Abstract: Deriving insights from time series data can include receiving subject matter expert (SME) input characterizing one or more aspects of a time series. A model template that specifies one or more components of the time series can be generated by translating the SME input using a rule-based translator. A machine learning model based on the model template can be a multilayer neural network having one or more component definition layers, each configured to extract one of the one or more components from time series data input corresponding to an instantiation of the time series. With respect to a decision generated by the machine learning model based on the time series data input, a component-wise contribution of each of the one or more components to the decision can be determined. An output can be generated, the output including the component-wise contribution of at least one of the one or more components.

Abstract: Methods, systems, and computer program products for facilitating user selection using trend-based joint embeddings are provided herein. A method includes obtaining a selection of an item in an online catalog; determining a compatible item of the plurality of items at least in part by providing the selected at least one item and at least one previously selected item corresponding to the user to a trend-based machine learning model, wherein the trend-based machine learning model is trained on historical data associated with the item in the online catalog and fine-tuned based on current trend data from multiple data sources; receiving feedback in response to outputting the at least one compatible item; identifying one or more attributes related to the at least one compatible item based on the feedback; and using the trend-based machine learning model to determine at least one additional compatible item based on the one or more attributes.

Abstract: A method, system, and computer program product for an interpretable, feature-based post-hoc black box explainer for univariate time series forecasters are provided. The method receives a set of time series forecasting predictions. The set of time series forecasting predictions are generated from a set of black-box models trained with an initial data set. The method generates a set of features based on at least a portion of the initial data set. A set of surrogate models are trained based on the set of time series forecasting predictions and at least a portion of the set of features. A subset of surrogate models is selected. Based on the subset of surrogate models, the method generates one or more explanation outputs for time series forecasting predictions of the set of black-box models.

Abstract: One embodiment provides a method, including: receiving information for each of a plurality of products; for each of the plurality of products: predicting a demand for the product; calculating a sustainability score, wherein the sustainability score is based upon a material composition of the product; and computing a similarity score, wherein the computing comprises comparing pairs of products within the plurality of products; generating a plurality of assortment choices from the plurality of products, wherein each of the plurality of assortment choices has a corresponding assortment demand value, assortment sustainability score, and assortment similarity score, wherein the generating comprises utilizing a multi-objective formulation based upon the predicted demand, the calculated sustainability score, and the computed similarity score; and providing the plurality of assortment choices to a user for selection.

Abstract: Methods, systems, and computer program products for determining locations for offerings using artificial intelligence are provided herein. A computer-implemented method includes determining an initial sub-set of one or more enterprise locations, from a set of multiple enterprise locations, for providing at least one enterprise offering; determining at least one sequential ordering of one or more additional ones of the multiple enterprise locations for providing the at least one enterprise offering by processing data related to the initial sub-set of one or more enterprise locations using an artificial intelligence engine that utilizes one or more artificial intelligence techniques; and automatically training at least a portion of the artificial intelligence engine that utilizes the one or more artificial intelligence techniques based at least on feedback data pertaining to provision of the at least one enterprise offering to the at least one sequential ordering of one or more additional enterprise locations.

Abstract: One embodiment provides a method, including: accessing, at an information handling device, a dynamic visual media corpus, wherein the dynamic visual media corpus comprises a plurality of dynamic visual media scripts; segmenting each of the plurality of dynamic visual media scripts into scenes; generating, for each of the plurality of dynamic visual media scripts, a character fingerprint identifying topics corresponding to each character within a corresponding dynamic visual media script, wherein the generating comprises (i) extracting both characters and topics from the dynamic visual media script and (ii) associating each of the topics with a corresponding character, wherein the character fingerprint identifies costumes of a given character and a topic corresponding to each costume; and producing, for each scene within each dynamic visual media script, a scene vector identifying (iii) the topics included within a corresponding scene and (iv) a character fingerprint for each character occurring within the sce

Abstract: A method for managing the composition and presentation sequencing of compound visual elements, the method including generating a graph of all possible compound visual element combinations, generating a set of possible visual element presentation sequences according to a depth-first search (DFS) of the graph, generating a score for each member of the set of possible visual element presentation sequences according to visual element attributes, and enumerating a visual element presentation sequence according to a visual element presentation sequence score.

