KR20220109982A - Method for recommending destination and computing device for executing the method - Google Patents

Abstract

Disclosed are a destination recommendation method and a computing apparatus for performing the same. In accordance with one embodiment of the present invention, the destination recommendation method, which is performed by the computing apparatus including at least one processor, and a memory storing at least one program executed by the at least one processor, includes the following steps of: receiving user interest information of a user seeking to be recommended a destination in a travel area; receiving tourism information from an external server; estimating the place density of the travel area from the tourism information; generating recommendation information for recommending a destination in the travel area based on the user interest information and the estimated place density; and providing the recommendation information to the user. Therefore, the present invention is capable of preventing a specific place from becoming crowded.

Description

A method for recommending a destination and a computing device for performing the same

Embodiments of the present invention relate to destination recommendation techniques.

A navigation system is a system that tracks the location of a transportation device such as a moving vehicle, accurately displays it on a road map, and guides a route to a destination, and is also referred to as an automatic navigation system. Such a navigation system is an autonomous navigation system that employs a plurality of sensors and tracks a location using information provided from them, a rudder navigation system using location information provided from GPS satellites, and a hybrid combining these two devices. navigation systems, etc.

In general, a navigation system receives location information from a GPS satellite using a GPS antenna, maps the received location information data to road map data, displays the current location on a screen, and a driving route to a destination input by a user. It is searched for and displayed on the road map so that the user can easily check it. Such a navigation system has a very diversified function, such as a route guidance function of the shortest distance, a route guidance function centered on highways, a function to guide a route with the smoothest communication in connection with traffic information, and a guide function to a short-distance gas station.

However, there is a problem in that the conventional navigation system only provides a function of guiding a movement route from a departure point to a destination, and cannot provide a function of recommending a destination according to an option selected by a user.

Republic of Korea Patent Publication No. 10-2012-0069942 (2012.06.28.)

Embodiments of the present invention are for recommending a place in a travel area according to an option input by a user.

According to an exemplary embodiment of the present invention, there is provided a destination recommendation method performed in a computing device having one or more processors and a memory for storing one or more programs executed by the one or more processors, the method comprising:

Receiving user interest information of a user who intends to be recommended a destination of a travel area, receiving tourism information from an external server, estimating a density of places in the travel area from the tourism information, the user interest information and the estimation There is provided a destination recommendation method comprising the steps of: generating recommendation information for recommending a destination in the travel area based on the selected place density; and providing the recommended information to the user.

The estimating of the place density may include extracting a preset density factor from the tourism information, generating a first data set based on the density factor, and using a first machine learning module from the first data set. The method may further include estimating the density of places in the travel area.

The first machine learning module may be trained to receive a first data set generated based on the tourism information, and to estimate a density of places in the travel area from the first data set.

The destination recommendation method includes the steps of extracting a preset cognitive factor from the tourism information, generating a second data set based on the cognitive factor, and a second machine learning module of the travel area from the second data set. The method may further include estimating place awareness.

The second machine learning module may be trained to receive a second data set generated based on the tourism information, and to estimate a place awareness of the travel area from the second data set.

The destination recommendation method may further include receiving the user's GPS information, wherein the generating of the recommendation information includes calculating an arrival time of the travel area based on the user interest information and the GPS information; extracting a place density for each place in the travel area corresponding to the arrival time of the calculated travel area from the estimated place density, and a travel preference style included in the user interest information and each place in the extracted travel area The method may further include comparing the density of places to each other and assigning a preset score to each place in the travel area.

The generating of the recommendation information further includes comparing a travel preference style included in the user interest information with a place recognition for each place in the extracted travel area, and giving each of the places in the travel area a preset score. can do.

The generating of the recommendation information may include generating the recommendation information according to the scores given to each place in the travel area, and may be arranged in the order of the highest score given to each place.

According to another exemplary embodiment of the present invention, there is provided a computing device having one or more processors, and a memory for storing one or more programs executed by the one or more processors, wherein a user who wants to be recommended a destination of a travel area A communication module for receiving user interest information and receiving tourism information from an external server, a place analysis module for estimating a place density of the travel area from the tourism information, and the travel based on the user interest information and the estimated place density There is provided a computing device including a recommendation information providing module that generates recommendation information for recommending a local destination and provides the recommendation information to the user.

