Patent Application
20120310737
This application claims priority under 35 U.S.C. §119 to Korean Patent Application No. 10-2011-0053693, filed on Jun. 3, 2011, in the Korean Intellectual Property Office, the disclosure of which is incorporated herein by reference in its entirety.
The present invention relates to a method for providing advertisement, and more particularly, to a method for providing an advertisement through mobile apparatuses, a computer-readable medium including a program for performing the method, and an advertisement providing system.
As smartphones have become prevalent, mobile advertising is getting significant attention as being not only a killer application in future mobile commerce, but also as an important business model of emerging mobile applications to monetize them.
Accordingly, research on more efficient and effective mobile advertising is strongly required to meet the requirements of both advertisers and mobile users in the upcoming new mobile era.
An exemplary embodiment of the present invention provides a method for providing an advertisement including: 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.
The collecting of the visit place history may include: detecting a current location of the mobile apparatus; and generating the visit place history of the mobile apparatus based on the detected current location.
The detecting of the current location of the mobile apparatus may include: 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.
The Wi-Fi data may include a 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.
The generating of the visit place history of the mobile apparatus may include: 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.
The generating of the visit place history of the mobile apparatus may further include setting the visit threshold time to be compared with the measured duration.
The predicting of the next visit place through the probabilistic reasoning technique may include probabilistically predicting the next visit place through a Bayesian network.
The Bayesian network may be modeled by including a plurality of visit places as variables. The predicting of the next visit place through the Bayesian network based on the collected visit place history may include: 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.
The probability distribution model of the Bayesian network may be learned based on the visit place histories of the corresponding advertisement receiver.
The probability distribution model of the Bayesian network may be learned based on the visit place histories of other advertisement receivers.
The Bayesian network may be modeled by further including the ages of the advertisement receiver as a variable. The visit probability distribution may be calculated based on the one or more visit place histories and the ages.
The Bayesian network may be modeled by further including the gender of the advertisement receiver as a variable. The visit probability distribution may be calculated based on the one or more visit place histories and the gender.
The Bayesian network may be modeled by further including a current time as a variable. The visit probability distribution may be calculated based on the one or more visit place histories and the current time.
The Bayesian network may be modeled by further including a visit duration as a variable. The visit probability distribution may be calculated based on the one or more visit place histories and the visit duration.
The selecting of the advertisement to be provided may include preparing an advertisement list relating to the next visit place based on the determined priority.
The method may further include providing advertisements included in the prepared advertisement list to the mobile apparatus according to the priority.
The method may further include collecting advertisement providing statistics data relating to the provided advertisement.
The advertisement providing statistics data may include 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.
Another exemplary embodiment of the present invention provides a computer-readable recording medium including a program for executing an advertisement providing method. 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.
The visit place history may be collected by a Wi-Fi fingerprint of a Wi-Fi signal received through the mobile apparatus.
The predicting of the next visit place through the probabilistic reasoning technique based on the collected visit place history may include probabilistically predicting the next visit place based on a conditional probability distribution of a Bayesian network. The next visit place may be determined based on the conditional probability distribution of the Bayesian network. The probability distribution model of the Bayesian network may be learned based on the visit place histories of advertisement receivers.
The advertisement providing method may further include providing the selected advertisement to the advertisement receiver.
Yet another exemplary embodiment of the present invention provides an advertisement providing system including: an advertising client and an advertising server. The advertising client collects a visit place history to provide the collected visit place history to an advertising server and receives an advertisement from the advertising server. The advertising server predicts a next visit place through a Bayesian network based on the visit place history received from the advertising client and provides the advertisement to the advertising client based on the predicted next visit place.
The advertising client may include: a location detector; and a visit history generator. The location detector may detect a current location. The visit history generator may generate the visit place history based on the detected current location.
The location detector may generate a Wi-Fi fingerprint by scanning a Wi-Fi signal, and select Wi-Fi data based on the generated Wi-Fi fingerprint and measure the current location based on the selected Wi-Fi data.
The visit history generator may measure a duration of the measured current location and regard the current location as a visit place when the duration is more than a visit threshold time.
