Abstract: Various embodiments are provided for implementing intelligent decision support system in a computing environment by a processor. Data of historical decisions may be collected and examples of decisions by domain experts may be generated. One or more machine learning models may be generated using different splits of the historical data and the annotated data. The one or more machine learning models may be combined and used to generate ensemble machine learning models that generate recommendations for the decisions. Users interact with a user interface displaying the data, recommendations, reasons for recommendations and a conversational dialog system for querying about the data, recommendations and guidance for decision making.

Abstract: Embodiments of the present invention disclose a method, computer program product, and system for talent acquisition. A method comprises receiving a job application from an Applicant, wherein the job application includes a resume and analyzing the job applicant of the Applicant to gather information about the Applicant. Creating an applicant profile based on the gathered information about the Applicant and comparing the applicant profile to an exemplar employee profile to determine if the Applicant is a good match for a job position that corresponds to the job application. Sending the applicant's profile, the exemplar employee profile, the comparison result, and the job application to a review contact.

Abstract: A method and system are provided. The method includes receiving by a computer having a processor and a memory, sequence data that includes labeled data and unlabeled data. The method further includes generating, by the computer having the processor and the memory, a recurrent neural network model of the sequence data, the recurrent neural network model having a recurrent layer and an aggregate layer. The recurrent neural network model feeds sequences generated from the recurrent layer into the aggregate layer for aggregation, stores temporal dependencies in the sequence data, and generates labels for at least some of the unlabeled data.

Abstract: A data set including at least skills data, recruiting data, compensation data, organization structure data can be received. A first training set can be created by cleaning and integrating the data set. A first machine learning model can be trained to predict skill scarcity associated with a skill, geography and organization using the first training set. A second training set can be created by selecting a subset of the first training set based on a local subject matter expert's input with respect to the trained first machine learning model's performance. The first machine learning model can be refined by retraining the first machine learning model using the second training set, the machine learning model refined to predict the skill scarcity associated with the skill, geography and organization within a locality associated with the local subject matter expert.

Abstract: A user trajectory graph may be constructed based on spatio-temporal data. A mobility pattern may be extracted from the user trajectory graph. Users may be clustered into groups, wherein the users in a same group possess similar feature values in the mobility pattern, and the users in different groups have different feature values, to identify personas and location sets. A distribution model may be constructed that models user timing and location preference, wherein an outcome indicates a preference for a particular time bin on a particular day for a particular location.

Abstract: Various embodiments are provided for implementing intelligent decision support system in a computing environment by a processor. Data of historical decisions may be collected and examples of decisions by domain experts may be generated. One or more machine learning models may be generated using different splits of the historical data and the annotated data. The one or more machine learning models may be combined and used to generate ensemble machine learning models that generate recommendations for the decisions. Users interact with a user interface displaying the data, recommendations, reasons for recommendations and a conversational dialog system for querying about the data, recommendations and guidance for decision making.

Abstract: A method and system are provided. The method includes converting, by a computer having a processor and a memory, categorical sequence data for a customer journey into a numerical similarity matrix. The method further includes learning, by the computer, features of the customer journey by applying a distance metric learning based matrix factorization approach to the numerical similarity matrix.

Abstract: A system, method and program product for bundling resources for a resource provisioning platform. A system is disclosed that includes a plurality of resources, wherein each resource belongs to one of a plurality of categories; a bundling system having: a data collection system that gathers historical transaction data associated with the resources; an analysis system that analyzes the historical transaction data to assign estimated valuations to different bundles of resources and includes (a) a substitution effect analyzer to analyze a substitution effect of resources in each category using discrete choice modeling and marginal value estimation, and (b) a joint dependence analyzer that determines intra-category and inter-category joint dependent inferences across all resources; and a bundle selection system that selects a set of bundles for provisioning based on the estimated valuations.

Abstract: Identifying one or more patterns of content consumption across multiple entities and determining an engagement action for a user of an entity based on the patterns may include receiving information associated with content, cross-industry user data associated with consumption of the content, and a given organization user data associated with consumption of the content. A first set of consumption profile vectors associated with the given organization and one or more second set of consumption profile vectors associated respectively with one or more cross-industry organizations may be generated. Information associated with a target user in the given organization may be received. A customized learning plan for the target user in the given organization may be generated based on the first set of consumption profile vectors, the one or more second set of consumption profile vectors, and the information associated with the target user.