The place analysis module extracts a preset density factor from the tourism information, and a first pre-processing module that generates a first data set based on the density factor and a first pre-processing module for estimating the density of places in the travel area from the first data set and a first machine learning module.

The first machine learning module may be trained to receive a first data set generated based on the tourism information, and to estimate a density of places in the travel area from the first data set.

The place analysis module extracts a preset cognitive element from the tourism information, and a second pre-processing module for generating a second data set based on the cognitive element and estimating the place recognition of the travel area from the second data set A second machine learning module may be included.

The second machine learning module may be trained to receive a second data set generated based on the tourism information, and to estimate a place awareness of the travel area from the second data set.

The communication module receives the user's GPS information, the recommendation information providing module calculates the arrival time of the travel area based on the user interest information and the GPS information, and the calculated travel from the estimated place density extracting the place density for each place in the travel area corresponding to the arrival time of the area, and comparing the travel preference style included in the user interest information with the place density for each place in the extracted travel area A preset score may be given to each place.

The recommendation information providing module may compare a travel preference style included in the user interest information with a place recognition for each of the extracted places in the travel area, and assign a preset score to each of the places in the travel area.

The recommendation information providing module may generate the recommendation information according to the scores given to each place in the travel area, and may sort the recommendation information in the order of the highest score given to each place.

According to embodiments of the present invention, by predicting the arrival time of the travel area selected by the user and analyzing the population density for each place according to the arrival time, a place in the travel area can be recommended according to the travel preference style desired by the user.

In addition, according to embodiments of the present invention, by recommending a place in a travel area suitable for a user's travel preference style based on the population density and awareness of each place in the travel area selected by the user, it is possible to prevent the concentration of people in a specific place in advance. can be prevented

1 is a diagram showing the configuration of a destination recommendation system according to an embodiment of the present invention;
2 is a block diagram illustrating the configuration of a management server of a destination recommendation system according to an embodiment of the present invention;
3 is a block diagram illustrating a place analysis module in a management server of a destination recommendation system according to an embodiment of the present invention;
4 is a flowchart illustrating a destination recommendation method according to an embodiment of the present invention;
5 is a block diagram illustrating and describing a computing environment including a computing device suitable for use in example embodiments;

Hereinafter, specific embodiments of the present invention will be described with reference to the drawings. The following detailed description is provided to provide a comprehensive understanding of the methods, apparatus, and/or systems described herein. However, this is merely an example and the present invention is not limited thereto.

In describing the embodiments of the present invention, if it is determined that the detailed description of the known technology related to the present invention may unnecessarily obscure the gist of the present invention, the detailed description thereof will be omitted. And, the terms to be described later are terms defined in consideration of functions in the present invention, which may vary according to intentions or customs of users and operators. Therefore, the definition should be made based on the content throughout this specification. The terminology used in the detailed description is for the purpose of describing embodiments of the present invention only, and should in no way be limiting. Unless explicitly used otherwise, expressions in the singular include the meaning of the plural. In this description, expressions such as “comprising” or “comprising” are intended to indicate certain features, numbers, steps, acts, elements, some or a combination thereof, one or more other than those described. It should not be construed to exclude the presence or possibility of other features, numbers, steps, acts, elements, or any part or combination thereof.

Also, terms such as first and second may be used to describe various components, but the components should not be limited by the terms. The above terms may be used for the purpose of distinguishing one component from another. For example, without departing from the scope of the present invention, a first component may be referred to as a second component, and similarly, the second component may also be referred to as a first component.

In the following description, the terms "transmission", "communication", "transmission", "reception" and other similar meanings of a signal or information are not only directly transmitted from one component to another component, but also a signal or information This includes passing through other components. In particular, to “transmit” or “transmit” a signal or information to a component indicates the final destination of the signal or information and does not imply a direct destination. The same is true for "reception" of signals or information. In addition, in this specification, when two or more data or information are "related", it means that when one data (or information) is acquired, at least a part of other data (or information) can be acquired based thereon.

Meanwhile, an embodiment of the present invention may include a program for performing the methods described in this specification on a computer, and a computer-readable recording medium including the program. The computer-readable recording medium may include program instructions, local data files, local data structures, etc. alone or in combination. The medium may be specially designed and configured for the present invention, or may be commonly used in the field of computer software. Examples of computer-readable recording media include hard disks, magnetic media such as floppy disks and magnetic tapes, optical recording media such as CD-ROMs and DVDs, and program instructions specially configured to store and execute program instructions such as ROMs, RAMs, flash memories, and the like. Hardware devices are included. Examples of the program may include not only machine language codes such as those generated by a compiler, but also high-level language codes that can be executed by a computer using an interpreter or the like.

