KR101373825B1 - Service system and service method of collective preference information based on current context information - Google Patents

Abstract

In the present invention, the group preference information service system based on the current situation information may be obtained by accumulating and storing the current location information of the user, and accumulating and storing the current situation information by accumulating and storing the current location information from the current location information and the current time information. A mobile terminal inferring and accumulating user intention information from context information; And a server that receives cumulatively stored user intention information from the mobile terminal and extracts preference information of a group to which the user belongs.

Description

SERVICE SYSTEM AND SERVICE METHOD OF COLLECTIVE PREFERENCE INFORMATION BASED ON CURRENT CONTEXT INFORMATION}

The present invention relates to a group preference information service system and service method based on current situation information using a mobile terminal.

As the spread of the Internet has spread, several attempts have been made to increase the search efficiency. Research on the type and impact of search terms has evolved to help people create better overall results through their intuitive judgments, and to improve the quality and quantity of URLs provided in search results. Various models with different performances have been proposed in terms of recall (precision / recall).

Conventionally, a method of improving the search efficiency by ranking the importance of each web document through a page rank technique has appeared. In addition, the user's or group's preference information is used by using people's clickthrough data itself. The study to get started. The research based on search information of users, without relying on web documents and web structure, is very meaningful in that it can implicitly identify group preference information. Each user does not prefer to provide passive feedback because, based on search information, it is possible to obtain a group's preference information without the users noticing.

However, the method of using clickthrough data on the web is also based on the information that each user accesses the web through a computer or a terminal, inputs a question, and selects a preferred URL. Therefore, the method of obtaining information is not implicit. There is a limitation that requires manual participation of the user. An example of this limitation is that a significant number of people obtain information through newspapers, telephones, or conversations with friends, not the web, and those who are reluctant to use the computer, such as the elderly, can Excluded from the subject, which is undesirable.

To answer questions such as “Where was the most popular restaurant in Korea last July?” Using existing database systems, database systems exist for each group or region, or tables, attributes, and data types. Since there is a case where the configuration of the back and the like is different, there is a limit that a lot of time and cost are required. In particular, if you change the condition of “July last year” to “June-August last year,” it is inevitable that you will be charged the same cost to get an answer.

Accordingly, there is a demand for a system of preferred context information service system and service method based on the current situation information which is more reliable, implicitly and automatically performed by avoiding the manual method on the web.

The present invention provides a group preference information service system and a service method based on the current context information which is more reliable, implicitly and automatically performed by providing a preference information of a group to which the user belongs. The purpose.

One aspect of the present invention is to obtain and store the current location information of the user, and to accumulate and store the current situation information from the stored and stored current position information and the current time information, and to store the customized inference from the stored and stored current situation information A mobile terminal for inferring and storing user intention information by applying; And a server for receiving cumulatively stored user intention information from the mobile terminal and extracting preference information of a group to which the user belongs.

In one embodiment of the present invention, the mobile terminal periodically obtains the current location information, or whenever the change of the current location information exceeds a set reference value, the current preference information based group preferences to obtain the current location information Provides an information service system.

In another embodiment of the present invention, the mobile terminal provides a group preference information service system based on current situation information for obtaining the current location information from Wi-Fi or GPS.

In another embodiment of the present invention, when obtaining the current location information from the Wi-Fi, the mobile terminal of the access point to the media access control address of the Wi-Fi, and the signal received from the Wi-Fi The present invention provides a group preference information service system based on current situation information using strength as raw data.

In another embodiment of the present invention, the mobile terminal provides a group preference information service system based on the current situation information to obtain current situation information by adding the accumulated stored current location information and the current time information.

In another embodiment of the present invention, the mobile terminal is based on the current situation information, the maximum number of the cumulative stored current position information, the maximum number of the stored stored current situation information, and the maximum number of the stored user intention information is set Provides a group preference information service system.

In another embodiment of the present invention, the server provides a group preference information service system based on current context information to extract preference information of a group to which a user belongs through a data mining technique.

Another aspect of the invention, the mobile terminal, the first step of acquiring and storing the current location information of the user; A second step of acquiring and storing current situation information from the stored current location information and current time information in the mobile terminal; In the mobile terminal, inferring and accumulating user intention information by applying customized inference from the stored stored current situation information; And a fourth step of extracting, from the mobile terminal, cumulatively stored user intention information from the mobile terminal to extract preference information of a group to which the user belongs.

