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: 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 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: An approach is provided in which the approach stores, in a cached recycle bin, a set of sensor data that is sent from a sensor at a transmit frequency. The approach samples at least a portion of the set of sensor data from the cached recycle bin based on a sampling frequency and training a machine learning model using the sampled data. In response to detecting that a performance of the machine learning model falls below a threshold during the training, the approach adjusts the sampling frequency and re-sampling at least a portion of the sensor data based on the adjusted sampling frequency. The approach instructs the sensor to adjust the transmit frequency in response to determining that the performance of the machine learning model reaches the threshold using the re-sampled data.

Abstract: Machine logic for selecting a given item for an inventory. This selection of the given item is based, at least in part, upon: (i) an amount of “ensembles” that include the item; (ii) relative popularity of “ensembles” that contain the item; and/or (iii) the relative profitability of ensembles that includes the item. This technology can be provided as part of assortment planning software for retail stores selling items such as: fashionable clothing, furniture sets, jewelry sets, and other types of items that are typically sold in ensembles and have subjective factors (like aesthetics) that play into the attractiveness of the ensemble considered as a whole.

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.