Event-based replenishment simulation for an enterprise supply chain is described. On a per item, per node, per epoch basis, the simulation may generate a stream of action events based on forecasted demand, supply chain logic, and policy inputs that are applied to a current-run state of the supply chain in order to yield a stream of observation events. Requested metrics may be received, and the observation events may then be transformed to predict values for the metrics as output of the simulation. The simulation may be repeated for a given epoch using discrete demand values from a demand distribution, for a plurality of epochs, and/or across a plurality of items at a plurality of nodes. Resultantly, the simulation output can be used for predicting a future run-state of the supply chain across items and nodes.

Transaction-enabled systems and methods for royalty apportionment and stacking are disclosed. An example system may include a plurality of royalty generating elements (a royalty stack) each related to a corresponding one or more of a plurality of intellectual property (IP) assets (an aggregate stack of IP). The system may further include a royalty apportionment wrapper to interpret IP licensing terms and apportion royalties to a plurality of owning entities corresponding to the aggregate stack of IP in response to the IP licensing terms and a smart contract wrapper. The smart contract wrapper is configured to access a distributed ledger, interpret an IP description value and IP addition request, to add an IP asset to the aggregate stack of IP, and to adjust the royalty stack.

Transaction-enabled systems and methods for royalty apportionment and stacking are disclosed. An example system may include a plurality of royalty generating elements (a royalty stack) each related to a corresponding one or more of a plurality of intellectual property (IP) assets (an aggregate stack of IP). The system may further include a royalty apportionment wrapper to interpret IP licensing terms and apportion royalties to a plurality of owning entities corresponding to the aggregate stack of IP in response to the IP licensing terms and a smart contract wrapper. The smart contract wrapper is configured to access a distributed ledger, interpret an IP description value and IP addition request, to add an IP asset to the aggregate stack of IP, and to adjust the royalty stack.

The present disclosure relates to improving recommendations for small shops on an ecommerce platform while maintaining accuracy for larger shops. The improvement is achieved by retraining a machine-learning recommendation model to reduce sample selection bias using a meta-learning process. The retraining process comprises identifying a sample subset of shops on the ecommerce platform, and then creating shop-specific versions of the recommendation model for each of the shops in the subset. Each shop-specific model is created by optimizing the baseline model to predict user-item interactions in a first training dataset for the applicable shop. Each of the shop-specific models is then tested using a second training dataset for the shop. A loss is calculated for each shop-specific model based on the model's predicted user-item interactions and the actual user-item interactions in the second training dataset for the shop. A global loss is calculated based on each of the shop-specific losses, and the baseline model is updated to minimize the global loss.

According to the present disclosure, there is disclosed a method of providing search information using an electronic apparatus, which includes: checking information on a product group corresponding to a keyword acquired from a user; checking color information on one or more products included in the product group; and providing a search result page corresponding to the keyword, the search result page including at least some of one or more color chips corresponding to the information on the product group and the color information on the one or more products.

Embodiments of the present disclosure relate to a method, a device, and a computer program product for user behavior prediction. In some embodiments, at a client, a first user behavior embedding engine in the client generates behavior prediction information of a target user based on feature information of the target user. The client sends the behavior prediction information of the target user to a server, and receives information about a target item recommended for the target user from the server. Such method enables user privacy-related information to be processed only locally, thereby not only ensuring user privacy and security, but also significantly reducing overall resource overhead.

A systems including one or more processors and one or more non-transitory computer readable media storing computing instructions that, when executed on the one or more processors, perform: receiving inventory information from two or more merchants; clustering the two or more merchants into a group of merchants; operating an optimization model for the subset of the inventory information for the group of merchants to determine a first inventory configuration for each of the two or more merchants at a seller location; operating the optimization model for the subset of the inventory information the group of merchants to determine a second inventory configuration for each of the two or more merchants at the seller location; and combining the first inventory configuration and the second inventory configuration to determine third inventory configuration for each of the two or more merchants at the seller location. Other embodiments are described.

Systems and methods are provided for object identifier translation using a key pairs platform in a virtualized or cloud-based computing system. A key pair refers to a pair of identifiers held by an entity. Each key pair includes at least one anonymized object identifier. Advantageously, the key pair system protects privacy and provides anonymity for objects by not disclosing the identity of the objects or the underlying data associated with the objects.

Systems and methods are disclosed to implement an item metric prediction system that predicts a metric for an item using a feature-based model built using other similar items. In embodiments, the system is used to predict item influence values (IIVs) of items indicating an expected amount of subsequent transactions that is caused by an initial transaction of the items. In embodiments, a sample of item transaction data is distributed to a plurality of task nodes, which execute in parallel to determine the items' observed IIVs from the transaction data. Subsequently, a new IIV is determined for an item whose observed IIV has a low confidence level. A set of similar items is selected, and a set of parameters of a feature-based model are tuned to fit the model to the observed IIVs of the similar items. A new IIV having a high confidence level is then obtained using the model.

Embodiments of this application provide a for training an information recommendation model. The method includes: obtaining historical user behavior data in a plurality of product domains; generating candidate sample data of one or more target product domains according to the historical user behavior data by using a generative model; performing user-specific authenticity sample discrimination on candidate sample data of the target product domains and actual user click sample data by using a discriminative model, to obtain a discrimination result; and performing adversarial training on the generative model and the discriminative model according to the discrimination result, to obtain a trained generative adversarial network as an information recommendation model for a to-be-expanded product domain in the plurality of product domains. According to the method, the training effect of the generative model may be improved, the accuracy of generating the pseudo sample is improved, thereby further improving the recommendation effect.