1 is a diagram showing the configuration of a destination recommendation system 100 according to an embodiment of the present invention.

As shown in FIG. 1 , the destination recommendation system 100 according to an embodiment of the present invention may include a user terminal 200 and a management server 300 .

First, the configurations according to the embodiment of the present invention do not have a physical substance, but operate in a form in which an application program performing each function is installed and executed in one computer or server, or among the configurations in a plurality of servers. It may be that an application program that performs one or more functions is installed and operates organically through an open network.

A server has the same configuration as a general web server in terms of hardware. However, in terms of software, it includes program modules that are implemented through any language such as C, C++, Java, Visual Basic, and Visual C and perform various functions.

In addition, the computer or server in which the above-described components are installed may be implemented in the form of a web server, and the web server is generally connected to an unspecified number of clients and/or other servers through an open computer network such as the Internet, and the client or It refers to a computer system that receives a request for performing work from another web server, derives the work result and provides it, and computer software (web server program) installed therefor.

However, in addition to the above-described web server program, a series of application programs operating on the web server should be understood as a broad concept including various databases built therein depending on the case.

As the user terminal 200 and the management server 300 are connected to each other using a communication network, communication may be possible.

In some embodiments, the communication network includes the Internet, one or more local area networks, wire area networks, cellular networks, mobile networks, other types of networks, or a combination of these networks. can do.

The user terminal 200 may be a terminal of a user who wants to use the destination recommendation service. The user terminal 200 is a terminal for Internet or mobile access, and may include, for example, any device capable of Internet or mobile access, including a PC such as a desktop or a laptop computer, a mobile terminal such as a smart phone, a tablet, etc. have. The user terminal 200 may transmit user interest information to the management server 300 and receive recommendation information and guide information from the management server 300 . User interest information may include, for example, the user's age, gender, travel location, number of travellers, travel theme (eg, prenatal trip, family trip, couple trip, friendship trip, gourmet, vacation, experience, etc.), travel preference style (For example, the density of travel places, awareness, etc.) and the like. The user terminal 200 may transmit Global Position System (GPS) information of the user terminal 200 to the management server 300 in order to confirm the current location of the user.

The management server 300 may receive user interest information from the user terminal 200 and may receive tourism information from an external server. Here, the tourism information may include information on places in the travel area, for example, locations of restaurants, tourist destinations, and cafes, ratings, visitors, holidays, and the like. The management server 300 may calculate a place density and a place awareness by using a deep learning-based technology from the tourism information. The management server 300 may generate recommendation information indicating a recommendation list based on location density, location awareness, user interest information, and GPS information. Here, the recommendation information may be in a form that a user can easily recognize (eg, a list, a category, etc.). When the management server 300 receives user interest information from the user terminal 200 , the management server 300 may provide the user terminal 200 with recommendation information generated for the corresponding user interest information. A more detailed description thereof will be provided later.

According to the disclosed embodiment, by predicting the arrival time of the travel area selected by the user and analyzing the population density for each place according to the arrival time, a place in the travel area can be recommended according to the travel preference style desired by the user. In addition, by recommending a place in the travel area that matches the user's travel preference style based on the population density and awareness of each place in the travel area selected by the user, it is possible to prevent the concentration of people in a specific place in advance.

2 is a block diagram illustrating the configuration of the management server 300 of the destination recommendation system 100 according to an embodiment of the present invention, and FIG. 3 is a management of the destination recommendation system 100 according to an embodiment of the present invention. It is a block diagram for explaining the place analysis module 330 in the server 300 .

2 and 3 , the management server 300 may include a communication module 310 , a storage module 320 , a place analysis module 330 , and a recommendation information providing module 340 .

In this specification, a module may mean a functional and structural combination of hardware for carrying out the technical idea of the present invention and software for driving the hardware. For example, the “module” may mean a logical unit of a predetermined code and a hardware resource for executing the predetermined code, and does not necessarily mean physically connected code or a single type of hardware. .

