Multi-level, cluster-based optimization techniques
Abstract:
Techniques are disclosed relating to multi-level, cluster-based optimization. In various embodiments, the disclosed techniques include performing a multi-level optimization operation to optimize the composition of a cluster of collections. For example, in various embodiments, the disclosed techniques include iteratively filtering a (typically large) set of available items down into progressively smaller subsets, from which items may be selected to modify the composition of the collections in a cluster of related collections. In some embodiments, the disclosed techniques include performing multi-level optimization operation that includes a collection-level optimization calculation for individual collections in the cluster, and, after filtering the set of available items based on the collection-level optimization calculations, performing a cluster-level optimization calculation. Based on this cluster-level optimization calculation, the disclosed techniques may determine a set of modifications to perform to optimize the composition of the collections in the cluster.
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