Inventory clustering

Abstract

The invention describes an inventory clustering. Various embodiments provide techniques for inventory clustering. In one or more embodiments, a set of inventory to be processed is placed into an initial cluster. The inventory can be related to impressions for advertising that are defined by values for a set of attributes. Recursive division of the initial cluster is performed by selecting an attribute and deriving child clusters that are constrained by one or more values of the attributes in accordance with one or more clustering algorithms. The clustering algorithms are configured to derive an optimum number of clusters by repetitively generating smaller child clusters and measuring a cost associated with adding additional clusters. Additional child clusters can be formed in this manner until the measured cost to add more clusters outweighs a benefit of adding more clusters.

Description

technical field [0001] The present invention relates to computer communication technology, and in particular to inventory clustering. Background technique [0002] In display advertising on an online resource, an advertiser makes an order targeting specific customers who are matched with a list() representing customer attributes defined by the order, such as impression(). Assigning orders to listings is a complex constrained satisfaction problem, and for large numbers of available listings (e.g., billions per day) and / or large numbers of advertiser orders (tens of thousands) that can be associated with a service provider, solving this problem precisely in Computationally expensive. When approximations are made to simplify the allocation problem and arrive at an allocation solution, these approximations can result in lost revenue as advertisers fail to achieve the impression goals associated with their orders. Inefficient allocation also prevents a service provider from sel...

AI Technical Summary

Problems solved by technology

[0003] Although, as mentioned above, this type of listing matching can become quite difficult as the number of advertisers and / or impressions to match becomes larger due to the amount of calculations involved in performing the matching

Therefore, conventional algorithms adapted to perform matching with relatively small datasets may not be successful for the large datasets that arise in some cases

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