Abstract: Each product of a number of products is assigned to one of a number of popularity tiers. The popularity tiers are ordered from a most popular tier to a least popular tier. The popularity tiers indicate how popular the products are expected to be among consumers. Each product is assigned to one of a number of margin tiers. The margin tiers are ordered from a highest margin tier to a lowest margin tier. The margin tiers indicate how much money a retailer makes in selling the products to the consumers. Which of the products to offer for sale by the retailer to the consumers are selected by applying decision rules to the products as have been assigned to the popularity tiers and to the margin tiers.

Abstract: An exemplary embodiment of the present invention provides a computer implemented method of developing a classifier. The method includes receiving input for a case, the case comprising a plurality of instances and an example, the example comprising a plurality of data fields each corresponding to one of the plurality of instances, wherein the input indicates which, if any, of the instances includes a data field belonging to a target class. The method also includes training the classifier based, at least in part, on the input from the trainer.

Abstract: An exemplary embodiment of the present invention provides a computer implemented method of developing a classifier. The method includes obtaining a set of training data comprising labeled cases. The method also includes training a classifier based, at least in part, on the training data. The method also includes applying the classifier to a plurality of unlabeled cases to generate classification scores for each of the unlabeled cases, wherein each classification score corresponds with an instance of a corresponding case. Furthermore, the classification score corresponding to a first instance in a case is computed based, at least in part, on a value of a case-centric feature corresponding to the first instance, wherein the value of the case-centric feature is based, at least in part, on characteristics of the first instance and a second instance in the case.

Abstract: A chemical mechanical polishing (CMP) composition, comprising (A) at least one type of inorganic particles which are dispersed in the liquid medium (C), (B) at least one type of polymer particles which are dispersed in the liquid medium (C), (C) a liquid medium, wherein the zeta-potential of the inorganic particles (A) in the liquid medium (C) and the zeta-potential of the polymer particles in the liquid medium (C) are of same signs.

Abstract: In an embodiment, a decode unit includes multiple decoders configured to decode different types of instructions. One or more of the decoders may be complex decoders, and the decode unit may disable the complex decoders if an instruction of the corresponding type is not being decoded. In an embodiment, the decode unit may disable the complex decoders by data-gating the instruction into the decoder. The decode unit may also include a control unit that is configured to detect instructions of the type decoded by the complex decoders, and to enable the complex decoders and redirect the fetching in response to the detection. The decode unit may also record an indication of the instruction (e.g. the program counter address (PC) of the instruction) to more rapidly detect the instruction and prevent a redirect in subsequent fetches.

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: The present disclosure includes a system and method for designing and displaying advertisements. One or more targeted advertising methods include surveying potential customers to ascertain a price sensitivity and a likelihood of the potential customers purchasing products and/or observing venues, and clustering the potential customers according to product clusters based on the likelihood of purchasing products. Potential customers of each product cluster are clustered according to one or more venue clusters based on a likelihood of the potential customers of respective product clusters to observe the venues. An advertisement is designed for a venue corresponding to a particular venue cluster to include at least one product corresponding to a particular product cluster which is promotionally-priced based on the price sensitivity of potential customers of a particular venue cluster. An electronic display of the venue is modified to include the designed advertisement.

Abstract: An exemplary embodiment of the present invention provides a method of generating Website content. The method includes generating a client profile comprising a cluster type obtained from a list of cluster types and information received from a user ID, wherein the list of cluster types is generated by processing a database of computer usage. The method includes utilizing the relevant cluster types included in the client profile to a selected Website, wherein the cluster type is used by the Website at least in part to determine the content provided by the Website.

Abstract: Methods, systems and computer program products are provided for social networking. In one method, a network builder receives a digital object from the user. The digital object contains information associated with the user. The network builder extracts the information associated with the user from the digital object. The network builder further access the strength of relationships between the user and a plurality of other users, each associated with one or more social networks. The relationships strength is extracted based at least in part on the extracted information. The network builder then adds the user to one or more social networks based on the information associated with the user and the strength of the relationships between the user and the plurality of other users.

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: 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: An exemplary embodiment of the present invention provides a computer implemented method of developing a classifier. The method includes receiving input for a case, the case comprising a plurality of instances and an example, the example comprising a plurality of data fields each corresponding to one of the plurality of instances, wherein the input indicates which, if any, of the instances includes a data field belonging to a target class. The method also includes training the classifier based, at least in part, on the input from the trainer.

Abstract: A computer implemented method of grouping query URLs is provided. The method includes obtaining a plurality of query URLs generated at a plurality of Websites. The method also includes analyzing the query URLs to identify similarities between the URLs. The method also includes grouping the query URLs into cases based, at least in part, on the similarities, wherein each case comprises a plurality of instances, and each instance comprises a plurality of data field values corresponding to data fields with a same data field name.

Abstract: An exemplary embodiment of the present invention provides a computer implemented method of developing a classifier. The method includes obtaining a set of training data comprising labeled cases. The method also includes training a classifier based, at least in part, on the training data. The method also includes applying the classifier to a plurality of unlabeled cases to generate classification scores for each of the unlabeled cases, wherein each classification score corresponds with an instance of a corresponding case. Furthermore, the classification score corresponding to a first instance in a case is computed based, at least in part, on a value of a case-centric feature corresponding to the first instance, wherein the value of the case-centric feature is based, at least in part, on characteristics of the first instance and a second instance in the case.

Abstract: In an embodiment, an internetworking device is configured with compliance proxy logic that is configured for sending, to a compliance server, a request to determine whether the command conforms to one or more compliance policies, wherein the request includes the command; receiving a compliance response from the compliance server; in response to determining whether the compliance response indicates success, executing the command only when the compliance response indicates that the command conforms to the one or more compliance policies. Thus the device can determine actively whether a proposed user command or configuration change will violate established standards or policies, before the command or change is applied to the device.

Abstract: A method for near real time patterns identification, in one example embodiment, comprises receiving a data stream containing information associated with a transaction and participants of the transaction and receiving an Artificial Intelligence (AI) algorithm trained to score data in the data stream. The method may further comprise receiving metadata associated with the historical information, comparing the data stream to the metadata by measuring differences between variables included in the historical metadata and the data stream. The method may further comprise modifying the data stream to suit the AI algorithm when the differences between variables are below predetermined threshold values and retraining the AI algorithm based on the data stream when the differences between the variables are greater than the predetermined threshold values. The method may further comprise feeding the data stream to the AI algorithm to classify the variables in the data stream.

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: An exemplary embodiment of the present invention provides a method of receiving Website content. The method includes generating a user profile comprising a cluster type obtained from a list of cluster types, wherein the list of cluster types is generated by processing a database of search queries. The method includes providing the relevant cluster types included in the user profile to a selected Website, wherein the cluster type sent to the Website is used by the Website at least in part to determine the content provided by the Website.

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: An exemplary embodiment of the present invention provides a method of processing Web activity data. The method includes obtaining a database of clickstream data comprising a user identifier corresponding with a user ID and a uniform resource locator (URL) corresponding with a Web page visited from the user ID. The method also includes generating a plurality of features based on the URL. Further, the method includes generating a data structure comprising the user ID and the feature. The method also includes generating segment information from the data structure based on the similarity of a URL visitation pattern across different user IDs, wherein each segment in the segment information comprises one or more user IDs and one or more features.