The advertising server may include: a visit history manager; a visit place predictor; and an advertisement selector. The visit history manager may manage the visit place history provided from the advertising client. The visit place predictor may predict the next visit place based on the visit place history provided from the visit history manager. The advertisement selector may select 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.
The advertising server may further include a visit history database. The visit history manager may store the visit place history provided from the advertising client in the visit history database. The visit history manager may search the visit history in the visit history database to provide the searched visit history to the visit place predictor.
The advertising server may further include an advertisement database. The advertisement database may store a plurality of advertisements. The advertisement selector may select 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.
The advertising server may further include an advertisement usage statistics database. The advertising server may store advertisement providing statistics data provided from the advertising client in the advertisement usage statistics database.
Other features and aspects will be apparent from the following detailed description, the drawings, and the claims.
Hereinafter, exemplary embodiments will be described in detail with reference to the accompanying drawings. Throughout the drawings and the detailed description, unless otherwise described, the same drawing reference numerals will be understood to refer to the same elements, features, and structures. The relative size and depiction of these elements may be exaggerated for clarity, illustration, and convenience. The following detailed description is provided to assist the reader in gaining a comprehensive understanding of the methods, apparatuses, and/or systems described herein. Accordingly, various changes, modifications, and equivalents of the methods, apparatuses, and/or systems described herein will be suggested to those of ordinary skill in the art. Also, descriptions of well-known functions and constructions may be omitted for increased clarity and conciseness.
Large-scale commercial complexes have huge potential for mobile advertising. COEX Mall, the largest commercial complex in South Korea, has more than 260 stores and attracts more than a hundred thousand visitors per day. From an advertisers' perspective, commercial complexes are strategically important places for advertising, because many people visit a commercial complex for the purpose of purchasing products or services. From a customers' perspective, mobile advertising can also help them to use a commercial complex in a more convenient way, since there are too many places to know well in detail.
Customer targeting may play a key role in order to effectively provide mobile advertisements in such a commercial complex. Through customer targeting, advertisers can identify people who will highly likely purchase a product and a service. Then, they can increase the effectiveness of the advertisement by focusing their efforts on those people. Also, customers can avoid spam ads.
Especially, for customer targeting at a commercial complex, spatial and temporal relevance of ads should be considered. If the category of an advertised place is interesting to a user and the place is closely located to the user, the ad will easily attract the user to visit the advertising place (spatial relevance). Also, if an advertised product or service will be highly likely consumed by a user soon, the ad will be able to immediately lead the user to actually purchase the product or service (temporal relevance).
Existing location-based advertisement delivers ads of nearby places depending on a user's current location. For example, as shown in
However, the existing location-based advertisement is highly limited in effective targeting since the advertisement having low relevance is provided to the advertisement receiver 110. For example, if the advertisement receiver 110 is having a meal at the current location, ads for restaurants 130 and 140 would not be appealing.
According to an embodiment of the present invention, users' next visit place is predicted based on the places where the advertisement receiver has visited and spatially and temporally relevant ads are provided to the user to increase the advertising effect.
Referring to
In the advertisement providing method according to the exemplary embodiment of the present invention, a visit place history of the advertisement receiver is collected to predict the next visit place. Since an advertisement for a category relating to the predicted next visit place is provided to the advertisement receiver, the advertising effect increases. That is, in the example shown in
Referring to
The visit place history may be collected by the mobile apparatus. The mobile apparatus may be a smartphone, and further, may be a portable electronic apparatus such as a portable media player (PMP), an MP3 player, or a tablet computer which can be used to provide the advertisement to the user. In the exemplary embodiment, the visit place history may be collected by a global positioning system (GPS) mounted on the mobile apparatus. Or, the visit place history may be collected by a Wi-Fi receiver mounted on the mobile apparatus. A method for collecting the visit place history will be described below with reference to
It is difficult to predict the next visit place with certainty, because there is inherent uncertainty in human behaviors. Therefore, a probabilistic prediction model based on the collected visit place history is used. There are various probabilistic reasoning techniques such as a decision tree or conditional random fields (CRF). In the exemplary embodiment, as the probabilistic reasoning technique for predicting the next visit place, a Bayesian network may be used. A method for predicting the next visit place through the Bayesian network will be described with reference to
After the next visit place of the advertisement receiver is predicted, the advertisement to be provided to the advertisement receiver can be selected based on the predicted place. The selected advertisement may be one, or a plurality of advertisements may be selected with ranks. In an exemplary embodiment, when two or more advertisements are selected, the selected advertisements may be grouped for each category. Alternatively, the plurality of selected advertisements may be arranged depending on a distance from a current location.
