Abstract: Data about clients, items included in an item inventory, executable code for recommendation processes, and executable code for feature selection process(es), are stored. The recommendation processes are used to identify items in the item inventory that are recommended for specific clients, and the feature selection process(es) is/are used to select features that are displayed to entities tasked with selecting items from the item inventory for clients. Selection feedback and client feedback are received. One or more feature selection process(es) is/are modified based on the selection feedback and/or the client feedback, to thereby change which, where or/or how certain portion of the data about clients is displayed to the entities tasked with selecting items from the item inventory for the clients, and/or change which, where and/or how certain portion of the data about items is displayed to the entities tasked with selecting items from the item inventory for the clients.

Abstract: Data about clients, items included in an item inventory, executable code for recommendation processes, and executable code for feature selection process(es), are stored. The recommendation processes are used to identify items in the item inventory that are recommended for specific clients, and the feature selection process(es) is/are used to select features that are displayed to entities tasked with selecting items from the item inventory for clients. Selection feedback and client feedback are received. One or more feature selection process(es) is/are modified based on the selection feedback and/or the client feedback, to thereby change which, where or/or how certain portion of the data about clients is displayed to the entities tasked with selecting items from the item inventory for the clients, and/or change which, where and/or how certain portion of the data about items is displayed to the entities tasked with selecting items from the item inventory for the clients.

Abstract: A plurality of desirability prediction values are determined by one or more machine learning models. A desirability prediction value of the plurality of desirability prediction values corresponds to a particular client and a particular product. A plurality of global constraints are determined. A plurality of products are allocated to a plurality of clients based on the plurality of determined desirability prediction values and the plurality of determined global constraints.

Abstract: Data about clients, items included in an item inventory, executable code for recommendation processes, and executable code for feature selection process(es), are stored. The recommendation processes are used to identify items in the item inventory that are recommended for specific clients, and the feature selection process(es) is/are used to select features that are displayed to entities tasked with selecting items from the item inventory for clients. Selection feedback and client feedback are received. One or more feature selection process(es) is/are modified based on the selection feedback and/or the client feedback, to thereby change which, where or/or how certain portion of the data about clients is displayed to the entities tasked with selecting items from the item inventory for the clients, and/or change which, where and/or how certain portion of the data about items is displayed to the entities tasked with selecting items from the item inventory for the clients.

Abstract: An article type adjustment approximation value for each physical article type of a plurality of physical article types of limited quantities is predetermined. A determination is made to assign to a selected client among a set of clients, a set of physical article types among the plurality of physical article types. A group of eligible physical article types for the selected client among the plurality of physical article types is identified. Using one or more machine learning prediction models, a desirability prediction value for each physical article type in the group of eligible physical article types is determined. The desirability prediction values are adjusted using the corresponding predetermined article type adjustment approximation value and a corresponding predetermined client adjustment approximation value to determine corresponding adjusted prediction values. The corresponding adjusted prediction values are used to determine the set of physical article types to be assigned to the selected client.

Abstract: A predicted size of a specific subject and a predicted size of a specific item are determined using one or more machine learning models. The machine learning models are trained using at least a specified size of the specific subject, feedback of the specific subject regarding sizing of a plurality of items, and feedback of other subjects regarding sizing of the plurality of items. The determined predicted size of the specific subject and the predicted size of the specific item are used to determine a predicted size fit between the specific item and the specific subject.

Abstract: Data about clients, items included in an item inventory, executable code for recommendation processes, and executable code for feature selection process(es), are stored. The recommendation processes are used to identify items in the item inventory that are recommended for specific clients, and the feature selection process(es) is/are used to select features that are displayed to entities tasked with selecting items from the item inventory for clients. Selection feedback and client feedback are received. One or more feature selection process(es) is/are modified based on the selection feedback and/or the client feedback, to thereby change which, where or/or how certain portion of the data about clients is displayed to the entities tasked with selecting items from the item inventory for the clients, and/or change which, where and/or how certain portion of the data about items is displayed to the entities tasked with selecting items from the item inventory for the clients.

Abstract: Computer implemented systems and methods intelligently distribute tasks received from clients among worker resources. One or more databases store information about worker resources and information about clients. A task assignment server, communicatively coupled to the database(s), receives a plurality of tasks that are to be performed for the clients, accesses the stored information about the worker resources, accesses the stored information about the clients, and assigns each of a majority of the tasks to one of the plurality of worker resources, in dependence on the information about the plurality of worker resources and in dependence on the information about the plurality of clients, so that the plurality of tasks are distributed among two or more of the plurality of worker resources. The system can also include a plurality of queues adapted to store information about tasks assigned to the worker resources associated with the queues.

Abstract: Systems and methods described herein employ adaptive machine learning to provide recommendations to an entity that selects one or more items for a client from an item inventory. Client information, item information, and recommendation algorithms are stored and are accessible by a recommendation engine. The recommendation algorithms utilize the client information and the item information in different manners to identify different subsets of items recommended for a client. Information about two or more of the subsets of the items in the item inventory that are identified are selected for display to the entity tasked with selecting items for the client.

Abstract: Computer implemented systems and methods intelligently distribute tasks received from clients among worker resources. One or more databases store information about worker resources and information about clients. A task assignment server, communicatively coupled to the database(s), receives a plurality of tasks that are to be performed for the clients, accesses the stored information about the worker resources, accesses the stored information about the clients, and assigns each of a majority of the tasks to one of the plurality of worker resources, in dependence on the information about the plurality of worker resources and in dependence on the information about the plurality of clients, so that the plurality of tasks are distributed among two or more of the plurality of worker resources. The system can also include a plurality of queues adapted to store information about tasks assigned to the worker resources associated with the queues.