than a maximum relevance score corresponding to the listings. For example, the computer system may calculate a cutoff score by multiplying a maximum relevance score corresponding to the listings by a weighting. In selected embodiments, the weighting may be greater than 0.05 and less than 0.5. The computer system may then serve to the client computer as a response to the query a subset of the listings. The subset may comprise those listings corresponding to a relevance score greater than the cutoff score.

Abstract: Techniques for providing improved search results for queries are provided. The techniques include a server receiving a query from a customer and identifying a number of product listings corresponding to the query according to relevance scores. The techniques also include identifying a signal associated with each of the product listings, calculating a signal score from a relevance score and a signal value for each of the product listings, creating query results comprising the number of product listings ordered according to the signal score, and transmitting the query results to the customer.

Abstract: The present disclosure is directed to positioning a new item in search results that may additionally include previously-released items. Items in a search result may be ranked by popularity scores. In embodiments of the present disclosure, a new item may receive a popularity score boost to compensate for that item's relative lack of history in comparison to the previously-released items. Embodiments of the present disclosure utilize outside data sources to rank the items. Outside data sources may include social media, knowledge bases, and like platforms to leverage available outside data to complement internal data.

Abstract: A system is disclosed for determining the popularity of elements within categorized data according to a category to which they pertain based on the popularity of element attributes. The categorized data may be a list of products, where the elements may be products classified by product categories and the attributes may be brand names. Key words may be associated with such brands and/or their products. The key words may be words used when referring to the brands and/or their products with respect to their product category. These key words may be applied to additional data from an external source providing an on-line service, where service user-text is recorded in the additional data. Popularity scores may then be assigned to the products based on indications of references to the brands and/or their products, with respect to their product category, derived from the application of the key words to the additional data.

Abstract: A computer-implemented method is disclosed for sorting listings or search results. In the method, a computer system may receive a query from a client computer. The computer system may respond to the query with listings ranked according to corresponding relevance scores. Thereafter, the computer system may receive a request to sort the listings according to some numeric attribute (e.g., price) corresponding thereto. Accordingly, the computer system may calculate a modified sorting criterion for each of the listings. Each modified sorting criterion may be weighted by both the original relevance score and the numeric attribute of the corresponding listing. The computer system may then serve, to the client computer, the listings ranked according to the modified sorting criteria.

Abstract: Policies defining the entitlements to be assigned to a new identity joining a role are automatically generated. An automatic policy assigns a new identity the entitlements commonly owned by a predetermined number of identities in the role, which may be all of the role identities. A conditional policy recommends that a new identity be assigned the non-commonly-owned entitlements associated with the role identity whose non-entitlement attributes most closely match the non-entitlement attributes of the new identity. This may be automatically determined by iterating through a vector that maps the non-commonly-owned entitlements with the non-entitlement attributes of each role identity, comparing the non-entitlement attributes of the new identity to find the closest match. The non-commonly-owned entitlements of that identity are then recommended to be assigned to the new identity, upon approval.

Abstract: A method for implementing organization-specific policy during establishment of an autonomous connection between computer resources includes evaluating a relative priority between default credentials and alternative credentials; and using the highest priority credentials to establish a connection between the computer resources. The alternative credentials are based on organization-specific policy and provide for autonomous connections between computer resources differently than the default credentials.

Abstract: An organization-specific policy is implemented during establishment of an autonomous connection between computer resources includes evaluating a relative priority between default credentials and alternative credentials; and using the highest priority credentials to establish a connection between the computer resources. The alternative credentials are based organization-specific policy and provide for autonomous connections between computer resources differently than the default credentials.

Abstract: Policies defining the entitlements to be assigned to a new identity joining a role are automatically generated. An automatic policy assigns a new identity the entitlements commonly owned by a predetermined number of identities in the role, which may be all of the role identities. A conditional policy recommends that a new identity be assigned the non-commonly-owned entitlements associated with the role identity whose non-entitlement attributes most closely match the non-entitlement attributes of the new identity. This may be automatically determined by iterating through a vector that maps the non-commonly-owned entitlements with the non-entitlement attributes of each role identity, comparing the non-entitlement attributes of the new identity to find the closest match. The non-commonly-owned entitlements of that identity are then recommended to be assigned to the new identity, upon approval.

Abstract: Policies defining the entitlements to be assigned to a new identity joining a role are automatically generated. An automatic policy assigns a new identity the entitlements commonly owned by a predetermined number of identities in the role, which may be all of the role identities. A conditional policy recommends that a new identity be assigned the non-commonly-owned entitlements associated with the role identity whose non-entitlement attributes most closely match the non-entitlement attributes of the new identity. This may be automatically determined by iterating through a vector that maps the non-commonly-owned entitlements with the non-entitlement attributes of each role identity, comparing the non-entitlement attributes of the new identity to find the closest match. The non-commonly-owned entitlements of that identity are then recommended to be assigned to the new identity, upon approval.

