KR20190113396A - Method and server for recommending a store using spending history and route information - Google Patents

Abstract

The present invention provides a method for recommending a store based on spending history information and moving route information and a server thereof. The method where a server recommends a store based on spending history information and moving route information comprises: a step of acquiring moving route information representing a moving route between a starting place and a destination set by a first user from a user device; a step of acquiring a first spending history for the first user from a household ledger service database storing payment histories for a plurality of users; a step of acquiring second spending histories for second users from the household ledger service database based on the moving route information; a step of comparing the first and second spending histories to calculate the similarity between the first and second users; a step of selecting a portion of the second users based on a result of the similarity calculation and determining at least one store among stores around the moving route visited by the selected second users as a next visit store of the first user; and a step of supplying store information for the next visit store to the user device.

Description

How to recommend stores and servers based on spending history and route information {METHOD AND SERVER FOR RECOMMENDING A STORE USING SPENDING HISTORY AND ROUTE INFORMATION}

The present disclosure provides a method and server for recommending a store based on the expense information and the movement path information.

Recently, a recommendation service for recommending a place (or store) to a user by using basic information such as user's personal information and user's location has been provided.

However, such a recommendation service has a limit in increasing a user's satisfaction with a store recommendation result in that the recommendation service is recommended to the user using only limited information on the user.

Therefore, a technique for increasing satisfaction with the store recommendation result of users who use the store recommendation service is required.

The present invention provides a method and server for recommending a store based on spending history information and movement path information. In addition, the present invention provides a computer-readable recording medium having recorded thereon a program for executing the method on a computer. The technical problem to be achieved by the present embodiment is not limited to the technical problems as described above, and further technical problems can be inferred from the following embodiments.

As a technical means for achieving the above-described technical problem, a first aspect of the present disclosure, in the method for recommending a store based on the expense history information and the movement route information, the starting point and destination set by the first user from the user device Obtaining movement path information indicating a movement path of the liver; Obtaining first expenses for the first user from an account book service DB in which expenses for a plurality of users are stored; Obtaining a second expenditure history for a second user from the account book service DB based on the movement path information; Calculating a similarity between the first user and the second user by comparing the first spending history with the second spending history; Selecting some of the second users based on a result of the similarity calculation and determining at least one of shops near the moving path visited by the selected second user as the next visited shop of the first user; And providing store information about the next visited store to the user device.

The method may further include selecting some of the second users based on a result of the similarity calculation, and determining at least one store visited by the selected second user among stores near the destination as the next visited store of the first user. It can provide a method comprising;

The method may further include selecting a part of the second users based on a result of the similarity calculation and determining at least one of the stores visited by the selected second user as a via store of the first user. Generating a new movement route based on the diesel store; And providing the generated movement path to the user device.

The user who previously visited at least one of the stores on the movement route among the plurality of users by comparing the store information included in the movement route information and the store information included in the spending history stored in the account book service DB. Determining to be the second user; And obtaining the second expenditure history for the second user from the account book service DB.

Further comprising: obtaining first user registration information for the first user from the user device; And obtaining second user registration information for the second user from the account book service DB. The method may further include calculating a similarity between the first user and the second user by comparing the first user registration information with the second user registration information.

The method may further include calculating a similarity between the first user and the second user by comparing the personal identification information included in the first user registration information with the personal identification information included in the second user registration information. It may provide a method.

The method may further include calculating a similarity between the first user and the second user by comparing the spending amount by category included in the first spending history with the spending amount by category included in the second spending history. It may provide a method.

The method may further include calculating a similarity between the first user and the second user by comparing the purchase item information included in the first expenditure history with the purchase item information included in the second expenditure history. Can provide.

The purchase goods information may be obtained by obtaining the purchase goods information for the plurality of users who use the account book service from an external server, or by obtaining a receipt image photographed by a camera mounted on the user device. The purchase article information may be determined by obtaining information determined or inputted to the user device by the first user.

The method may further include determining at least one of the stores near the moving route visited by the selected second user as the next visit store based on the frequency of visits by the selected second user. can do.

The method may further include determining at least one of the stores near the moving route visited by the selected second user as the next visiting store based on the promotion information about the stores near the moving route visited by the selected second user. It can provide a method comprising;

The first payment time visited by the selected second user may be compared by comparing a popular payment time for each store near the moving route visited by the selected second user with an estimated time of arrival when the first user arrives at a store near the moving route. And determining at least one of the stores around the movement route as the next visited store.