In an embodiment of the present disclosure, the first step may include: determining whether there is an input of a user's current location information from Wi-Fi; Acquiring and accumulating the current location information through the Wi-Fi when the current location information is input from the Wi-Fi; Determining whether there is an input of the current location information from a GPS when there is no input of the current location information from the Wi-Fi; And when the current location information is input from the GPS, acquiring and accumulating the current location information through the GPS, and accumulating and storing the current location information.

In another embodiment of the present invention, the second step may include: determining whether the accumulated current location information exists; Extracting the accumulated current location information when the accumulated current location information exists; And acquiring and storing current situation information from the accumulated current location information and current time information and storing the current situation information.

In another embodiment of the present invention, the third step may include: determining whether the accumulated current situation information exists; Extracting the accumulated current situation information when the accumulated current situation information exists; Analyzing a pattern of the accumulated current situation information and learning a pattern type of a user from the analyzed pattern; And inferring and accumulating the user intention information from the pattern type. The present invention provides a group preference information service method based on the current situation information.

Another aspect of the invention provides a computer readable recording medium comprising instructions for performing the method of any one of claims 8-11.

According to the present invention, by implementing a group preference information service using a mobile terminal, it is possible to provide various and diversified information services to users, companies, organizations and governments.

In addition, according to the present invention, the government may enhance the surrounding facilities or other administrative services for the convenience of the public, the enterprise may further enhance the sale and promotion of related products, consumers receive a high quality group preference information service It is available.

1 is a block diagram of a group preference information service system based on current context information according to an embodiment of the present invention.
2 is a flowchart of a method of providing group preference information based on current context information according to an embodiment of the present invention.
3 is a flow chart embodying step S100 of FIG.
FIG. 4 is a flowchart illustrating an operation S200 of FIG. 2.
FIG. 5 is a flowchart illustrating an operation S300 of FIG. 2.
FIG. 6 is a flowchart illustrating an operation S400 of FIG. 2.
7 is a schematic diagram of a server model for inferring group preference information by collecting user intention information according to the present invention.
8 is a schematic diagram of a model for inferring group preference information using a modified PLSA technique among data mining techniques in a server according to the present invention.

DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings. However, the embodiments of the present invention may be modified into various other forms, and the scope of the present invention is not limited to the embodiments described below. The shape and the size of the elements in the drawings may be exaggerated for clarity and the same elements are denoted by the same reference numerals in the drawings.

1 is a block diagram of a group preference information service system based on current context information according to an embodiment of the present invention. Referring to FIG. 1, the group preference information service system based on the current situation information includes a mobile terminal 100 and a server 200. The mobile terminal 100 may be any terminal capable of receiving information from Wi-Fi or GPS. For example, a smartphone terminal may be used.

The mobile terminal 100 obtains and stores the current location information of the user. The mobile terminal 100 acquires the current location information periodically, or obtains the current location information whenever a change in the current location information exceeds a setting reference value. The mobile terminal 100 may obtain current location information from a measurement device such as Wi-Fi or GPS, and the current time information is obtained using information used inside the mobile terminal 100. When the current location information is obtained from the Wi-Fi, the mobile terminal 100 receives the Media Access Control Address of the Access Point (AC) for Wi-Fi, and the Wi-Fi. The current position information is obtained using the signal strength as raw data.

In addition, the mobile terminal 100 acquires the current situation information from the accumulated current location information and the current time information and stores the current situation information. At this time, the mobile terminal 100 obtains the current situation information by adding the accumulated current location information and the current time information.

In addition, the mobile terminal 100 infers and stores the user intention information from the cumulatively stored current situation information.

In addition, the mobile terminal 100 is set to the maximum number of cumulatively stored current location information, the maximum number of cumulatively stored current situation information, and the maximum number of cumulatively stored user intention information.

The server 200 receives cumulatively stored user intention information from the mobile terminal 100 and extracts preference information of a group to which a user belongs through a data mining technique. Data mining is a method of finding statistical rules or patterns systematically and automatically in large-scale stored data.

In the group preference information service system based on the current situation information of the present invention, since the mobile terminal 100 obtains only the current location information and the current visual information of the user, there is no fear of personal information and infringement, and inference from the server 200. The preferred group's preference information can be provided as a service or automated service by direct request from each user, corporation, government or organization.

2 is a flowchart of a method of providing group preference information based on current context information according to an embodiment of the present invention.

