Hewlett-packard Development Company, L.p.

Hewlett Packard Enterprise Development LP

Xerox Corporation

Micro Focus Lcc

Entit Software LLC

Hewlett-packard Development, L.p.

Rajan Mathew Lukose

Martin B Scholz

Joshua Rogers Tyler

Eytan Adar

Shyam Sundar Rajaram

Caesar Sengupta

Craig Peter Sayers

Mark David Lillibridge

Shyamsundar Rajaram

Rong Pan

Stephen Phillip Sorkin

Ignacio Zendejas

Norman Paul Jouppi

Bernardo A Huberman

Li Zhang

Evan Randy Kirshenbaum

Henri Jacques Suermondt

Tad H Hogg

Jerry Zhaolin Shan

Prakash D Reddy

Yunhong Zhou

Erwin Oertli

Lada A Adamic

Ravigopal Vennelakanti

Deeparnab Chakrabarty

There is described a system and computer-implemented method for providing a recommendation based on a sparse pattern of data. An exemplary method comprises determining a likelihood that an item for which no user preference data is available will be preferred. The exemplary method also comprises determining a likelihood that an item for which user preference data is available for users other than a particular user will be preferred based on the likelihood that the item for which no user preference data is available will be preferred. The exemplary method additionally comprises predicting that an item for which no user preference data relative to the particular user is available will be preferred if the likelihood that the particular user will prefer the item exceeds a certain level.

System and method for making a recommendation based on user data

In a method for managing a plurality of data entities, data pertaining to transactions by a plurality of users with respect to the data entities is collected and a collaborative filtering operation is applied on the data entities to determine similarity levels of the data entities with respect to each other. In addition, for at least one of the data entities, remaining ones of the data entities are ranked according to the determined similarities while discounting for popularities of the data entities. Moreover, identifications of at least another one of the data entities having the highest rankings to the at least one of the data entities are presented to a first user to recommend the at least another one of the data entities for use by the first user.

Managing data entities using collaborative filtering

Developing a population category hierarchy can include providing a candidate category hierarchy, including a number of candidate categories, and a mapping between a number of reference pages and the number of candidate categories, including a number of mapped reference pages (). Population usage data of the number of mapped reference pages can be obtained and used to determine a population traffic metric for each of the number of candidate categories (). A number of population categories can be generated by using the population traffic metric of each of the number of candidate categories (); and, a population category hierarchy can be produced including the number of population categories ().

Population category hierarchies

One embodiment is a method that receives a seed Uniform Resource Locator (URL) that represents a category for website classification. Clickstream data generated from the seed URL and additional URLs are analyzed to determine whether the additional URLs belong to the category. The method selects one or more of the additional URLs to represent the category.

Clickstreams and website classification

An exemplary embodiment of the present invention provides a method of processing Web activity data. The method includes obtaining a database of Website organizational data. The method also includes generating a data structure from the database of Website organizational data comprising an Item identifier and a Website category corresponding to the item identifier. The method also includes generating a reduced-rank classification structure from the data structure, the reduced-rank classification structure including a category grouping corresponding to one or more of the Website categories.

Method and system for characterizing web content

A privacy-preserving system and method is disclosed for profiling clients within a system for knowledge management. The method of the present invention discloses steps for generating a client profile in support of receiving and processing messages using scoring techniques and/or filtering techniques. The method of the present invention further includes steps for generating a client profile in support of a method for generating and obtaining responses to messages using scoring techniques and/or filtering techniques. The system of the present invention, includes all means for implementing the method.

System and method for profiling clients within a system for harvesting community knowledge

A method performed by a processing system includes receiving a recommendation from a source user in response to performing an action corresponding to an action context of the recommendation, determining whether the source user appears in social network information of a target user, and distinguishing a presentation of the recommendation to the target user in response to the source user appearing in the social network information of the target user.

Social and contextual recommendations

For each web page visited, a path is determined through a hierarchy of categories. The hierarchy of categories has levels from a most abstract level to a most concrete level. For each microblog entry of a microblog, a path is determined through the hierarchy of categories. Each microblog entry for which the path is similar to the path for at least one web page is determined as a selected microblog entry.