The communication module 310 may communicate with the user terminal 200 . For example, the management server 300 may receive user interest information from the user terminal 200 . Here, the user's interest information includes the user's age, gender, travel region, number of travellers, travel theme (eg, prenatal trip, family trip, couple trip, friendship trip, gourmet, vacation, experience, etc.), travel preference style (eg, For example, the density of the travel area, awareness, etc.) may be included. Also, the communication module 310 may communicate with an external server. For example, the management server 300 may receive tourism information from an external server. Here, the tourism information may include information about places in the travel area, for example, locations, ratings, reviews, visitors, holidays, etc. for restaurants, tourist destinations, and cafes.

The storage module 320 may store user interest information and GPS information of the user terminal 200 received from the user terminal 200 . Also, the storage module 320 may store tourism information received from an external server.

The place analysis module 330 may include a first pre-processing module 331 and a first machine learning module 332 .

The first pre-processing module 331 may generate a first data set by extracting a dense element from the tourism information stored in the storage module 320 . Here, the dense element may include a number of people who have visited a specific place (eg, a restaurant, a tourist destination, a cafe, etc.). That is, the first data set may include the number of occupants of each place, users of each place, and usage time. The first preprocessing module 331 may transmit the generated first data set to the first machine learning module 332 .

The first machine learning module 332 may receive the generated first data set and estimate the density of places from the first data set. Here, the place density indicates the number of users with respect to the number of occupants of the place, and may be classified by time period. When the first data set is input, the first machine learning module 332 may perform machine learning based on an already learned density estimation model to estimate the place density in the first data set. The density estimation model is trained to estimate the place density when the first data set is input using a machine learning-based technique. That is, the first machine learning module 332 may be implemented as a density estimation model that receives the first data set and is trained to estimate the density of places for each place, respectively.

In an exemplary embodiment, the first machine learning module 332 may use multiple linear regression, deep learning, or the like as a machine learning technique. In this case, when the first data set is input, the first machine learning module 332 may be a multiple linear regression model trained to estimate and output the place density from the input first data set. . Multiple linear regression may use, for example, polynomial regression, generalization method (GAM), logistic regression (GLM), or the like. The first machine learning module 332 may transmit the location density to the recommendation information providing module 340 .

In addition, the place analysis module 330 may include a second pre-processing module 333 and a second machine learning module 334 .

The second pre-processing module 333 may generate a second data set by extracting a cognitive element from the tourism information included in the storage module 320 . Here, the cognitive factor may include a rating, a review, and the like of a user who has visited a specific place. That is, the second data set may include ratings and reviews of each place. The second preprocessing module 333 may transmit the generated second data set to the second machine learning module 334 .

The second machine learning module 334 may receive the generated second data set and estimate the place recognition from the second data set. When the second data set is input, the second machine learning module 334 may perform machine learning based on the already learned recognition estimation model to estimate place recognition in the second data set. The recognition estimation model is trained to estimate place awareness when the second data set is input using machine learning-based technology. That is, the second machine learning module 334 receives the second data set and may be implemented as an awareness estimation model trained to respectively estimate the place recognition for each place.

In an exemplary embodiment, the second machine learning module 334 may use multiple linear regression, deep learning, or the like as a machine learning technique. In this case, when the second data set is input, the second machine learning module 334 may be a multiple linear regression model trained to estimate and output place recognition from the input second data set. . Multiple linear regression may use, for example, polynomial regression, generalization method (GAM), logistic regression (GLM), or the like. The second machine learning module 334 may transmit the place recognition to the recommendation information providing module 340 .

The recommendation information providing module 340 may generate recommendation information based on location density, location recognition, user interest information, and GPS information.

In an exemplary embodiment, the recommendation information providing module 340 may calculate the arrival time of the travel area based on user interest information and GPS information. The recommendation information providing module 340 may extract the place density for each place in the travel area corresponding to the arrival time of the travel area from the place density. The recommendation information providing module 340 compares the travel preference style included in the user's interest information and the density of places for each place in the travel area, and assigns a preset score to each place in the travel area. For example, when a user's travel preference style is a small number of places, a high score may be given to a place having a low density of places.

In another exemplary embodiment, the recommendation information providing module 340 may extract a place recognition for each place in the travel area based on user interest information. The recommendation information providing module 340 may compare the travel preference style included in the user's interest information with the place recognition for each place in the travel area, and give each place a preset score to each place in the travel area. For example, when a user's travel preference style is an unknown place (an unfamiliar place), a high score may be given to a place with low place recognition.

