INTEREST-BASED LOCATION TARGETING ENGINE

• US 20100185642A1
• Filed: 01/21/2009
• Published: 07/22/2010
• Est. Priority Date: 01/21/2009
• Status: Abandoned Application

• Current Assignee: Excalibur IP, LLC (Acacia Research Corporation)
• Original Assignee: Yahoo! Inc. (Apollo Global Management, Inc.)
• Inventors: King, Simon P., Davis, Marc Eliot, Higgins, Christopher William, Nair, Rahul, Burgener, Carrie, Paretti, Christopher Todd
• Application Number:
  US12/357,345
• Publication Number:
  US 20100185642A1
• Time in Patent Office: Days
• US Class Current: 707/758
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation

Abstract

A method for targeted advertisement is provided, for which one or more tags relating to an advertisement is/are determined, one or more of the most representative entities for the tag(s) is/are determined, and the advertisement is targeted to the one or more most representative entities. In addition, for each of a plurality of tags, one or more of the most representative entities is/are determined based on term frequency-inverse document frequency, such that an entity is relatively more representative of a tag if the tag is more uniquely and/or frequently associated with the entity. For each tag, the associated entities may be divided into multiple categories, such that one or more most representative entities within each category is/are determined for each tag.

Claims

1. A method, comprising:

accessing at least one tag relating to an advertisement, wherein the at least one tag is among a plurality of tags;

selecting at least one entity that is most representative of the at least one tag; and

targeting the advertisement to the at least one entity.

2. A method as recited in claim 1, further comprising:

for each of the plurality of tags, determining at least one most representative entity, wherein an entity is relatively more representative of a tag if the tag is at least relatively more uniquely associated with the entity.

3. A method as recited in claim 2, wherein an entity is relatively more representative of a tag if the tag is further relatively more frequently associated with the entity.

4. A method as recited in claim 2, wherein the at least one most representative entity for a tag is determined based on a term frequency-inverse document frequency weight of each of the at least one most representative entity.

5. A method as recited in claim 1, further comprising:

for each of the plurality of tags, determining at least one most representative entity for a group of users,

wherein an entity is relatively more representative of a tag if the tag is relatively more uniquely and relatively more frequently associated with the entity, and

wherein only entities with which the group of users has associated tags are analyzed to determine the at least one most representative entity.

6. A method as recited in claim 1, further comprising:

parsing the advertisement to determine the at least one tag related to the advertisement.

7. A method as recited in claim 1, further comprising:

receiving the at least one tag related to the advertisement.

8. A method as recited in claim 1, further comprising:

delivering the advertisement to the at least one entity.

9. A method as recited in claim 1, wherein each of the at least one entity exists in a real world or a virtual world.

10. A method as recited in claim 9, wherein each of the at least one entity is one selected from the group consisting of:

a geographical location, a virtual location, a person, a user ID, a time interval, a point in time, an event, an activity, a topic, and a concept.

11. A method as recited in claim 9, wherein each of the at least one entity is one selected from the group consisting of:

a geographical location, a virtual location, a time interval, a point in time, and an activity.

12. A method as recited in claim 1, wherein if the at least one tag related to the advertisement comprises two or more tags, then the at least one entity that is most representative of the at least one tag is at least one common entity that is most representative to each of the at least one tag.

13. A method, comprising:

accessing at least one tag relating to an advertisement, wherein the at least one tag is among a plurality of tags;

selecting at least one geographical location that is most representative of the at least one tag, wherein a geographical location is relatively more representative of a tag if the tag is relatively more uniquely and frequently associated with the geographical location; and

targeting the advertisement at least to the at least one geographical location.

14. A method as recited in claim 13, further comprising:

selecting at least one time that is most representative of the at least one tag, wherein a time is relatively more representative of a tag if the tag is relatively more uniquely and frequently associated with the time; and

targeting the advertisement further to the at least one time.

15. A method as recited in claim 14, further comprising:

selecting at least one person that is most representative of the at least one tag, wherein a person is relatively more representative of a tag if the tag is relatively more uniquely and frequently associated with the person; and

targeting the advertisement further to the at least person.

16. A method as recited in claim 15, wherein targeting the advertisement to the at least one geographical location, the at least one time, and the at least one person comprises delivering the advertisement to each of the at least one person only during one of the at least one time and while the person is within one of the at least one geographical location.

17. A method as recited in claim 15, further comprising:

for each of the plurality of tags, determining at least one most representative geographical location, at least one most representative time, and at least one most representative person using term frequency-inverse document frequency.

18. A method as recited in claim 14, further comprising:

receiving an advertising offer with respect to the advertisement and desirable target categories;

parsing the advertisement to automatically determine the at least one tag related to the advertisement; and

providing at least one most representative location, time, and person based on the desirable target categories.

19. A method as recited in claim 14, further comprising:

receiving an advertising offer with respect to the advertisement and desirable target categories;

receiving the at least one tag related to the advertisement; and

providing at least one most representative location, time, and person based on the desirable target categories.

20. A computer program product comprising a computer-readable medium having a plurality of computer program instructions stored therein, wherein the plurality of computer program instructions are operable to cause at least one computing device to:

access at least one tag relating to an advertisement, wherein the at least one tag is among a plurality of tags;

select at least one entity that is most representative of the at least one tag; and

target the advertisement to the at least one entity.

21. A computer program product as recited in claim 20, wherein the plurality of computer program instructions are further operable to:

for each of the plurality of tags, determine at least one most representative entity, wherein an entity is relatively more representative of a tag if the tag is relatively more uniquely and more frequently associated with the entity.

22. A computer program product as recited in claim 20, wherein the at least one entity comprises at least one geographical location, at least one time, and at least one person, and wherein target the advertisement to the at least one entity comprises deliver the advertisement to each of the at least one person only during one of the at least one time and while the person is within one of the at least one geographical location.