Selecting microblog entries based on web pages, via path similarity within hierarchy of categories

In a method of visually representing a hierarchy of category nodes that identify one or more concepts, accesses to documents are tracked and one or more concepts that are relevant to the accessed documents are determined. In addition, one or more category paths through the hierarchy of category nodes for the determined one or more concepts are determined and relevance levels for the category nodes with respect to the determined one or more concepts are determined based upon the generated one or more category paths. Moreover, a graph depicting a visual representation of the relevance levels determined for each of the category nodes in the hierarchy of category nodes is constructed.

Visually representing a hierarchy of category nodes

One embodiment of the method discloses: identifying an information resource accessed by a client computer; generating an information resource pointer including an address for the information resource; and transmitting a pointer message including the information resource pointer over a network. A second embodiment of the method discloses: generating a client profile; storing the profile; receiving a pointer message containing an information resource pointer; scoring the pointer message with respect to the profile; totaling a number of times that the information resource pointer is received, over a predetermined time period; initializing a timeliness score to a maximum value; decrementing the timeliness score by a predetermined percentage each time a predetermined time period elapses after transmission by a sending client computer; generating an aggregate score; and displaying the pointer message and the aggregate score. The system of the present invention, includes all means for implementing the method.

System and method for anonymously sharing and scoring information pointers, within a system for harvesting community knowledge

A system and method for knowledge management is disclosed. The method discloses: monitoring messages transmitted over a peer-to-peer network; profiling the messages; receiving a messages report request; identifying message profiles corresponding to the message report request; selecting messages corresponding to the identified message profiles; and generating the requested messages report from the selected messages. The system discloses means for implementing the method.

System and method for message traffic analysis, categorization, and reporting, within a system for harvesting community knowledge

Systems, methods, and machine readable and executable instructions are provided for collaborative filtering. Collaborative filtering includes representing users and objects by rows and columns in a binary ratings matrix having a particular dimensional space. Unknown values in the binary ratings matrix are weighted with a weight matrix having the particular dimensional space. The binary ratings matrix and the weight matrix are hashed into a lower dimensional space by one of row and column. The hashed binary ratings matrix and the hashed weight matrix are low-rank approximated by alternating least squares. A result of the low-rank approximation for the one of row and column is updated using the binary ratings matrix and the weight matrix. A recommendation of one of the objects can be generated for one of the users based on the updated result.

Collaborative filtering with hashing

Systems, methods, and machine readable and executable instructions are provided for collaborative filtering. Collaborative filtering includes representing users and objects by rows and columns in an ordinal ratings matrix having a particular dimensional space. Values in the ordinal ratings matrix are weighted with a weight matrix having the particular dimensional space. The weight matrix is hashed into a lower dimensional space by one of row and column by multiplying a projection matrix by the weight matrix. The ordinal ratings matrix is hashed into a lower dimensional space by multiplying the projection matrix by an element-wise product of the weight matrix and the ordinal ratings matrix to form a reduced ratings matrix, and element-wise dividing the reduced ratings matrix by the hashed weight matrix. The hashed ordinal ratings matrix and the hashed weight matrix are low-rank approximated by alternating least squares. A result of the low-rank approximation for the one of row and column is updated using the ordinal ratings matrix and the weight matrix. A recommendation of one of the objects can be generated for one of the users based on the updated result.

Embodiments of the present invention include a computational forecasting system that includes an identity of a dependent variable of interest and identities of a plurality of candidate indicators along with historical data or stored references to historical data, forecast-problem parameters stored in an electronic memory of the one or more electronic computers, an independent-variable selection component that generates correlations to the dependent variable of interest and lag times for the candidate indicators, and uses the generated correlations and lag times to select a number of the candidate indicators as a set of independent variables, and a model-generation component that, using a regression method, generates forecast models for the dependent variable of interest until a model that meets an acceptance criterion or criteria is obtained.

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Palo Alto, CA, United States of America

Rajan Mathew Lukose