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: The disclosure relates to methods and systems for generating application retention metrics. In one aspect, a method for generating coupled application retention metrics is disclosed. First and second reference application identifiers are selected (230), and a coupled application retention metric between the first and second reference application identifiers is generated (330). The coupled retention metric is generated by querying (100) an application database including a plurality of application records, where each record includes an application identifier, a user identifier, and retention information. Retention metrics in the application database are compared for application identifiers corresponding to the first or second reference application identifier and having a common user identifier. An application of interest is selected by and its identifier is received (200) from a user of a remote application, where the selected application corresponds to the first reference application identifier.

Abstract: The present disclosure provides a computer-implemented method of processing Web activity data. The method includes obtaining a collection of Web activity data generated by a plurality of users at a plurality of Webpages, wherein the Webpages are from a plurality of unaffiliated Websites. The method also includes extracting a plurality of search terms from the Web activity data and associating each of the plurality of search terms with a corresponding Webpage. The method also includes generating statistical data from the Web activity data based, at least in part, on the search terms, the statistical data corresponding to the online activity at one or more Webpages.

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: Provided are, among other things, systems, methods and techniques for identifying related objects in a computer database. In one representative implementation: (a) a feature vector that describes an existing object is obtained; (b) comparison scores are generated between the feature vector and various sample vectors; (c) a set that includes at least one designated vector is identified from among the sample vectors by evaluating the generated comparison scores; (d) a computer database is searched for matches between label(s) for the designated vector(s) and labels for representative vectors for other objects represented in the computer database; and (e) at least one related object is identified based on the identified match(es).

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 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: The disclosure relates to methods and systems for generating application retention metrics. In one aspect, a method for generating coupled application retention metrics is disclosed. First and second reference application identifiers are selected (230), and a coupled application retention metric between the first and second reference application identifiers is generated (330). The coupled retention metric is generated by querying (100) an application database including a plurality of application records, where each record includes an application identifier, a user identifier, and retention information. Retention metrics in the application database are compared for application identifiers corresponding to the first or second reference application identifier and having a common user identifier. An application of interest is selected by and its identifier is received (200) from a user of a remote application, where the selected application corresponds to the first reference application identifier.

Abstract: The disclosure relates to methods and systems for generating application retention metrics. In one aspect, a method for generating a contextual application recommendation is disclosed. Contextual web location information including a remote developer identifier and/or a remote application identifier is received from a remote application. An application database including application records with a developer identifier and an application identifier is queried to determine if the contextual web location information corresponds to an application recommendation. The application database query includes comparing the remote developer identifier to the application database developer identifier to generate a first application recommendation, and/or comparing the remote application identifier to the application database application identifier to generate a second application recommendation.

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: A collection of labeled training cases is received, where each of the labeled training cases has at least one original feature and a label with respect to at least one class. Non-linear transformation of values of the original feature in the training cases is applied to produce transformed feature values that are more linearly related to the class than the original feature values. The non-linear transformation is based on computing probabilities of the training cases that are positive with respect to the at least one class. The transformed feature values are used to train a classifier.

Abstract: A method and a system of making a recommendation to a user that is currently generating events based on a subset of historical event data are provided. Historical event data, which is segmented into a set of sessions, is received. Each session includes events. The sessions are associated with clusters that represent the users that generated the historical event data. Each of the associated sessions is associated with one cluster and the number of the clusters is the same as the number of the users. A determination as to which cluster is associated with events currently being generated by a current user's behavior is made. The determining does not require identification of the current user. A recommendation is made to the current user based on the cluster that is associated with the events currently being generated.

Abstract: A computer-implemented method for populating a product recommendation list can include identifying a first set of products using customer data and a second set of products using social network data, identifying a third set of products, wherein the third set of products includes products in the second set of products and not in the first set of products, calculating a product score for each product in the second set of products, and populating the product recommendation list of the customer with a subset of the first set of products and a subset of the third set of products based on the calculated product scores.

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: The disclosure relates to methods and systems for generating application retention metrics. In one aspect, a method for generating a contextual application recommendation is disclosed. Contextual web location information including a remote developer identifier and/or a remote application identifier is received from a remote application. An application database including application records with a developer identifier and an application identifier is queried to determine if the contextual web location information corresponds to an application recommendation. The application database query includes comparing the remote developer identifier to the application database developer identifier to generate a first application recommendation, and/or comparing the remote application identifier to the application database application identifier to generate a second application recommendation.

Abstract: To generate at least one hash value for a feature vector that represents a data object, a discrete orthogonal transform is applied on a second vector produced from the feature vector. Applying the discrete orthogonal transform on the second vector produces a third vector. At least one value is selected from the third vector to produce the hash value. The at least one hash value is used to perform an action.

Abstract: A method and a system of making a recommendation to a user that is currently generating events based on a subset of historical event data are provided. Historical event data, which is segmented into a set of sessions, is received. Each session includes events. The sessions are associated with clusters that represent the users that generated the historical event data. Each of the associated sessions is associated with one cluster and the number of the clusters is the same as the number of the users. A determination as to which cluster is associated with events currently being generated by a current user's behavior is made. The determining does not require identification of the current user. A recommendation is made to the current user based on the cluster that is associated with the events currently being generated.

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.