First, the mobile terminal acquires and stores the current location information of the user (S100). The step S100 will be described in detail with reference to FIG. 3.

After the step S100, the mobile terminal acquires the current situation information from the stored current location information and the current time information and stores the current situation information (S200). The step S200 will be described in detail with reference to FIG. 4.

After the step S200, the mobile terminal infers and stores the user intention information from the accumulated current situation information (S300). The step S300 will be described in detail with reference to FIG. 5.

After the step S300, the server receives the user intention information accumulated from the mobile terminal to extract the preference information of the group to which the user belongs (S400). The S400 step will be described in detail with reference to FIG. 6.

3 is a flow chart embodying step S100 of FIG.

First, the mobile terminal 100 determines whether there is an input of the user's current location information from the Wi-Fi (S110).

After the step S110, when there is an input of the current location information from the Wi-Fi, the mobile terminal 100 obtains and stores the current location information via the Wi-Fi (S120).

However, after the step S110, if there is no input of the current location information from the Wi-Fi, the mobile terminal 100 determines whether there is an input of the current location information from the GPS (S130).

After the step S130, if there is an input of the current location information from the GPS, the mobile terminal 100 obtains the current location information through the GPS and accumulates the stored (S140). However, if there is no input of the current location information from the GPS, the feedback (feedback) to step S110.

In connection with S110 to S130, the mobile terminal 100 can be set in the environment, the environment setting means setting the attribute value for the input standby of the Wi-Fi data or GPS data. For example, the GPS information may be periodically received, and the GPS information may be adjusted whenever the change in position exceeds a predetermined value (for example, 10 m). These property values should be set to the default values, but allow the user to change them arbitrarily.

In the Wi-Fi zone or indoors, the location information is determined and stored using the Wi-Fi information, and in the case of the outdoor where the GPS signal is received, the location information is determined and stored through the GPS information. The reason for using the Wi-Fi information first is that, unlike GPS, the indoor location can be recognized relatively accurately, and when the infrastructure for the Wi-Fi service grows in the future, the recognition rate in the outdoors is also expected to increase.

The location information is represented as raw data. For example, when location information is acquired through GPS in an area where Wi-Fi is not possible, raw data such as latitude 36.39243721961975 and longitude 127.39979445934296 can be obtained. The location information is accumulated for obtaining the situation information. In the case of using Wi-Fi, location information can be obtained by using the media access control address and signal strength of each access point as raw data.

FIG. 4 is a flowchart illustrating an operation S200 of FIG. 2.

First, the mobile terminal 100 determines whether the accumulated current location information exists (S210).

After the step S210, if there is accumulated current location information, the mobile terminal 100 extracts the accumulated current location information (S220). However, if the accumulated current position information does not exist, the flow returns to step S210.

After the step S220, the mobile terminal 100 obtains the current situation information from the accumulated current location information and the current time information and stores the cumulative current information (S230). In relation to step S230, the current location is recognized through the current location information, and the current situation information is configured by combining the information on the current place and the current time information. In the mobile terminal 100, environment setting is possible. The environment setting means setting any attribute value, for example, the maximum number of accumulated location information, which is required for setting the current situation information.

When place information is obtained from raw data, it is selected from a predefined list. If you define 'Hollis', 'Starbucks' and 'Coffee Shop' for the coffee shop when defining the list of place information, the coffee shops other than 'Hollis' and 'Starbucks' are called 'Coffee Shop'. It is recognized as a place.

As the list of place information is segmented, the reliability of the criteria for identifying the user's intention is increased, and the preference information of the group to which the user belongs can be more diversified.

Visual information is needed for the calculation of the duration. For example, when entering the coffee shop at 08:50 am, only the information of entering the coffee shop is not used to construct the situation information. If you enter 08:50 am and go out at 09:30, you will be able to organize your situation with information that the coffee shop visit lasted for 40 minutes from 08:50 am. Thus, the configuration of the situation information is possible when the duration of the visit is completed.

The situation information is composed of location information, place information, and visual information. For example, 'GPS, (36.39243721961975, .39979445934296), coffee shop, 2010.09.27.08:50, 2010.09.27.09:30, 0.00: 40'. An example of this is situational information that a coffee shop located at latitude and longitude above was visited for 40 minutes from 08:50 am to 09:30 am. Since the situation information has no special meaning in itself, it is accumulated for intention information inference.