Also, the recommendation information providing module 340 may generate recommendation information according to the given score. The recommendation information providing module 340 may generate the recommendation information by arranging the scores given to each location in the order of highest. The recommendation information providing module 340 may provide the generated recommendation information to the user terminal 200 . Here, the recommendation information may include place information of a travel area, for example, a business name, a location, a holiday, etc. for a restaurant, a tourist destination, a cafe, and the like.

Therefore, the destination recommendation system 100 according to the present invention predicts the arrival time of the travel area selected by the user and analyzes the population density for each place according to the arrival time, thereby selecting a place in the travel area according to the travel preference style desired by the user. can recommend In addition, by recommending a place in the travel area that matches the user's travel preference style based on the population density and awareness of each place in the travel area selected by the user, it is possible to prevent the concentration of people in a specific place in advance.

4 is a flowchart illustrating a destination recommendation method according to an embodiment of the present invention. The method illustrated in FIG. 4 may be performed, for example, by a computing device having one or more processors and a memory storing one or more programs executed by the one or more processors. To this end, the destination recommendation method may be implemented in the form of a program or software including one or more computer-executable instructions and stored in the memory.

In addition, in the illustrated flowchart, the method is described by dividing the method into a plurality of steps, but at least some of the steps are performed in a reversed order, are performed in combination with other steps, are omitted, or are performed in sub-steps, or shown. One or more steps not included may be added and performed.

Referring to FIG. 4 , the computing device 12 receives user interest information of a user who wants to receive a recommendation for a destination in a travel area ( S401 ), and receives tourism information from an external server ( S402 ).

Next, the computing device 12 estimates the density of places in the travel area from the tourism information (S403).

Next, the computing device 12 generates recommendation information for recommending a destination in a travel area based on the user interest information and the estimated place density ( S404 ).

Finally, the computing device 12 provides the recommended information to the user (S405).

5 is a block diagram illustrating and describing a computing environment 10 including a computing device suitable for use in example embodiments. In the illustrated embodiment, each component may have different functions and capabilities other than those described below, and may include additional components in addition to those described below.

The illustrated computing environment 10 includes a computing device 12 . In one embodiment, computing device 12 may be user terminal 200 . Also, the computing device 12 may be the management server 300 .

Computing device 12 includes at least one processor 14 , computer readable storage medium 16 , and communication bus 18 . The processor 14 may cause the computing device 12 to operate in accordance with the exemplary embodiments discussed above. For example, the processor 14 may execute one or more programs stored in the computer-readable storage medium 16 . The one or more programs may include one or more computer-executable instructions that, when executed by the processor 14, configure the computing device 12 to perform operations in accordance with the exemplary embodiment. can be

Computer-readable storage medium 16 is configured to store computer-executable instructions or program code, program data, and/or other suitable form of information. The program 20 stored in the computer readable storage medium 16 includes a set of instructions executable by the processor 14 . In one embodiment, computer-readable storage medium 16 includes memory (volatile memory, such as random access memory, non-volatile memory, or a suitable combination thereof), one or more magnetic disk storage devices, optical disk storage devices, flash It may be memory devices, other forms of storage medium that can be accessed by computing device 12 and store desired information, or a suitable combination thereof.

Communication bus 18 interconnects various other components of computing device 12 , including processor 14 and computer readable storage medium 16 .

Computing device 12 may also include one or more input/output interfaces 22 and one or more network communication interfaces 26 that provide interfaces for one or more input/output devices 24 . The input/output interface 22 and the network communication interface 26 are coupled to the communication bus 18 . Input/output device 24 may be coupled to other components of computing device 12 via input/output interface 22 . Exemplary input/output device 24 may include a pointing device (such as a mouse or trackpad), a keyboard, a touch input device (such as a touchpad or touchscreen), a voice or sound input device, various types of sensor devices, and/or imaging devices. input devices and/or output devices such as display devices, printers, speakers and/or network cards. The exemplary input/output device 24 may be included in the computing device 12 as a component constituting the computing device 12 , and may be connected to the computing device 12 as a separate device distinct from the computing device 12 . may be

Although representative embodiments of the present invention have been described in detail above, those of ordinary skill in the art to which the present invention pertains will understand that various modifications are possible within the limits without departing from the scope of the present invention with respect to the above-described embodiments. . Therefore, the scope of the present invention should not be limited to the described embodiments, and should be defined by the claims described below as well as the claims and equivalents.