Referring to
In this specification, a location is distinguished from a place. The location means a specific point having a coordinate detected by the mobile apparatus. Meanwhile, the place means a specific zone separated through a partition for a specific purpose. The place may include a plurality of coordinates and the place can be a cinema, restaurant, etc. In the advertisement providing method according to the exemplary embodiment of the present invention, in order to predict the next visit place, not just a history regarding the location but a detailed visit place, a detailed visit time, and a visit duration may be required. Since the location searched through the GPS or Wi-Fi receiver may not include information on the place, an operation for verifying a visit place where the mobile apparatus stays based on the detected current location may be required in order to collect the visit place history.
Specifically, the current location may be detected using a Wi-Fi communication system. General location system using a radio signal calculates coordinates, or latitude and longitude of the current location through a triangulation technique based on a plurality of radio signals received simultaneously. To verify the visit place where the mobile apparatus stays through a Wi-Fi communication system, Wi-Fi fingerprint is generated from the Wi-Fi signal and a Wi-Fi fingerprint database is searched based on the Wi-Fi fingerprint. A reference Wi-Fi fingerprint (we will call it Wi-Fi data) which is the most similar to the generated Wi-Fi fingerprint is searched from the Wi-Fi fingerprint database and a place associated with the searched fingerprint may be determined as the current visit place. A detailed method for verifying the current visit place through the Wi-Fi communication system will be described below with reference to
Referring to
In order to collect a visit place history of the mobile apparatus, the current location of the mobile apparatus may be detected. In the exemplary embodiment of the present invention, a Wi-Fi localization system may be used to detect the current location of the mobile apparatus. Furthermore, a GPS localization or Bluetooth localization technique may be used. However, since the Wi-Fi localization system can accurately detect the location even in an indoor place such as a large-scale commercial complex and has relatively high localization accuracy as compared with the GPS localization system, the Wi-Fi localization system may be used. As compared with the Bluetooth localization technology, since a plurality of Wi-Fi access points have already been deployed in the commercial complex, additional equipments do not need to be installed for localization.
The Wi-Fi fingerprint of the Wi-Fi signal may include information on the Wi-Fi access point (AP) and a received signal strength indicator (RSSI) included in one or more Wi-Fi signals received by the mobile apparatus. The information may include a media access control (MAC) address of the Wi-Fi access point. The Wi-Fi database may be searched by generating the Wi-Fi fingerprint of the received Wi-Fi signal. The Wi-Fi database as a database regarding the Wi-Fi fingerprint of the Wi-Fi signal may manage connection between the Wi-Fi fingerprint and a relevant visit place. To this end, the Wi-Fi database may include locational information of each Wi-Fi access point deployed in the commercial complex. The reference Wi-Fi fingerprint (we will call it Wi-Fi data) which is the most similar to the generated Wi-Fi fingerprint of the Wi-Fi signal may be selected by searching the Wi-Fi database. In the exemplary embodiment, the Wi-Fi data (i.e., the reference Wi-Fi fingerprint) may include the signal strength, an identification number, and the locational information of the Wi-Fi access point. When the Wi-Fi data includes the locational information of the corresponding Wi-Fi access point, the place relevant to the Wi-Fi fingerprint may be determined as the current visit place by measuring the location of the mobile apparatus based on the locational information.
Referring to
In order to selectively provide the advertisement based on the visit location history, it is not enough to search the current location of the mobile apparatus, and a process of verifying which place is visited in detail based on the current location may be further required. To this end, a process of detecting a place-in event and a place-out event may be required through the periodic detection on current location.
In the exemplary embodiment of the present invention, a place identification (ID) based on the detected current location may be searched in order to generate the visit place history. The place ID may be a serial number regarding a specific place and may include locations in a compartment of the specific place. For example, when the detected current location belongs to the inside of a specific restaurant, an ID of the restaurant may be searched as the place ID.