Abstract: Methods, systems, and computer program products for facilitating user selection using trend-based joint embeddings are provided herein. A method includes obtaining a selection of an item in an online catalog; determining a compatible item of the plurality of items at least in part by providing the selected at least one item and at least one previously selected item corresponding to the user to a trend-based machine learning model, wherein the trend-based machine learning model is trained on historical data associated with the item in the online catalog and fine-tuned based on current trend data from multiple data sources; receiving feedback in response to outputting the at least one compatible item; identifying one or more attributes related to the at least one compatible item based on the feedback; and using the trend-based machine learning model to determine at least one additional compatible item based on the one or more attributes.

Abstract: A method comprises receiving as input one or more spatial temporal factors associated with one or more products where the one or more spatial temporal factors affect a demand for one or more product attributes of the one or more products at one or more locations at a select period of time and dynamically modifying a first option plan based on the received input of the one or more spatial temporal factors to generate a second option plan for a given location of the one or more locations at the select period of time. The steps are performed by at least one processor device coupled to a memory.

Abstract: Methods, systems, and computer program products for environmental impact aware product refurbishing are provided herein. A computer-implemented method includes obtaining information for products comprising images of the products and location-specific demand data; determining product embeddings of the products based on the images, wherein each of the product embeddings encodes attributes of the corresponding product; creating one or more refurbished designs of each given one of the products based on the initial image of the given product and one or more design constraints; calculating an environmental impact score and a demand impact score associated with each of the created refurbished designs, wherein the demand impact score is based on the location-specific demand data; generating a recommendation to refurbish at least one of the products in accordance with at least one of the refurbished designs based on the environmental impact scores and the demand impact scores.

Abstract: One embodiment provides a computer implemented method, including: creating a buyer and manufacturer virtual agent representing a buyer and manufacturer within a design supply chain; creating a designer virtual agent representing a designer within the design supply chain; detecting a conflict between design attributes preferred by the buyer and manufacturer and design attributes preferred by the designer for a product being designed by the designer; resolving, utilizing the buyer and manufacturer virtual agent and the designer virtual agent, the conflict via initiating a conversation between the buyer and manufacturer virtual agent and designer virtual agent to generate attribute suggestions for incorporation into the product; and presenting the attribute suggestions to the designer for incorporation into the product.

Abstract: One embodiment provides a method, including: receiving information for each of a plurality of products; for each of the plurality of products: predicting a demand for the product; calculating a sustainability score, wherein the sustainability score is based upon a material composition of the product; and computing a similarity score, wherein the computing comprises comparing pairs of products within the plurality of products; generating a plurality of assortment choices from the plurality of products, wherein each of the plurality of assortment choices has a corresponding assortment demand value, assortment sustainability score, and assortment similarity score, wherein the generating comprises utilizing a multi-objective formulation based upon the predicted demand, the calculated sustainability score, and the computed similarity score; and providing the plurality of assortment choices to a user for selection.

Abstract: A method for managing the composition and presentation sequencing of compound visual elements, the method including generating a graph of all possible compound visual element combinations, generating a set of possible visual element presentation sequences according to a depth-first search (DFS) of the graph, generating a score for each member of the set of possible visual element presentation sequences according to visual element attributes, and enumerating a visual element presentation sequence according to a visual element presentation sequence score.

Abstract: One embodiment provides a method, including: accessing, at an information handling device, a dynamic visual media corpus, wherein the dynamic visual media corpus comprises a plurality of dynamic visual media scripts; segmenting each of the plurality of dynamic visual media scripts into scenes; generating, for each of the plurality of dynamic visual media scripts, a character fingerprint identifying topics corresponding to each character within a corresponding dynamic visual media script, wherein the generating comprises (i) extracting both characters and topics from the dynamic visual media script and (ii) associating each of the topics with a corresponding character, wherein the character fingerprint identifies costumes of a given character and a topic corresponding to each costume; and producing, for each scene within each dynamic visual media script, a scene vector identifying (iii) the topics included within a corresponding scene and (iv) a character fingerprint for each character occurring within the sce

Abstract: Methods, systems and computer program products for resource demand prediction are provided herein. A computer-implemented method includes obtaining historical data logs for activities performed in a distributed service network, the distributed service network comprising a plurality of locations. The method also includes identifying factors influencing resource demand for the distributed service network, determining one or more constraints for specifying activity sequence ordering for activities performed in the distributed service network and generating a statistical model of the distributed service network utilizing the historical data logs, the identified factors and the determined constraints.