Abstract: A system, method and program product for bundling resources for a resource provisioning platform. A system is disclosed that includes a plurality of resources, wherein each resource belongs to one of a plurality of categories; a bundling system having: a data collection system that gathers historical transaction data associated with the resources; an analysis system that analyzes the historical transaction data to assign estimated valuations to different bundles of resources and includes (a) a substitution effect analyzer to analyze a substitution effect of resources in each category using discrete choice modeling and marginal value estimation, and (b) a joint dependence analyzer that determines intra-category and inter-category joint dependent inferences across all resources; and a bundle selection system that selects a set of bundles for provisioning based on the estimated valuations.

Abstract: A scheduling system and method for high-performance computing (HPC) applications includes a network management component stored in physical memory and executed by a processor. The management component is configured to transform HPC resources into a schedulable resource catalog by transforming multi-dimensional HPC resources into a one dimension versus time resource catalog with a dependent graph structure between resources such that HPC resources are enabled to be provisioned into a service environment with predictable provisioning using the resource catalog. A graphical user interface component is coupled to the network management component and configured to provide scheduling visibility to entities and to enable a plurality of different communication modes for scheduling and communication between entities.

Abstract: A system and method for scheduling resources includes a memory storage device having a resource data structure stored therein which is configured to store a collection of available resources, time slots for employing the resources, dependencies between the available resources and social map information. A processing system is configured to set up a communication channel between users, between a resource owner and a user or between resource owners to schedule users in the time slots for the available resources. The processing system employs social mapping information of the users or owners to assist in filtering the users and owners and initiating negotiations for the available resources.

Abstract: Methods and a system are provided that is performed by a computer server for inferring location context categories for a set of mobile users having at least two members. A method includes, for each mobile user in the set, obtaining at least one location context category therefor from publically available information responsive to uncertain mobile device location data. The method further includes applying multi-user collaborative machine learning to the at least one location context category for each mobile user in the set to infer a single refined location context category for each mobile user in the set.

Abstract: Selecting candidates from a set of candidates by receiving a set of input parameters about a single aspect of the candidates and applying multiple scoring methods to those input parameters to compute a candidate score via each scoring method. A subset of zero or more candidates is then selected from the set of candidates by applying one or more selection criteria to the set of scores. There is not necessarily a value for every input parameter of every candidate.

Abstract: Aspects of the present invention include a method, system and computer program product. The method includes determining, by the processor, an optimal bid price for each one of a plurality of campaigns in each one of a plurality of online real time bidding auctions. The method also includes determining, by the processor, a winning function for bids placed for campaigns in each one of a plurality of online real time bidding auctions, and determining, by the processor, a bidding function for bids placed for campaigns in each one of a plurality of online real time bidding auctions. The method also includes determining, by the processor, the optimal bid price for each one of a plurality of campaigns in each one of a plurality of online real time bidding auctions as a function of the determined winning function and the determined bidding function.

Abstract: A method and system are provided. The method includes converting, by a computer having a processor and a memory, categorical sequence data for a customer journey into a numerical similarity matrix. The method further includes learning, by the computer, features of the customer journey by applying a distance metric learning based matrix factorization approach to the numerical similarity matrix.

Abstract: A method and system are provided. The method includes receiving by a computer having a processor and a memory, sequence data that includes labeled data and unlabeled data. The method further includes generating, by the computer having the processor and the memory, a recurrent neural network model of the sequence data, the recurrent neural network model having a recurrent layer and an aggregate layer. The recurrent neural network model feeds sequences generated from the recurrent layer into the aggregate layer for aggregation, stores temporal dependencies in the sequence data, and generates labels for at least some of the unlabeled data.

Abstract: A user trajectory graph may be constructed based on spatio-temporal data and mobility pattern extracted from trajectory graph. Users may be clustered into groups, wherein the users in a same group posses similar feature values in the mobility pattern, and the users in different groups have different feature values, to identify personas and location sets. A distribution model may be constructed that models user timing and location preference, wherein an outcome indicates a preference for a particular time bin on a particular day for a particular location.

Abstract: Methods and a system are provided that is performed by a computer server for inferring location context categories for a set of mobile users having at least two members. A method includes, for each mobile user in the set, obtaining at least one location context category therefor from publically available information responsive to uncertain mobile device location data. The method further includes applying multi-user collaborative machine learning to the at least one location context category for each mobile user in the set to infer a single refined location context category for each mobile user in the set.

Abstract: Methods and a system are provided that is performed by a computer server for inferring location context categories for a set of mobile users having at least two members. A method includes, for each mobile user in the set, obtaining at least one location context category therefor from publically available information responsive to uncertain mobile device location data. The method further includes applying multi-user collaborative machine learning to the at least one location context category for each mobile user in the set to infer a single refined location context category for each mobile user in the set.