In case of updating the place ID by periodic search on the current location, the visit place history may be generated by recognizing the place as the visit place when the place ID is maintained without change for a predetermined time or more. For example, when an advertisement receiver having a mobile apparatus temporarily enters a restaurant to ask a menu and comes out, a place ID corresponding to the restaurant may be temporarily searched, but in this case, since there is no action of taking a meal at the restaurant, the case may not be recognized as ‘visiting’ the restaurant. That is, when the advertisement receiver stays at a specific place for a certain amount of time, the place is recognized as the visit place to generate the visit place history.
For example, when the place ID of the restaurant searched based on the current location is maintained for only a short time such as one minute, probability that the advertisement receiver took a meal at the restaurant may be relatively low. On the contrary, when the searched place ID has been stable for 40 minutes, it can be regarded that the advertisement receiver took a meal at the restaurant. The advertisement providing method according to the exemplary embodiment of the present invention may further include setting the visit threshold time to be compared with the measured duration. For example, in the above-mentioned example, the visit threshold time relating to the restaurant may be set to 30 minutes. In this case, when the duration is more than 30 minutes, the restaurant is regarded as the visit place to generate the visit place history. When the duration is less than 30 minutes, the current location is searched continuously without generating the visit place history and the duration of the current location is measured.
Referring to
As described below, in predicting a next visit place, when a plurality of next visit places are predicted, a plurality of advertisements that belong to a category of the plurality of next visit places may be selected. For example, when a cafe and a clothing store are predicted as the next visit place, one or more advertisements that belong to a category of the cafe and one or more advertisements that belong to a category of the clothing store may be selected as the advertisement to be provided. In providing the selected advertisement to the mobile apparatus (S70) in
In the exemplary embodiment, the advertisement providing method may further include collecting advertisement providing statistics data relating to the provided advertisement. The advertisement providing statistics data may include 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. The model of the Bayesian network may be updated by collecting the advertisement providing statistics data and a feedback of the advertisement receiver relating to the advertisement providing may be accepted.
Referring to
In order to selectively provide the advertisement by predicting the next visit place of the advertisement receiver, in the advertisement providing method according to the exemplary embodiment of the present invention, the next visit place may be probabilistically predicted by using the Bayesian network (BN). The Bayesian network may mean a graphical model representing random variables and their conditional dependencies through a directed acyclic graph (DAG). In order to analyze a customer's actions which are inherent in some degree of uncertainty, the probabilistic model such as the Bayesian network may be appropriately used. As described above, as the probabilistic model for predicting the next place, various probabilistic reasoning techniques such as the decision tree or conditional random fields may be used in addition to the Bayesian network. That is, the advertisement providing method according to the present invention, predicting the next visit place is not limited to the Bayesian network technique.
Hereinafter, as the exemplary embodiment of the present invention, the method for predicting the next visit place through the Bayesian network will be described. The Bayesian network may be modeled to the directed acyclic graph including a node expressing the variables and an arc expressing a dependency relationship between the variables. One node may be a random variable, but one node may be various kinds of variables such as a measurement value, a constant, a factor, and a hypothesis.
The Bayesian network may be a model expressing a joint probability distribution for all variables expressed by the node on the graph. The Bayesian network model may be represented by Equation 1.
In Equation 1, ‘v’ written as a lower case letter may represent each node included in the Bayesian network model and ‘V’ written as an upper case letter may represent a set of the nodes v. ‘parents(v)’ may be a parent node of the node v. For example, when a directed arc from node A to node B exists, node A may be the parent node of node B.
Prediction of the next visit place based on the past visit place history may be performed through the Bayesian network. The method for predicting the next visit place using the Bayesian network will be described below with reference to
In the model for predicting the next place shown in
In the Bayesian network model for predicting the next place shown in
Referring to
In the model of
P(P0,P1,P2)=P(P0|P1P2)·P(P1|P2)·P(P2) [Equation 2]
In Equation 2, P0, P1, and P2 represent the visit places corresponding to the nodes in the graph.
