SYSTEM FOR OPTIMIZING THE PERFORMANCE OF ONLINE ADVERTISEMENTS USING A NETWORK OF USERS AND ADVERTISERS
A system is described for optimizing the performance of online advertisements using a network of users and advertisers. The system may include a memory, an interface, and a processor. The memory may store a data representing a network comprised of queries linked to advertisements, a search query, a relevance value for each query, and a predicted weight for each advertisement. The interface may communicate with a plurality of users. The processor may be operatively connected to the memory and interface and may identify the network, and receive a query from a user, wherein the query exists in the network. The processor may calculate relevance values for the queries and use the queries with the highest relevance values to calculate a weight for each advertisement, the weight representing the relevance of the advertisement to the search query. The processor may then serve the advertisements with the highest weights to the user.
1 . A method for optimizing the performance of online advertisements using a network of users and advertisers, the method comprising:
identifying a network comprising a plurality of query items representing queries linked to a plurality of advertisement items representing advertisements via a plurality of query-advertisement link items wherein each query-advertisement link item comprises a weight representing the strength of the association between each linked query item and each linked advertisement item;
receiving a search query item from a user wherein the search query item exists in the plurality of query items;
calculating a relevance value for each additional query item in the plurality of query items based on the received search query item wherein the relevance value of each additional query item represents a relevance of each additional query item in the plurality of query items to the received search query item;
calculating a predicted weight for each advertisement item in the plurality of advertisement items based on the received search query item and the query items in the plurality of query items with the highest relevance values; and
serving the advertisement items in the plurality of advertisement items with the highest predicted weights to the user.
2 . The method of claim 1 wherein each query item in the plurality of query items comprises a search query performed by a user in a plurality of users.
3 . The method of claim 1 wherein each advertisement item in the plurality of advertisement items comprises an online advertisement of an advertiser.
4 . The method of claim 3 further comprising linking an advertisement item to a query item when a user clicks on the advertisement item after searching for the query item.
5 . The method of claim 4 further comprising calculating a weight of each query-advertisement link item based on a total number of click throughs attributable to each query-advertisement link item.
6 . The method of claim 5 wherein the total number of click throughs represents the number of times each user in the plurality of users clicked on the advertisement item associated with the query-advertisement link item after searching for the query item associated with the query-advertisement link item.
7 . The method of claim 1 wherein serving the advertisement items in the plurality of advertisement items with the highest predicted weights to the user further comprises:
adding the advertisement items in the plurality of advertisement items with the highest predicted weights to a page; and
serving the page to the user via an interface.
8 . The method of claim 1 wherein the calculating a predicted weight for each advertisement item in the plurality of advertisement items based on the received search query item and the query items in the plurality of query items with the highest relevance values further comprises calculating a predicted weight for each advertisement item in the plurality of advertisement items based on the received search query item and the query items in the plurality of query items with the N highest relevance values, further wherein N comprises any positive integer.
9 . The method of claim 1 wherein serving the advertisement items in the plurality of advertisement items with the highest predicted weights to the user further comprises serving the advertisement items in the plurality of advertisement items with the N highest predicted weights to the user, further wherein N comprises any positive integer.
10 . A method for optimizing the performance of online advertisements using a network of users and advertisers, the method comprising:
identifying a network comprising a plurality of query items representing queries wherein each query item is linked to a set of advertisement items representing advertisements;
receiving a first query item from a user wherein the first query item exists in the plurality of query items;
identifying a set of first-query advertisement items wherein the set of first-query advertisement items comprises the set of advertisement items linked to the first query item in the network;
selecting a second query item in the plurality of query items wherein the second query item is linked to at least one advertisement item in the set of first-query advertisement items;
identifying a set of second-query advertisement items wherein the set of second-query advertisement items comprises the set of advertisement items linked to the second query item in the network; and
serving the set of first-query advertisement items and the set of second-query advertisement items to the user.
11 . The method of claim 10 further comprising linking each advertisement item in the set of advertisement items to each query item in the plurality of query items when a user clicks on the advertisement item.
12 . The method of claim 10 wherein at least one advertisement item in the set of second-query advertisement items does not exist in the set of first-query advertisement items.
13 . The method of claim 12 wherein the query items in the plurality of query items are linked to the advertisement items in the sets of advertisement items via a plurality of query-advertisement link items.
14 . The method of claim 13 wherein each query-advertisement link in the plurality of query-advertisement links comprises a weight value indicating the strength of the relationship between the query and the advertisement linked by each query-advertisement link.
15 . The method of claim 14 further comprising calculating the weight of each query-advertisement link item in the plurality of query-advertisement link items based on the total number of click throughs attributable to each query-advertisement link item.
16 . The method of claim 14 wherein selecting a second query item in the plurality of query items further comprises:
identifying a plurality of second query items in the plurality of query items wherein each second query item is linked to at least one first-query advertisement item in the set of first-query advertisement items, further wherein a plurality of second query-advertisement link items comprises the plurality of query-advertisements link items linking the plurality of second query items and the set of first-query advertisement items; and
selecting the second query item from the plurality of second query items wherein the second query item comprises the second query item associated with the query-advertisement link item with the greatest weight value in the plurality of second query items.
17 . The method of claim 10 wherein the set of second-query advertisement items are served when at least one advertising slot on a page can not be filled by the set of first query advertisement items.
18 . A system for optimizing the performance of online advertisements using a network of users and advertisers, the system comprising:
a memory to store a data representing a network comprising a plurality of query items representing queries linked to a plurality of advertisement items representing advertisements via a plurality of query-advertisement link items, wherein each query-advertisement link item comprises a weight representing the strength of the relationship between each linked query item and each linked advertisement item, a search query item, a relevance value for each query item in the plurality of query items, and a predicted weight for each advertisement item in the plurality of advertisement items;
an interface connected to the memory, the interface operative to communicate with a plurality of users; and
a processor operatively connected to the memory and the interface, the processor operative to identify the data representing the network, receive the search query item from the user wherein the search query item exists in the plurality of query items, calculate the relevance value for each additional query item in the plurality of query items based on the received search query item wherein the relevance value of each additional query item represents a relevance of each additional query item in the plurality of query items to the received search query item, calculating the predicted weight for each advertisement item in the plurality of advertisement items based on the received search query item and the query items in the plurality of query items with the highest relevance values, and serve the advertisement items in the plurality of advertisement items with the highest predicted weights to the user.
19 . The system of claim 18 wherein each query item in the plurality of query items comprises a search query performed by at least one user in the plurality of users.
20 . The system of claim 18 wherein each advertisement item in the plurality of advertisement items comprises an online advertisement of an advertiser.
21 . The system of claim 18 wherein the processor links the advertisement item in the plurality of advertisement items to the query item in the plurality of query items if at least one of the plurality of users clicked on the advertisement item after searching for the query item.
22 . The system of claim 18 wherein the weight of each query-advertisement link item comprises a total number of click throughs attributable to each query-advertisement link item.
23 . The system of claim 18 wherein the query items in the plurality of query items with the N highest relevance values are used to predict the weight for each advertisement item in the plurality of advertisement items, further wherein N comprises any positive integer.
24 . The system of claim 18 wherein the advertisement items in the plurality of advertisement items with the N highest predicted weights are served to the user, further wherein N comprises any positive integer.