Abstract: Systems and methods described herein, which utilize a combination of batch-processing and on-demand processing to provide recommendations, can include database(s) that store client data, item data, and executable code for composable algorithms that utilize the client data and/or the item data to provide recommendations. The system also includes a batch-process results data store that stores results of composable algorithms executed using batch-processing. Additionally, the system includes an algorithm engine that accepts calls to composable algorithms and output results thereof. The algorithm engine determines which called composable algorithms are designated as being executable using batch-processing, and which are designated as being executable using on-demand processing.

Abstract: Systems and methods described herein, which utilize a combination of batch-processing and on-demand processing to provide recommendations, can include database(s) that store client data, item data, and executable code for composable algorithms that utilize the client data and/or the item data to provide recommendations. The system also includes a batch-process results data store that stores results of composable algorithms executed using batch-processing. Additionally, the system includes an algorithm engine that accepts calls to composable algorithms and output results thereof. The algorithm engine determines which called composable algorithms are designated as being executable using batch-processing, and which are designated as being executable using on-demand processing.

Abstract: Systems and methods described herein, which utilize a combination of batch-processing and on-demand processing to provide recommendations, can include database(s) that store client data, item data, and executable code for composable algorithms that utilize the client data and/or the item data to provide recommendations. The system also includes a batch-process resultsP data store that stores results of composable algorithms executed using batch-processing. Additionally, the system includes an algorithm engine that accepts calls to composable algorithms and output results thereof. The algorithm engine determines which called composable algorithms are designated as being executable using batch-processing, and which are designated as being executable using on-demand processing.

Abstract: Systems and methods described herein, which utilize a combination of batch-processing and on-demand processing to provide recommendations, can include database(s) that store client data, item data, and executable code for composable algorithms that utilize the client data and/or the item data to provide recommendations. The system also includes a batch-process resultsP data store that stores results of composable algorithms executed using batch-processing. Additionally, the system includes an algorithm engine that accepts calls to composable algorithms and output results thereof. The algorithm engine determines which called composable algorithms are designated as being executable using batch-processing, and which are designated as being executable using on-demand processing.

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: 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.