Abstract: The invention is a method, system and computer program for automatically discovering concepts from a corpus of documents and automatically generating a labeled concept hierarchy. The method involves extraction of signatures from the corpus of documents. The similarity between signatures is computed using a statistical measure. The frequency distribution of signatures is refined to alleviate any inaccuracy in the similarity measure. The signatures are also disambiguated to address the polysemy problem. The similarity measure is recomputed based on the refined frequency distribution and disambiguated signatures. The recomputed similarity measure reflects actual similarity between signatures. The recomputed similarity measure is then used for clustering related signatures. The signatures are clustered to generate concepts and concepts are arranged in a concept hierarchy. The concept hierarchy automatically generates query for a particular concept and retrieves relevant documents associated with the concept.

Abstract: The present invention provides a method, system and computer program for naming a cluster, or a hierarchy of clusters, of words and phrases that have been extracted from a set of documents. The invention takes these clusters as the input and generates appropriate labels for the clusters using a lexical database. Naming involves first finding out all possible word senses for all the words in the cluster, using the lexical database; and then augmenting each word sense with words that are semantically similar to that word sense to form respective definition vectors. Thereafter, word sense disambiguation is done to find out the most relevant sense for each word. Definition vectors are clustered into groups. Each group represents a concept. These concepts are thereafter ranked based on their support. Finally, a pre-specified number of words and phrases from the definition vectors of the dominant concepts are selected as labels, based on their generality in the lexical database.

Abstract: Policies defining the entitlements to be assigned to a new identity joining a role are automatically generated. An automatic policy assigns a new identity the entitlements commonly owned by a predetermined number of identities in the role, which may be all of the role identities. A conditional policy recommends that a new identity be assigned the non-commonly-owned entitlements associated with the role identity whose non-entitlement attributes most closely match the non-entitlement attributes of the new identity. This may be automatically determined by iterating through a vector that maps the non-commonly-owned entitlements with the non-entitlement attributes of each role identity, comparing the non-entitlement attributes of the new identity to find the closest match. The non-commonly-owned entitlements of that identity are then recommended to be assigned to the new identity, upon approval.

Abstract: A role hierarchy is automatically generated by hierarchically ranking roles in a role based control system, each role including a plurality of identities having attributes. Iteratively at each hierarchical level: each non-cohesive role (wherein, in this case, at least one attribute is not possessed by every identity in the role) is replaced, at the same hierarchical level, by a cohesive role formed by grouping identities having at least one common attribute. The remaining identities are clustered into children roles based on attributes other than the common attribute, and the children roles are added to the role hierarchy at a hierarchical level below the cohesive role. If no common attribute exists in the non-cohesive role, the role is clustered into two or more new roles based on all the attributes in the role, and the non-cohesive role is replaced with the new roles at the same hierarchical level.

Abstract: An automated, bottom-up role discovery method for a role based control system includes automatically extracting identities and attributes from data sources and automatically clustering the identities based on the attributes to form recommended roles. The recommended roles may be modified by intervention of an administrator. Additionally, the recommended roles may be aggregated by defining the role definition as an attribute of each constituent identity, and re-clustering the identities to generate refined roles. The recommended, modified, and/or refined roles may then be utilized in a role based control system, such as a role based access control system. Periodically performing the role discovery process provides a means to audit a role based access control system.

Abstract: The invention is a method, system and computer program for automatically discovering concepts from a corpus of documents and automatically generating a labeled concept hierarchy. The method involves extraction of signatures from the corpus of documents. The similarity between signatures is computed using a statistical measure. The frequency distribution of signatures is refined to alleviate any inaccuracy in the similarity measure. The signatures are also disambiguated to address the polysemy problem. The similarity measure is recomputed based on the refined frequency distribution and disambiguated signatures. The recomputed similarity measure reflects actual similarity between signatures. The recomputed similarity measure is then used for clustering related signatures. The signatures are clustered to generate concepts and concepts are arranged in a concept hierarchy. The concept hierarchy automatically generates query for a particular concept and retrieves relevant documents associated with the concept.

Abstract: The present invention provides a method, system and computer program for naming a cluster, or a hierarchy of clusters, of words and phrases that have been extracted from a set of documents. The invention takes these clusters as the input and generates appropriate labels for the clusters using a lexical database. Naming involves first finding out all possible word senses for all the words in the cluster, using the lexical database; and then augmenting each word sense with words that are semantically similar to that word sense to form respective definition vectors. Thereafter, word sense disambiguation is done to find out the most relevant sense for each word. Definition vectors are clustered into groups. Each group represents a concept. These concepts are thereafter ranked based on their support. Finally, a pre-specified number of words and phrases from the definition vectors of the dominant concepts are selected as labels, based on their generality in the lexical database.