Sponsored online content management using query clusters
Aspects of the subject disclosure are directed towards managing sponsored online content based upon advertiser behavior. Defining mini-markets to represent such advertiser behavior may be accomplished by clustering queries that generate revenue from one or more campaigns. Query revenue data between queries and a set of campaigns may be used to determine such mini-markets. To illustrate, a query whose highest revenue is attributed to a campaign may be selected for that campaign's mini-market. When that query is entered as a search term, the campaign's mini-market helps allocate space for advertisements.
1. One or more computer-readable storage hardware devices having computer-executable instructions, which when executed perform operations comprising:
accessing query related data between a set of advertisers and a set of queries, the query related data being retrieved from a query related data store;
grouping queries into a cluster for each advertiser in which each cluster comprises a first set of queries that include queries previously bid on by the advertiser or a second set of queries that include queries the advertiser has previously spent money on to build a layer of a tree structure;
generating a set of communities in which each community comprises at least one query cluster and corresponds to a subset of the set of advertisers;
determining a modularity corresponding to the communities;
increasing modularity amongst the community until the tree structure reaches a maximum modularity;
using the tree structure to generate mini-market data describing each cluster on a densest layer of the tree structure as an individual mini-market, each minimarket representing a set of queries related to a set of advertisers based upon advertiser behavior;
determining an optimal set of auction parameters for each cluster on the densest layer of the tree structure;
establishing a search auction for the set of advertisers based on one or more of the mini-markets and the optimal set of auction parameters; and
presenting content associated with an advertiser from the set of advertisers at a particular location on a search result page based on the search auction.
2. The one or more computer-readable storage hardware devices of claim 1 , wherein the second set of queries have had more than a threshold number of clicks.
3. The one or more computer-readable storage hardware devices of claim 1 , wherein the first set of queries and the second set of queries include one or more queries that have provided revenue above a defined threshold.
4. The one or more computer-readable storage hardware devices of claim 1 wherein merging the communities into another set of communities forms a new layer on a tree structure.
5. The one or more computer-readable storage hardware devices of claim 4 having further computer-executable instructions comprising:
adding layers to the tree structure until the tree structure achieves stability; and
establishing auction parameters based upon the tree structure.
6. The one or more computer-readable storage hardware devices of claim 5 having further computer-executable instructions comprising:
updating the auction parameters when the tree structure changes.
7. The one or more computer-readable storage hardware device of claim 4 , wherein a top layer of the tree structure is characterized by a division of nodes within which network connections are dense but between which network connections are more sparse.
8. A method performed at least in part on at least one processor, the method comprising:
accessing query related data between a set of advertisers and a set of queries, the query related data being retrieved from a query related data store;
grouping queries into a cluster for each advertiser in which each cluster comprises at least one of a set of queries that include queries previously bid on by the advertiser or a second set of queries that includes queries the advertiser has previously spent money on to build a layer of a tree structure;
generating a set of communities in which each community comprises at least one query cluster and corresponds to a subset of the set of advertisers;
determining a modularity corresponding to the communities;
increasing modularity amongst the community until the tree structure reaches a maximum modularity;
using the tree structure to generate mini-market data describing each cluster on a densest layer of the tree structure as an individual mini-market, each minimarket representing a set of queries related to a set of advertisers based upon advertiser behavior;
determining an optimal set of auction parameters for each cluster on the densest layer of the tree structure;
establishing a search auction for the set of advertisers based on one or more of the mini-markets and the optimal set of auction parameters; and
presenting content associated with an advertiser from the set of advertisers at a particular location on a search result page based on the search auction.
9. The method of claim 8 , wherein the second set of queries have had more than a threshold number of clicks.
10. The method of claim 9 , wherein a top layer of the tree structure is characterized by a division of nodes within which network connections are dense but between which network connections are more sparse.
11. The method of claim 8 , wherein the first set of queries and the second set of queries include one or more queries that have provided revenue above a defined threshold.
12. The method of claim 8 , wherein merging the communities into another set of communities forms a new layer on a tree structure.
13. The method of claim 12 , wherein the method further comprises:
adding layers to a tree structure until the tree structure achieves stability; and
establishing auction parameters based upon the tree structure.
14. The method of claim 13 , further comprising updating the auction parameters when the tree structure changes.
15. A system comprising:
a memory for storing query related data for advertisers; and
a processor coupled to the memory, the processor programmed to:
access, from the memory, query related data between a set of advertisers and a set of queries, the query related data being retrieved from the memory;
group queries into a cluster for each advertiser in which each cluster comprises at least one of a set of queries that include queries previously bid on by the advertiser or a second set of queries that includes queries the advertiser has previously spent money on to build a layer of a tree structure;
generate a set of communities in which each community comprises at least one query cluster and corresponds to a subset of the set of advertisers;
determine a modularity corresponding to the communities;
increase modularity amongst the community until the tree structure reaches a maximum modularity;
use the tree structure to generate mini-market data describing each cluster on a densest layer of the tree structure as an individual mini-market, each minimarket representing a set of queries related to a set of advertisers based upon advertiser behavior;
determine an optimal set of auction parameters for each cluster on the densest layer of the tree structure;
establish a search auction for the set of advertisers based on one or more of the mini-markets and the optimal set of auction parameters; and
present content associated with an advertiser from the set of advertisers at a particular location on a search result page based on the search auction.
16. The system of claim 15 , wherein the second set of queries have had more than a threshold number of clicks.
17. The system of claim 15 , wherein merging the communities into another set of communities forms a new layer on a tree structure.
18. The system of claim 17 , wherein the processor is further programmed to:
add layers to the tree structure until the tree structure achieves stability; and
establish auction parameters based upon the tree structure.
19. The system of claim 18 , wherein the processor is further programmed to update the auction parameters when the tree structure changes.