International Business Machines Corporation

Globalfoundries Inc.

Sechan Oh

Marek Petrik

Markus Ettl

Hovey R Strong, Jr

Amit Dhurandhar

Christopher S Campbell

Anca Andreea Chandra

Jeanette L Blomberg

Neil H A Boyette

Rajesh Kumar Ravi

H Raymond Strong, Jr

Adam N Elmachtoub

Steven G Pinchuk

Techniques facilitating interpretable rule generation using loss-preserving transformation are provided. In one example, a computer-implemented method can comprise evaluating, by a system operatively coupled to a processor, an input data set that comprises three data categories. The computer-implemented method can also comprise transforming, by the system, the input data set into a transformed data set. The transformed data set can comprise two data categories determined based on the three data categories. Transforming the input data set can comprise determining a first cost associated with the transformed data set is no greater than a second cost associated with the input data set.

Interpretable rule generation using loss preserving transformation

Interpretable rule generation using loss-preserving transformation

A system, method and computer program product for determining a target population to be subject to a promotion or offer of goods/services. The target population is determined that strongly prefers a given promotion, while also making sure the target population represents a sizable number of consumers such that profits may be maximized. The system provides an output solution listing available promotion options and one or more corresponding target groups based on solving an optimization problem that incorporates, prior obtained most important customer features for each promotion using historical promotion data and statistics measures. The system may automatically initiate a promotion offering to each of said customers by communicating the promotion to the members of the targeted group of people, wherein a percentage of future transactions to which the promotion is offered is expected to exceed a threshold level.

Determining feature importance and target population in the context of promotion recommendation

User information may be received and a market segment associated with the user may be received. A personalized or individualized offer may be determined based on the user information, the personalized offer determined based on a product offeror's goal with respect to the user at a given time. The market segment offer and the personalized offer may be blended to determine a recommended personalized offering for the user at the given time, e.g., given a company's tactical, strategic and lifetime goals and values for that user.

Computer-implemented techniques for determining and provisioning real-time individualized product and service offering

Disclosed are methods and systems for identifying and visualizing the patterns of work transfers for service delivery using financial data. Within a service firm, work is often transferred from one delivery center to another, which results in additional costs and delays in service delivery. To control unnecessary work transfers, sustained patterns of work transfer are identified so that steps can be taken to limit their occurrence. The disclosed methods and systems analyze the relations among the costs transferred from one location to another.

Method and system for identifying and visualizing work transfers using financial data

An approach for visualizing versions of a hierarchically organized object is provided. A visualization is generated to include first and second versions including information from multiple hierarchical levels of trees representing the versions. The visualization is generated so that the second version highlights a value of a dimension for a labeled path of the tree representing the second version. The highlighted value differs from a corresponding value of a dimension for a labeled path of the tree representing the first version based on a vector space constructed to have multiple dimensions for each labeled path of the trees. A similarity measure between the first and second versions is computed by normalizing a first vector associated with the first version, normalizing a second vector associated with the second version, and determining a product of the normalized first vector and the normalized second vector.

Version visualization

An approach for visualizing versions of a hierarchically organized object is presented. A measure of similarity is computed between each version and a standard version. Versions having identical hierarchies are clustered into sets of versions. Versions within each of the sets of versions are organized according to the computed similarity measures. A visualization is generated as a plot having first and second dimensions. The first dimension represents similarity between a set of versions and the standard version. The second dimension represents similarity between a version and the standard version. In one embodiment, the visualization includes, in a first area, information from multiple hierarchical levels of the standard version and includes, in a second area, information from only the root node level of the other versions. After receiving an approval of a version in the second area, the approved version may replace the standard version in the first area.

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Stanford, CA, United States of America

Sechan Oh