Microsoft Technology Licensing, LLC

D Maxwell Chickering

David Earl Heckerman

Christopher A Meek

Robert L Rounthwaite

Daniel Rosen

John Carlton Platt

Amir Netz

Bo Thiesson

Thierry D'Hers

A decision theoretic approach to targeted solicitation, by maximizing expected profit increases, is disclosed. A decision theoretic model is used to identify a sub-population of a population to solicit, where the model is constructed to maximize an expected increase in profits. A decision tree in particular can be used as the model. The decision tree has paths from a root node to a number of leaf nodes. The decision tree has a split on a solicitation variable in every path from the root node to each leaf node. The solicitation variable has two values, a first value corresponding to a solicitation having been made, and a second value corresponding to a solicitation not having been made.

Decision theoretic approach to targeted solicitation by maximizing expected profit increases

Targeted delivery of items with inventory management using a cluster-based approach or a rule-based approach is disclosed. An example of items is advertisements. Each item is allocated to one or more clusters. The allocation is made based on a predetermined criterion accounting for at least a quota for each item and possibly a constraint for each cluster. The former can refer to the number of times an item must be shown. The latter can refer to the number of times a given group of web pages is likely to be visited by users, and hence is the number of times items can be shown in a given cluster. The invention is not limited to any particular definition of what constitutes a cluster or item.

Cluster-based and rule-based approach for automated web-based targeted advertising with quotas

The transmission of information during ad click-through is disclosed. In one embodiment, a computer-implemented method selects an ad to be displayed on a web page, as one of a plurality of ads within a current cluster in which each of the ad has a probability to be selected. The method displays the ad on the web page, and then detects activation&#x2014;for example, click-through&#x2014;of the displayed ad. The method transmits information to an entity associated with the ad, such as an advertiser, upon detecting click-through or other activation of the ad. In one embodiment, the information transmitted includes information regarding the current cluster.

Transmission of information during ad click-through

Clustering for purposes of data visualization and making predictions is disclosed. Embodiments of the invention are operable on a number of variables that have a predetermined representation. The variables include input-only variables, output-only variables, and both input-and-output variables. Embodiments of the invention generate a model that has a bottleneck architecture. The model includes a top layer of nodes of at least the input-only variables, one or more middle layer of hidden nodes, and a bottom layer of nodes of the output-only and the input-and-output variables. At least one cluster is determined from this model. The model can be a probabilistic neural network and/or a Bayesian network.

Goal-oriented clustering

Reduction of noise within a cluster-based approach for item (such as ad) allocation, such as by using a linear program, is described. In one embodiment, probabilities are discretized into a predetermined number of groups, where the mean for the group that a particular probability has been discretized into is substituted for the particular probability when the items are being allocated. In another embodiment, the probabilities are decreased by a power function of the variances for them. In a third embodiment, allocation of items to clusters is not changed unless the sample sizes used to determine the corresponding probabilities for those ads is greater than a threshold. In a fourth embodiment, after allocation is performed a first time, a predetermined number of item are removed, and reallocation is performed.

Noise reduction for a cluster-based approach for targeted item delivery with inventory management

Efficient data modeling utilizing sparse representation of a data set. In one embodiment, a computer-implemented method such that a data set is first input. The data set has a plurality of records. Each record has at least one attribute, where each attribute has a default value. The method stores a sparse representation of each record, such that the value of each attribute of the record is stored only if the value of the attribute varies from the default value. A data model is then generated, utilizing the sparse representation, and the model is output. The generation of the data model in one embodiment is in accordance with the Expectation Maximization (EM) algorithm.

Fast clustering with sparse data

Visualization of high-dimensional data sets is disclosed, particularly the display of a network model for a data set. The network, such as a dependency or a Bayesian network, has a number of nodes having dependencies thereamong. The network can be displayed items and connections, corresponding to nodes and dependencies, respectively. Selection of a particular item in one embodiment results in the display of the local distribution associated with the node for the item. In one embodiment, only a predetermined number of the items are shown, such as only the items representing the most popular nodes. Furthermore, in one embodiment, in response to receiving a user input, a sub-set of the connections is displayed, proportional to the user input. In another embodiment, a particular item is displayed in an emphasized manner, and the particular connections representing dependencies including the node represented by the particular item, as well as the items representing nodes also in these dependencies, are also displayed in the emphasized manner. Furthermore, in one embodiment, only an indicated sub-set of the items is displayed.

Visualization of high-dimensional data

The dynamic determination of continuous split intervals for decision trees, without sorting, is disclosed. In one embodiment, the continuous split intervals are determined based on the mean of a relevant sub-set of the set of training data. In another embodiment, they are based on the mean and the standard deviation of the relevant sub-set. In another embodiment, the intervals are based on the median instead. In still another embodiment, they are based on a number of percentiles of the relevant sub-set of the set of training data.

Dynamic determination of continuous split intervals for decision-tree learning without sorting

Two-way counts utilizing sparse representation of a data set. In one embodiment, a computer-implemented method such that a data set is first input. The data set has a plurality of records. Each record has at least one attribute, where each attribute has a default value. The method stores a sparse representation of each record, such that the value of an attribute of the record is stored only if it varies from the default value. A data model is then generated, utilizing the sparse representation. Generation of the data model includes initially extracting two-way counts from the sparse representation. Finally, the model is output.

Fast extraction of one-way and two-way counts from sparse data

An architecture for automated data analysis. In one embodiment, a computerized system comprising an automated problem formulation layer, a first learning engine, and a second learning engine. The automated problem formulation layer receives a data set. The data set has a plurality of records, where each record has a value for each of a plurality of raw transactional variables. The layer abstracts the raw transactional variables into cooked transactional variables. The first learning engine generates a model for the cooked transactional variables, while the second learning engine generates a model for the raw transactional variables.

Architecture for automated data analysis

A method and a system for abstracting cooked variables from raw variables. In one embodiment, a data set that has a plurality of records is input into a system, where each record has a value for each of a plurality of raw transactional variables. These variables are organized into a hierarchy of nodes. The raw transactional variables are abstracted into a lesser number of cooked transactional variables, and the cooked transactional variables are output.

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Redmond, WA, United States of America

D Maxwell Chickering