The probabilistic relationship for each of the combined variables may be empirically acquired. As a result, before predicting the next visit place of the advertisement receiver in order to actually provide the advertisement, learning or training the Bayesian network model is performed in advance. The learning or training of the Bayesian network model may mean configuring topology of a graph based on given learning data and configuring a conditional probability table (CPT) for a dependency relationship that exists between the nodes. The learning data may mean a plurality of data that are empirically acquired. That is, the learning of the Bayesian network model may be performed based on visit place histories collected from a plurality of advertisement receivers.
In the exemplary embodiment, when a prediction model of the Bayesian network is learned, both configuring the topology of the graph and configuring the conditional probability table may be performed based on given learning data. In another exemplary embodiment, while the topology of the graph is prepared, only the conditional probability table may be configured based on the given learning data. In the learning step, when only the conditional probability table is configured, the learning may be more easily performed.
When sequential visit pattern information of a predetermined user is given, a probability distribution of a place to be visited next may be calculated based on the learned prediction model. For example, in
When the next visit place is predicted through the prediction model of the Bayesian network in the same manner as the above-mentioned method, prediction accuracy can be improved, and as a result, advertisements having a high advertisement effect can be selectively provided to the user. In the exemplary embodiment, the prediction model, i.e., the probability distribution model of the Bayesian network may be learned based on the previous visit place histories of the corresponding advertisement receiver. In the exemplary embodiment, the probability distribution model of the Bayesian network may be learned based on visit place histories of other advertisement receivers.
In the example of
Referring to
In the Bayesian network model shown in
P(P0,P1,P2,T0,T1,T2)=P(P0|P1,P2,T0)·P(P1|P2,T1)·P(P2|T2) [Equation 3]
A visit duration corresponding to the duration when the user stays at each of the visit places is included as a variable in addition to the visit time for each visit place to perform more accurate prediction.
Referring to
In the Bayesian network model shown in
P(P0,P1,P2,T0,T1,T2,D1,D2)=P(P0|P1,P2,T0,D1)·P(P1|P2,T1,D2)·P(P2|T2) [Equation 4]
Age and gender of the user in addition to the past visit place, visit time, and visit duration may also influence selection of the next visit place. Since the age A and gender G of the user are not the variables which are changed with movement of the place or the passage of the time, graph topology shown in
The graph shown in
In the method similar to methods described in
P(P0,P1,P2,T0,T1,T2,Dc1,D2,G,A)=P(P0|P1,P2,T0,D1,G,A)·P(P1|P2,T1,D2)·P(P2|T2) [Equation 5]
As described above with reference to
Meanwhile, the advertisement providing method according to the exemplary embodiment of the present invention is implemented as a program command format which can be executed in various computing systems to be recorded in computer-readable recording media. These computer-readable recording media may be implemented as magnetic recording media such as a hard disk, a floppy disk, and a magnetic tape, optical recording media such as a CD-ROM and a DVD, a magneto-optical recording medium such as a floptical disk, or hardware devices manufactured to store and execute program commands. The above method may include collecting a visit place history of a mobile apparatus, predicting a next visit place through a Bayesian network based on the collected visit place history, and selecting an advertisement to be provided based on the predicted next visit place.
For an experiment for investigating prediction accuracy, sequential visit place histories of approximately 130 COEX Mall users are actually collected. Histories of visitors for three places or less are removed from the histories of the 130 persons. Histories of the duplicate visitors showing the same circulation as companies are removed. Visit histories of 76 users are acquired through filtering. The total number of visits of the users is 351. 80% of the visit place histories are used for learning of the Bayesian network model and the rest of the visit place histories, 20% are used for test data. That is, by using the Bayesian network modeled from the 80% data, predicting next visit places of the rest of the user, 20% is compared with places which the 20% users visit actually. In order to prepare a result graph, three next visit places are predicted by giving a priority on the basis of the probability according to the exemplary embodiment of the present invention.
In
Therefore, prediction results that belong to TOP1, TOP2, and TOP3 are not exclusive to each other but correspond to an inclusive relationship. That is, a result that belongs to TOP1 is included in even TOP2 and TOP3. Further, a result that belongs to TOP2 is included in even TOP3. For example, in the case where the next visit place is predicted according to priorities of the cafe, the clothing store, the cinema, and other places, a case where the user actually visits the cinema corresponds to only TOP3. Meanwhile, a case where the user actually visits the cafe corresponds to all of TOP1, TOP2, and TOP3.
