Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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 (143). 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 (147). A number of population categories can be generated by using the population traffic metric of each of the number of candidate categories (149); and, a population category hierarchy can be produced including the number of population categories (151).

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: In the present disclosure, methods and apparatuses are disclosed that enable a device to determine whether a contact is in a shared environment based on an audio sample of a voice call. More specifically, an audio sample of a voice call is generated. A controller then determines whether a contact is in an environment of the mobile device based on the audio sample.

Abstract: 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.

Abstract: 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.

Abstract: Systems (490), methods (100, 200), and computer-readable and executable instructions (324, 424) are provided for estimating costs of behavioral targeting. Estimating costs of behavioral targeting can include scoring a topic with a behavioral targeting model (101, 201). Estimating costs of behavioral targeting can also include obtaining a plurality of data items including geographic location information (102, 202). Estimating costs of behavioral targeting can also include detecting (104, 204) and scoring (209) a sentiment from filtered data items regarding a topic within a region (104, 204). Estimating costs of behavioral targeting can include computing a penalty score for the topic in the region in response to the scored sentiment exceeding a threshold (213), (106, 206). Estimating costs of behavioral targeting can include adjusting the topic score in the region according to the penalty score (108, 208).

Abstract: 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 (143). 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 (147). A number of population categories can be generated by using the population traffic metric of each of the number of candidate categories (149); and, a population category hierarchy can be produced including the number of population categories (151).

Abstract: Embodiments disclosed herein relate to providing control over a personalized category of information. In one embodiment, a personalized category of information is determined based on stored information associated with the use of an electronic device 110. In one embodiment, user feedback on the personalized category of information is received. If the user feedback comprises a rejection of the personalized category of information, the personalized category of information is discarded.

Abstract: In a method for delivering an item of interest, identification of the item of interest is received and identification of an action context to trigger delivery of the identified item of interest is received. In addition, the item of interest, the action context, and an association of the item of interest to the action context are stored. Moreover, a determination that an indication regarding the action context has been received is made and the item of interest is delivered.

Abstract: In a method for context-based item bookmarking (300), an instruction to bookmark an item for future delivery and an action context configured to trigger delivery of the bookmarked item are received (304 and 306). In addition, the action context and the item are bookmarked (308) and at least one entity's activities are monitored to determine whether an activity associated with the action context has been performed (310). Moreover, in response to a determination that the activity associated with the action context has been performed, the bookmarked item is delivered to at least one entity (314).

Abstract: 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.