Abstract: 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.

Abstract: A computer-implemented method comprises running, by a processor, a plurality of classifiers on a web page to obtain one or more keywords. The method further comprises selecting, by the processor, advertisements based on the one or more keywords.

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: A method of information module recommendation is provided. The method comprises collecting a first set of user information associated with a user from an electronic device that is associated with the user, and identifying an information topic associated with the first set of user information. The method further comprises accessing a module database comprising a plurality of information modules, identifying an information module from among the plurality of information modules configured to deliver information pertaining to the information topic, and recommending the information module to the 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: A collaborative filtering method for evaluating a group of items to aid in predicting utility of items for a particular user comprises assigning an item value of either known or missing to each item of the group of items, and applying a modification scheme to the item values of the missing items to assign a confidence value to each of the item values of the missing items to thereby generate a group of modified item values. The group of items having modified item values and the group known items are evaluated to generate a prediction of utility of items for a particular user.

Abstract: The invention is directed to systems, methods, and an apparatus for bidding in online auctions. Bids for advertising include an amount that is a function of an expected value-per-click and a fraction of a budget already spent for advertising slots.

Abstract: 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.

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: 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 of determining at least one category path for identifying an input text, one or more categories that are most relevant to the input text are determined, one or more concepts that are most relevant to the input text using information from a labeled text data source and the one or more categories determined to be the most relevant to the input text are determined, and one or more category paths through a hierarchy of predefined category levels are determined for one or more of the determined concepts.

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 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.

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 computer-implemented method comprises receiving, by a processor, a vector and a matrix. The vector includes historical periodic returns of a portfolio and the matrix contains historical periodic returns of each security in a set of all possible securities comprising the portfolio. The method further comprises computing, by a processor, a linear programming solution of a vector of weights of the securities comprising the portfolio. The vector comprises a product of the matrix and the vector of weights. The linear programming solution is subject to a criterion that a sum of absolute values of the weights in the vector of weights is a minimum. The method also comprises displaying or storing results of the computing.

Abstract: A system and method for providing personalized recommendations are disclosed herein. A system includes a processor and a software system executed by the processor. The software system provides a recommendation for an item. The recommendation is based on a comparison of a low-rank approximation of a domain matrix to a user profile. The user profile is based, in part, on the low-rank approximation of the domain matrix.

Abstract: A computer-implemented method comprises running, by a processor, a plurality of classifiers on a web page to obtain one or more keywords. The method further comprises selecting, by the processor, advertisements based on the one or more keywords.

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: A collaborative filtering method for evaluating a group of items to aid in predicting utility of items for a particular user comprises assigning an item value of either known or missing to each item of the group of items, and applying a modification scheme to the item values of the missing items to assign a confidence value to each of the item values of the missing items to thereby generate a group of modified item values. The group of items having modified item values and the group known items are evaluated to generate a prediction of utility of items for a particular user.

Abstract: A method of predicting user purchase intent from user-centric data includes applying a classification model to a user-centric clickstream, where the classification model predicting a likelihood of a future user purchase by a user within one or more product categories, and customizing content displayed to the user based on the likelihood of future user purchase. A system of predicting user purchase intent from user-centric data includes a computer programmed to record a user's clickstream data as a user accesses a plurality of different websites. The computer is also loaded with a classification model configured to predict a likelihood of a future user purchase by the user within one or more product categories based on the clickstream data.