FIG. 5 is a flowchart illustrating an operation S300 of FIG. 2.

First, the mobile terminal 100 determines whether the accumulated current situation information exists (S310).

After the step S310, if the accumulated current situation information exists, the mobile terminal 100 extracts the accumulated current situation information (S320). However, if the accumulated current situation information does not exist, the flow returns to step S310.

After the step S320, the mobile terminal 100 analyzes the accumulated pattern of the current situation information, and learns the pattern type of the user from the analyzed pattern (S330). In the mobile terminal 100, environment setting is possible. The environment setting means setting any attribute value, for example, the maximum number of cumulative situation information, which is required for setting intention information. To infer customized intention information, we use general information of users and patterns of life. The general information of the user may be information that can be checked in the mobile terminal 100, for example, gender and age, without the user directly inputting. The pattern of life can be obtained by accumulating situation information and analyzing the pattern.

Status information is accumulated in units of days, weeks, and months for pattern analysis. For example, you can find out if a user goes to a restaurant almost every morning through the accumulated status information on a daily basis, and find out if the user goes to the tennis court after dinner every Monday. In addition, it is possible to find out that the user visits the 'dental' once a month through the situation information accumulated on a monthly basis. Of course, by accumulating every year, it is possible to analyze annually. How many units to accumulate depends on the hardware state of the mobile terminal 100.

In addition, the analyzed life patterns include information such as how many minutes a user usually visits a bookstore or how long a meal takes. Because it is too much to deduce the intention information about eating from the information you visited in the restaurant for only 10 seconds, and it is rather credible that you came to find something you left behind. Also, you cannot deduce intention information if you come to buy a book the same day as someone who visits your bookstore and someone who visits you occasionally. People who stop by every day are more likely to be employees, and the information on how much time they spend at the bookstore can more accurately infer the likelihood of being an employee.

After operation S330, the mobile terminal infers and accumulates user intention information from the pattern type at operation S340. The present invention uses the current situation information for intention information inference, and applies customized inference. The reason for the need for customized reasoning is that each person may have a different meaning for a particular place or time. For example, if you stop by the bookstore in the morning, you can infer the general intention that you might have gone to buy a book. However, if the person is a person who works at a bookstore, it's more reliable to know that going to work instead of buying a book. Also, even if you go to buy a book, the time it takes to buy a book varies widely from person to person. For example, if you visit a bookstore for 5 seconds and for a 10-hour visit, you will not be able to infer that you went to buy a book.

Through the user type learning, the criteria for determining intention information for a specific place and time zone vary according to each person, and the intention information is selected from a predetermined list of intention information. The more detailed the list of intention information is, the more diverse the group preference information service becomes. The intention information list will be described in detail with reference to FIG. 7.

The intention information obtained by applying the customized reasoning consists of the current situation information and the intention information. For example, intention information such as 'GPS, (36.39243721961975, .39979445934296), coffee shop, 2010.09.27.08:50, 2010.09.27.09:30, 0.00: 40, after-drink drink' may be used by the user on September 27, 2010. I stopped at the coffee shop for 40 minutes from 08:50 am, and the intention expected by customized reasoning means 'after-drink drink'. This intent information is accumulated for transmission to the server.

FIG. 6 is a flowchart illustrating an operation S400 of FIG. 2.

First, the mobile terminal 100 determines whether the accumulated user intention information exists (S410). In the mobile terminal 100, environment setting is possible. The environment setting is to set any attribute value, for example, the maximum number of cumulative intention information required for setting the intention information.

After the operation S410, the mobile terminal 1000 extracts the accumulated user intention information (S420). However, if the accumulated user intention information does not exist, the feedback is returned to the operation S410.

After operation S420, the mobile terminal 100 determines whether it is possible to transmit the accumulated user intention information to the server 200 (S430).

After the step S430, the mobile terminal 100 transmits the user intention information to the server 200 (S440). However, if it is not possible for the mobile terminal 100 to transmit the user intention information to the server 200, the mobile terminal 100 feeds back to the step S410. When the user intention information is transmitted to the server 200, the intention information transmitted first is submitted to the server 200. If communication with the server 200 is not possible, the intention information is accumulated and waits for new cumulative information. When again check the communication with the server 200. Therefore, when communication with the server 200 is possible, accumulated intention information is sequentially transmitted to the server 200. This is to prevent intentional information from being lost in consideration of the fact that communication with the server 200 may be impossible for various reasons. The upper limit of the number of cumulative intention information depends on the hardware environment of the mobile terminal 100.

