METHOD FOR PROVIDING ADVERTISEMENT, COMPUTER-READABLE MEDIUM INCLUDING PROGRAM FOR PERFORMING THE METHOD AND ADVERTISEMENT PROVIDING SYSTEM

• US 20120310737A1
• Filed: 09/01/2011
• Published: 12/06/2012
• Est. Priority Date: 06/03/2011
• Status: Abandoned Application

• Current Assignee: Korea Advanced Institute of Science and Technology
• Original Assignee: Korea Advanced Institute of Science and Technology
• Inventors: SONG, Junehwa, Ha, Jin-Young, Rhee, Yunseok, Kim, Byoungjip, Lee, SangJeong, Kang, Seungwoo, Lee, Youngki
• Application Number:
  US13/224,062
• Publication Number:
  US 20120310737A1
• Time in Patent Office: Days
• US Class Current: 705/14.57
• CPC Class Codes: G06Q 30/0261 based on user location

Abstract

A visit-pattern-aware mobile advertising system is provided for urban commercial complexes. In order to provide highly relevant advertisements to mobile users, next visit place is predicted through a probabilistic reasoning technique based on the collected visit place history, and an advertisement to be provided is selected based on the predicted next visit place. Since the advertisement is selectively provided according to the next visit place predicted based on a visit place history of an advertisement receiver, it is possible to increase an advertisement effect.

Claims

1. A method for providing an advertisement, comprising:

collecting a visit place history of a mobile apparatus;

predicting a next visit place through a probabilistic reasoning technique based on the collected visit place history; and

selecting an advertisement to be provided based on the predicted next visit place.

2. The method of claim 1, wherein the collecting of the visit place history includes:

detecting a current location of the mobile apparatus; and

generating the visit place history of the mobile apparatus based on the detected current location.

3. The method of claim 2, wherein the detecting of the current location of the mobile apparatus includes:

scanning a Wi-Fi signal through the mobile apparatus;

generating a Wi-Fi fingerprint of the scanned Wi-Fi signal;

selecting Wi-Fi data based on the generated Wi-Fi fingerprint by searching a Wi-Fi database; and

measuring the current location of the mobile apparatus based on the selected Wi-Fi data.

4. The method of claim 3, wherein the Wi-Fi data includes signal strength of a Wi-Fi access point (AP), an identification number of the Wi-Fi access point (AP), and locational information of the Wi-Fi access point.

5. The method of claim 3, wherein the generating of the visit place history of the mobile apparatus includes:

measuring a duration of the measured current location;

comparing the measured duration with a visit threshold time; and

regarding the measured current location as a visit place when the measured duration is more than the visit threshold time.

6. The method of claim 5, wherein the generating of the visit place history of the mobile apparatus further includes setting the visit threshold time to be compared with the measured duration.

7. The method of claim 1, wherein the predicting of the next visit place through the probabilistic reasoning technique includes probabilistically predicting the next visit place through a Bayesian network.

8. The method of claim 7, wherein:

the Bayesian network is modeled by including a plurality of visit places as variables, and

the predicting of the next visit place through the Bayesian network based on the collected visit place history includes;

calculating a visit probability distribution for the plurality of visit places through the Bayesian network based on the one or more visit place histories; and

determining a priority of the next visit place based on the calculated visit probability distribution.

9. The method of claim 8, wherein the Bayesian network for calculating the probability distribution is learned based on the visit place histories of the corresponding advertisement receiver.

10. The method of claim 8, wherein the Bayesian network for calculating the probability distribution is learned based on the visit place histories of other advertisement receivers.

11. The method of claim 8, wherein:

the Bayesian network is modeled by further including the ages of the advertisement receiver as a variable, and

the visit probability distribution is calculated based on the one or more visit place histories and the ages.

12. The method of claim 8, wherein:

the Bayesian network is modeled by further including the gender of the advertisement receiver as a variable, and

the visit probability distribution is calculated based on the one or more visit place histories and the gender.

13. The method of claim 8, wherein:

the Bayesian network is modeled by further including a current time as a variable, and

the visit probability distribution is calculated based on the one or more visit place histories and the current time.

14. The method of claim 8, wherein:

the Bayesian network is modeled by further including visit duration as a variable, and

the visit probability distribution is calculated based on the one or more visit place histories and the visit duration.

15. The method of claim 8, wherein the selecting of the advertisement to be provided includes preparing an advertisement list relating to the next visit place based on the determined priority.

16. The method of claim 15, further comprising providing advertisements included in the prepared advertisement list to the mobile apparatus according to the priority.

17. The method of claim 16, further comprising collecting advertisement providing statistics data relating to the provided advertisement.

18. The method of claim 17, wherein the advertisement providing statistics data includes at least one of the total number of issued provided advertisements, the number of times in which the advertisement receiver uses the provided advertisement, and the number of times of purchases generated by providing the advertisement.

19. A computer-readable recording medium including a program for executing an advertisement providing method, wherein the advertisement providing method includes:

collecting a visit place history of a mobile apparatus;

predicting a next visit place through a probabilistic reasoning technique based on the collected visit place history; and

selecting an advertisement to be provided based on the predicted next visit place.

20. The computer-readable recording medium of claim 19, wherein the visit place history is collected by a Wi-Fi fingerprint of a Wi-Fi signal received through the mobile apparatus.

21. The computer-readable recording medium of claim 19, wherein:

the predicting of the next visit place through the probabilistic reasoning technique based on the collected visit place history includes probabilistically predicting the next visit place based on a conditional probability distribution of a Bayesian network, and

a probability distribution model of the Bayesian network is learned based on the visit place histories of advertisement receivers.

22. The computer-readable recording medium of claim 19, wherein the advertisement providing method further includes providing the selected advertisement to the advertisement receiver.

23. An advertisement providing system, comprising:

an advertising client collecting a visit place history to provide the collected visit place history to an advertising server and receiving an advertisement from the advertising server; and

the advertising server predicting a next visit place through a Bayesian network based on the visit place history received from the advertising client and providing the advertisement to the advertising client based on the predicted next visit place.

24. The system of claim 23, wherein the advertising client includes:

a location detector detecting a current location; and

a visit history generator generating the visit place history based on the detected current location.

25. The system of claim 24, wherein the location detector

generates a Wi-Fi fingerprint by scanning a Wi-Fi signal, and

selects Wi-Fi data having high relevance with the generated Wi-Fi fingerprint and measures the current location based on the selected Wi-Fi data.

26. The system of claim 25, wherein the visit history generator measures a duration of the measured current location and regards the current location as a visit place when the duration is more than a visit threshold time.

27. The system of claim 23, wherein the advertising server includes:

a visit history manager managing the visit place history provided from the advertising client;

a visit place predictor predicting the next visit place through the Bayesian network based on the visit place history provided from the visit history manager; and

an advertisement selector selecting an advertisement to be provided based on the next visit place predicted from the visit place predictor to provide the selected advertisement to the advertising client.

28. The system of claim 27, wherein:

the advertising server further includes a visit history database, and

the visit history manager stores the visit place history provided from the advertising client in the visit history database and searches the visit history in the visit history database to provide the searched visit history to the visit place predictor.

29. The system of claim 27, wherein:

the advertising server further includes an advertisement database,

the advertisement database stores a plurality of advertisements, and

the advertisement selector selects at least one advertisement from the plurality of advertisements stored in the advertisement database based on the next visit place to provide the selected advertisement to the advertising client.

30. The system of claim 27, wherein:

the advertising server further includes an advertisement usage statistics database, and

the advertising server stores advertisement providing statistics data provided from the advertising client in the advertisement usage statistics database.