100: destination recommendation system
200: user terminal
300: management server
310: communication module
320: storage module
330: place analysis module
331: first pre-processing module
332: first machine learning module
333: second pre-processing module
334: second machine learning module
340: module for providing recommended information

Claims (16)

one or more processors, and
As a destination recommendation method performed in a computing device having a memory for storing one or more programs executed by the one or more processors,
receiving user interest information of a user who wants to be recommended a destination in a travel area;
Receiving tourism information from an external server;
estimating a density of places in the travel area from the tourism information;
generating recommendation information for recommending a destination in the travel area based on the user interest information and the estimated place density; and
and providing the recommendation information to the user.
The method according to claim 1,
The step of estimating the density of places is,
extracting a preset concentration factor from the tourism information;
generating a first data set based on the cluster element; and
The method of claim 1, further comprising estimating a density of places in the travel area in the first data set via a first machine learning module.
3. The method according to claim 2,
The first machine learning module,
receiving a first data set generated based on the tourism information, and learning to estimate a density of places in the travel area from the first data set.
The method according to claim 1,
The destination recommendation method is:
extracting a preset cognitive factor from the tourism information;
generating a second data set based on the cognitive factors; and
estimating a place awareness of the travel area in the second data set via a second machine learning module.
5. The method according to claim 4,
The second machine learning module,
receiving a second data set generated based on the tourism information, and learning to estimate a place awareness of the travel area from the second data set.
5. The method according to claim 4,
The destination recommendation method is:
Further comprising the step of receiving the user's GPS information,
The step of generating the recommendation information includes:
calculating an arrival time of the travel area based on the user interest information and the GPS information;
extracting a place density for each place in the travel area corresponding to the arrival time of the calculated travel area from the estimated place density; and
Comparing the travel preference style included in the user interest information with the location density for each location in the extracted travel area, the method further comprising the step of giving a preset score to each location in the travel area.
7. The method of claim 6,
The step of generating the recommendation information includes:
Comparing a travel preference style included in the user interest information with a place recognition for each place in the extracted travel area, the method further comprising the step of giving a preset score to each place in the travel area.
8. The method of claim 7,
The step of generating the recommendation information includes:
Recommendation information is generated according to the score given to each place in the travel area,
A destination recommendation method that sorts in order of highest score given to each place.
one or more processors, and
A computing device having a memory for storing one or more programs executed by the one or more processors, the computing device comprising:
a communication module for receiving user interest information of a user who intends to receive a recommendation for a destination in a travel area, and receiving tourism information from an external server;
a place analysis module for estimating a density of places in the travel area from the tourism information; and
and a recommendation information providing module that generates recommendation information for recommending a destination of the travel area based on the user interest information and the estimated place density, and provides the recommendation information to the user.
10. The method of claim 9,
The place analysis module,
a first pre-processing module for extracting a preset density factor from the tourism information and generating a first data set based on the density factor; and
and a first machine learning module for estimating a density of places in the travel area in the first data set.
11. The method of claim 10,
The first machine learning module,
A computing device that receives a first data set generated based on the tourism information and is trained to estimate a density of places in the travel area from the first data set.
10. The method of claim 9,
The place analysis module,
a second pre-processing module for extracting preset cognitive factors from the tourism information and generating a second data set based on the cognitive factors; and
and a second machine learning module for estimating a place awareness of the travel area in the second data set.
13. The method of claim 12,
The second machine learning module,
A computing device that receives a second data set generated based on the tourism information and is trained to estimate a place awareness of the travel area from the second data set.
13. The method of claim 12,
The communication module receives the user's GPS information,
The recommendation information providing module,
The arrival time of the travel area is calculated based on the user interest information and the GPS information, and the location density for each place in the travel area corresponding to the calculated arrival time of the travel area is extracted from the estimated place density. and comparing the travel preference style included in the user interest information with the location density for each location in the extracted travel area, and assigning a preset score to each location in the travel area.
15. The method of claim 14,
The recommendation information providing module,
Computing device, which compares the travel preference style included in the user interest information with a place recognition for each place in the extracted travel area to give a preset score to each place in the travel area.
16. The method of claim 15,
The recommendation information providing module,
Recommendation information is generated according to the score given to each place in the travel area,
Computing device, in order of highest score given by place.

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