After step S440, the server 200 extracts the preference information of the group to which the user belongs (S450).

7 is a schematic diagram of a server model for inferring group preference information by collecting user intention information according to the present invention. By accumulating the intention information received from the plurality of mobile terminals 100, the group preference information is inferred through the data mining technique of PLSA, LDA, or a modified model thereof. The group preference information is managed at the region and date level, and the preference information corresponding to the specific date of the specific region is expressed by the hierarchical level intention, thereby providing various services. Breakfast, lunch, and dinner represented by a tree structure can be examples of 'intention'. The term 'intention' is expressed as 'high intention' for breakfast, lunch, and dinner. As such, 'intention' is stratified to enable a multifaceted approach. Multilateral service provision will be described in detail with reference to FIG. 8.

8 is a schematic diagram of a model for inferring group preference information using a modified PLSA technique among data mining techniques in a server according to the present invention. The intention information transmitted from each user's mobile terminal 100 is accumulated as a numerical value, which is used as part of the probability calculation in the modified PLSA data model.

Accumulated numerical data is stored in units of dates and regions. For example, if the date is stored in monthly units and the region is stored in hours, it is accumulated in units of 'Suwon, Gyeonggi-do, June 2010' as indicated. It is stored for each date and place. Therefore, the table corresponding to 'Suwon, Gyeonggi-do in June 2010' will have numerical data on intention data collected from Suwon, Gyeonggi-do in June 2010.

The three-dimensional coordinates of regions, dates, and levels mean that these numerical data are stored in units of regions and dates. Climbing along the level arrows means you can combine regions with date units. For example, all the regions in Gyeonggi-do, such as Ansan and Gyeonggi-do, are united together to form a higher-level regional unit called Gyeonggi-do. In addition, units from January to December 2010 can be combined to form a higher-level date unit called “2010.” Thus, various levels of access to numerical data are possible by appropriately adjusting the level arrows at the date and local levels. In other words, it can deal with the numerical data of 'May-August 2010 in Ansan and Suwon, Gyeonggi-do'.

Enabling multiple accesses through level arrows at the date and local level does not mean calculating on the basis of numerical data for each attempt. For example, when calculating the numerical data of “May-August 2010 in Ansan and Suwon, Gyeonggi-do,” simply do not combine the tables of the numerical data. Since numerical data is used to obtain group preference information, it can be obtained by inferring preference information of the group corresponding to the upper level at the date and region level based on the group preference information obtained based on the lowest level numerical data. .

When performing iterative probability calculation by applying numerical data, it is based on a modified PLSA model using P (c), P (i | c), and P (l | c). Where c is 'intention', i (interval) is time, and l (location) is place.

Numerical data can be applied to the modified PLSA model and used as part of the probability calculation. The result is expressed as a probability for each 'intention', which expresses the group's preference as a probability. Breakfast, lunch, and dinner represented by a tree structure can be examples of 'intention'. The term 'intention' is expressed as 'high intention' for breakfast, lunch, and dinner. As such, 'intention' is stratified to enable a multifaceted approach. For example, you might ask, 'How long did people eat breakfast on time in May-August 2010 in Ansan and Suwon, Gyeonggi-do?' And 'From May-August 2010 in Ansan and Suwon, Gyeonggi-do. How much time did people eat on time? '

Among the stratified 'intentions', the 'lowest intention' can be obtained from numerical data, but for the speed of the system, 'high intention' is not directly obtained from numerical data. Once all the "lower intentions" are found, "higher intentions" can be appropriately inferred through "lower intents," and this hierarchy can be extended to multiple levels.

However, only the most subordinate intents can be defined, and proper initiation is required in order for each intent to have the desired meaning. The initialization operation uses numerical data and 'intention' provided by each mobile terminal 100.

When a user submits specific intention information, for example, 'GPS, (36.39243721961975, .39979445934296), Coffee Shop, 2010.09.27.08:50, 2010.09.27.09:30, 0.00 In the intention information such as '40, drink after meal ', since the inferred intention of the user is' after meal drink,' the number of 'intentions' as' after meal drink' is counted. The counted information is used for initialization of P (c), and the accumulation of numerical data is counted at the coffee shop at the place, at 08:00 to 08:59 and 09:00 to 09:59 at the time. Such numerical data is used to initialize P (i | c) and P (l | c), and it is also used for iterative probability calculation of the model.