Referring to
Referring to
In the exemplary embodiment, the advertising client 310 may provide advertisement providing statistics data AS relating to the use of the provided advertisement PA to the advertising server 330. The advertising server 330 may collect and store the advertisement providing statistics data AS. The advertisement providing statistics data AS may include 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.
Referring to
In the advertisement providing system using a mobile apparatus, the advertising client 310 may include the mobile apparatus which an advertisement receiver possesses. As a result, the advertising client 310 may be a device including a portable apparatus such as a smartphone, a portable media player, an MP3 player, or a tablet computer, or the like. Therefore, although not shown in
In the exemplary embodiment, the location detector 313 may include a GPS receiver or a Wi-Fi receiver detecting the current location of the advertising client 310. In the exemplary embodiment, the location detector 313 is connected with the GPS receiver or Wi-Fi receiver to receive and process locational information. The location detector 313 may periodically detect the current location and provide the detected current location to the visit history generator 315.
The visit history generator 315 generates the visit history CVH of the advertising client 310 based on the current location received from the location detector 313 to provide the generated CVH to the advertising server. In the exemplary embodiment, the method described above with reference to
Referring to
Continuously referring to
The advertisement database 335 may include a plurality of advertisement categories. The plurality of advertisement categories may include category 1 335a, category 2 335b to category N 335c. The advertisement categories may correspond to a plurality of visit places, respectively. For example, category 1 335a and category 2 335b may be the advertisement categories corresponding to a cinema and a restaurant, respectively. Each of advertisement categories 335a to 335c may include one or more advertisements. For example, category 1 335a may include advertisements for one or more cinemas.
The advertisement selector 333 may search the advertisement database 335 based on one or more next visit place NVP provided from the visit place predictor 333. The advertisement database 335 may provide advertisements AD of a category relating to the next visit place NVP to the advertisement selector 333. The advertisement selector processes the advertisements searched in the advertisement database 335 to provide the processed advertisements to the advertising client 310.
As described above, the advertising client 310 may provide the advertisement providing statistics data AS relating to the use of the provided advertisement PA to the advertising server 330. The advertising server 330 may collect and store the advertisement providing statistics data AS. Specifically, the advertising client 310 provides the advertisement providing statistics data AS to the advertisement statistics collector 336 of the advertising server 330 and the advertisement statistics collector 336 processes the advertisement providing statistics data AS to store the processed advertisement providing statistics data in the advertisement usage statistics database 337 as statistics data SD. The advertisement providing statistics data AS may include 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.
The statistics data SD stored in the advertisement usage statistics database 337 may be usefully used in order to provide the advertisement more preferably. For example, the statistics data SD may be used to improve the Bayesian network model for predicting the next visit place and may be used as data for feedback to an advertiser.
Referring to
The advertisements that belong to each category may be displayed randomly or displayed according to a predetermined rule. For example, as shown in
The present invention can be usefully used to provide an advertisement using a mobile apparatus. In particular, the present invention can be applied to an advertisement providing business through a portable communication apparatus such as a smart phone and further, can be widely used to provide the advertisements using portable electronic apparatuses such as a cellular phone, a portable media player (PMP), an MP3 player, a notebook, a tablet computer.
According to exemplary embodiments of the present invention, a method for providing an advertisement predicts a next visit place based on a visit place history of an advertisement receiver and selectively provides an advertisement relating to the predicted next visit place to improve an advertisement effect. Further, the method can reduce a possibility of providing a spam advertisement.
A number of exemplary embodiments have been described above. Nevertheless, it will be understood that various modifications may be made. For example, suitable results may be achieved if the described techniques are performed in a different order and/or if components in a described system, architecture, device, or circuit are combined in a different manner and/or replaced or supplemented by other components or their equivalents. Accordingly, other implementations are within the scope of the following claims.
| Number | Date | Country | Kind |
|---|---|---|---|
| 10-2011-0053693 | Jun 2011 | KR | national |
