Abstract: A system can include a database and a computing device. The computing device is configured to receive an item recommendation request corresponding to an asset from an analyst device and select a set of item identifiers of a plurality of item identifiers. An associated published timeframe of the selected item identifiers is related to a present timeframe. The computing device is further configured to determine a composite similarity value for each item identifier of the set of item identifiers comparing a similarity of the asset to each item identifier of the set of item identifiers. The computing device is also configured to generate an item recommendation list including each item identifier of the set of item identifiers with a corresponding composite similarity value above a threshold value and transmit the item recommendation list to the analyst device for display.

Abstract: A system can implement operations that generate a representative keyword search term based on obtained domain data. The operations including, based on the domain data, identifying keyword search term data, product data, and interaction data. Additionally, the operations include determining a set of keyword search term-product pairings, based on the keyword search term data, the product data and the interaction data. Moreover, the operations include determining, one or more clusters of keyword search terms and, a representative keyword search term for each of the one or more clusters of keyword search terms, based on an engagement score associated with each keyword search term and the set of keyword search terms. Furthermore, the operations include outputting the representative keyword search term of each of the one or more clusters of keyword search terms.

Abstract: Systems and methods for auto-naming nodes in a behavior tree are provided. An example method can include: providing a hierarchy of tree nodes by a computing device; generating a first corpus for each node at a final level; creating a first term-document matrix associated with the first corpus; identifying a first group of high-frequency words in the first term-document matrix; removing the first group of the high-frequency words obtain a second corpus; creating a second term-document matrix based on each of a set of predefined rules; identifying a second group of high-frequency words to represent node names; selecting a best set of the predefined rules based on an automatic evaluation model; generating a node name by removing a duplicate word in each node; incorporating feedback to generate a predicted name for each node; and selecting a final name for each node from the predicted name and the generated node name.

Abstract: Systems and methods for auto-naming nodes in a behavior tree are provided. An example method can include: providing a hierarchy of tree nodes by a computing device; generating a first corpus for each node at a final level; creating a first term-document matrix associated with the first corpus; identifying a first group of high-frequency words in the first term-document matrix; removing the first group of the high-frequency words obtain a second corpus; creating a second term-document matrix based on each of a set of predefined rules; identifying a second group of high-frequency words to represent node names; selecting a best set of the predefined rules based on an automatic evaluation model; generating a node name by removing a duplicate word in each node; incorporating feedback to generate a predicted name for each node; and selecting a final name for each node from the predicted name and the generated node name.

Abstract: The system and method described herein enable generating inventory placement recommendations for a store based on transaction data. Inventory data associated with the individual store is obtained. The inventory data includes container location data, item location data, item category data, and transaction data. A store layout model of the store is generated based on the container location data and item location data. Item category relationships are calculated based on the transaction data and the item category data. Inventory layouts are generated based on the store layout model and the calculated item category relationships. Average distance values for each inventory layout are calculated, and an inventory placement recommendation is generated based on the average distance values of the inventory layouts. The generated inventory placement recommendation enables arrangement of the inventory of the store based on past transactions to reduce the average travel distance of customers when shopping there.

Abstract: Examples provide demand transference modeling for item assortment management. A demand prediction component analyzes item attribute data using a demand transference model to calculate a magnitude of demand transfer between items in a set of substitute items associated with a proposed item assortment. The proposed item assortment includes at least one assortment change. The assortment change includes a set of one or more items to be added to a current item assortment and/or a set of one or more items to be removed from the current item assortment. The demand prediction component generates a demand transference result including the calculated magnitude of demand transfer for each item in the set of substitute items and/or a predicted walk-off rate associated with lost demand. An assortment recommendation component generates an accept recommendation and/or a reject recommendation based on the demand transference result, the predicted walk-off rate, and/or a demand transference score.