By adjusting the level of numerical data managed at the date and region level, multi-level data access is possible, and the hierarchical 'intention' model enables multi-faceted access to group preference information.

It is also applicable to the present invention through other applied data mining techniques including LDA rather than modified PLSA.

9 is a schematic diagram of a model in which group preference information is provided as a service according to the present invention. The server 200 for acquiring group preference information may provide two services to users and governments, corporations, and organizations. The first is a service by direct demand, and the second is an automated service.

On demand service, each user, group, government, or company can answer questions such as 'How long have people eaten breakfast in May and August 2010 in Ansan and Suwon, Gyeonggi-do?' This is the case when the request is made directly to 200). The server 200 may provide an answer with fewer operations.

Automated services are primarily provided for each user. Since each mobile terminal 100 has a customized intention inference model optimized for a corresponding user, the mobile terminal 100 may automatically request information from the server 200 using this model. For example, if a user leaves the hospital, he can automatically recommend the most popular pharmacy last month at or near a nearby pharmacy.

The present invention is not limited by the above-described embodiment and the accompanying drawings. It is intended that the scope of the invention be defined by the appended claims, and that various forms of substitution, modification, and alteration are possible without departing from the spirit of the invention as set forth in the claims. Will be self-explanatory.

100: mobile terminal
200: Server

Claims (12)

Acquire and store the user's current location information, accumulate and save the current situation information from the accumulated and stored current location information and current time information, and infer user intention information by applying customized inference from the accumulated and stored current situation information. A mobile terminal for accumulating and storing the mobile terminal; And
A server for receiving accumulated user intention information from the mobile terminal and extracting preference information of a group to which the user belongs;
Group preference information service system based on the current situation information. The method of claim 1,
The mobile terminal periodically acquires the current location information, or obtains the current location information whenever a change in the current location information exceeds a set reference value. The method of claim 1,
The mobile terminal is a group preference information service system based on the current situation information to obtain the current location information from the Wi-Fi or GPS. The method of claim 3,
When the current location information is obtained from Wi-Fi, the mobile terminal uses the media access control address of the access point for the Wi-Fi and the strength of a signal received from the Wi-Fi as raw data. Collective preference information service system based on the current situation information to obtain location information. The method of claim 1,
The mobile terminal is a group preference information service system based on the current situation information to obtain the current situation information by adding the accumulated stored current position information and the current time information. The method of claim 1,
The mobile terminal is a group preference information service system based on the current situation information, wherein the maximum number of the accumulated stored current location information, the maximum number of the stored stored current situation information, and the maximum number of the stored stored user intention information are set. The method of claim 1,
The server is a group preference information service system based on the current context information to extract the preference information of the group to which the user belongs through a data mining technique. A first step of acquiring and storing current location information of a user in a mobile terminal;
A second step of acquiring and storing current situation information from the stored current location information and current time information in the mobile terminal;
In the mobile terminal, inferring and accumulating user intention information by applying customized inference from the stored stored current situation information; And
A fourth step of receiving, at a server, preference information of a group to which the user belongs by receiving accumulated user intention information from the mobile terminal;
Group preference information service method based on the current context information, including. 9. The method of claim 8,
In the first step,
Determining whether there is an input of a user's current location information from Wi-Fi;
Acquiring and accumulating the current location information through the Wi-Fi when the current location information is input from the Wi-Fi;
Determining whether there is an input of the current location information from a GPS when there is no input of the current location information from the Wi-Fi; And
Acquiring and storing the current location information through the GPS when there is input of the current location information from the GPS;
Group preference information service method based on the current context information, including. 9. The method of claim 8,
The second step comprises:
Determining whether the accumulated current location information exists;
Extracting the accumulated current location information when the accumulated current location information exists; And
Acquiring and storing current situation information from the accumulated current location information and current time information;
Group preference information service method based on the current context information, including. 9. The method of claim 8,
In the third step,
Determining whether the accumulated current situation information exists;
Extracting the accumulated current situation information when the accumulated current situation information exists;
Analyzing a pattern of the accumulated current situation information and learning a pattern type of a user from the analyzed pattern; And
Inferring and accumulating user intention information from the pattern type;
Group preference information service method based on the current context information, including. A computer-readable recording medium comprising instructions for performing the method of any one of claims 8-11.

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