A second aspect of the present disclosure, the storage unit for storing at least one program; Communication unit for transmitting and receiving data to and from the user device and the account book service DB; And at least one processor for recommending a store by using the user device and the account book service DB by executing the at least one program, wherein the storage unit is configured by the at least one processor from the user device. Obtaining movement route information indicating a movement route between a starting point and a destination set by the user; Obtaining first expenses for the first user from the account book service DB in which expenses for a plurality of users are stored; Obtaining a second expenditure history for a second user from the account book service DB based on the movement path information; Calculating a similarity between the first user and the second user by comparing the first spending history with the second spending history; Selecting some of the second users based on a result of the similarity calculation and determining at least one of shops near the moving path visited by the selected second user as the next visited shop of the first user; And providing store information about the next visited store to the user device.

A third aspect of the present disclosure can provide a computer readable recording medium having recorded thereon a program for executing the method of the first aspect on a computer.

1 is a diagram illustrating an example of a store recommendation system according to an exemplary embodiment.
2 is a diagram for describing information stored in an account book service DB, according to an exemplary embodiment.
3 is an exemplary diagram for describing information stored in an account book service DB according to an embodiment.
4 is a flowchart illustrating a method of recommending a store based on spending history information and movement path information, according to an exemplary embodiment.
FIG. 5 is a diagram illustrating an example of comparing store information included in movement route information and store information included in spending history according to an exemplary embodiment.
6 is a diagram for describing an example of calculating a similarity between a first user and a second user, according to an exemplary embodiment.
FIG. 7 is a diagram for explaining an example of recommending a store located around a moving route, according to an exemplary embodiment; FIG.
8 is a diagram for explaining an example of recommending a new moving path according to one embodiment.
9 is a diagram for describing an example of determining a next visit store, according to an exemplary embodiment.
FIG. 10 is a diagram for explaining an example of providing store information about a next visit store, according to an exemplary embodiment; FIG.
11 is a block diagram of a store recommendation server according to an exemplary embodiment.

DETAILED DESCRIPTION Hereinafter, exemplary embodiments of the present invention will be described in detail with reference to the accompanying drawings so that those skilled in the art may easily implement the present invention. As those skilled in the art would realize, the described embodiments may be modified in various different ways, all without departing from the spirit or scope of the present invention. In the drawings, parts irrelevant to the description are omitted in order to clearly describe the present invention, and like reference numerals designate like parts throughout the specification.

Throughout the specification, when a part is "connected" to another part, this includes not only "directly connected" but also "electrically connected" with another element in between. . In addition, when a part is said to "include" a certain component, which means that it may further include other components, except to exclude other components unless otherwise stated.

Also, in the present specification, the first user may be a user who uses the user device and receives a next visit store through the store recommendation system. The second user may be at least a part of a plurality of users whose information is stored in the account book service DB as a user using the account book service.

Hereinafter, the present disclosure will be described in detail with reference to the accompanying drawings.

1 is a diagram illustrating an example of a store recommendation system according to an exemplary embodiment.

Referring to FIG. 1, the store recommendation system may include a user device 1000, a store recommendation server 2000, and an account book service DB 3000.

The user device 1000 may generate movement route information indicating a movement route between the departure point and the destination, based on the departure point and the destination set by the first user. The store recommendation server 2000 of the store recommendation system may receive movement path information from the user device 1000 and receive information related to the first user from the account book service DB 3000 (or the user device 1000). have.

The account book service DB 3000 may store information on a plurality of users who use the account book service, and the store recommendation server 2000 may have visited a store near a moving path where a first user wants to move among a plurality of users. May be determined as the second user. The store recommendation server 2000 may receive information related to the second user from the account book service DB 3000.

The store recommendation server 2000 may calculate the similarity between the first user and the second user by comparing the information related to the first user and the information related to the second user. In one embodiment, the store recommendation server 2000 may calculate the similarity between the first user and the second user based on the similarity of demographic classification between the first user and the second user, the similarity of the consumption propensity, and the like.

The store recommendation server 2000 may determine a store visited by a second user having a high similarity with the first user among stores near the moving path as the next visited store of the first user. In addition, the store recommendation server 2000 may recommend a store by providing store information about a next visit store to the first user through the user device 1000.

The user device 1000 may be a smartphone, a tablet PC, a PC, a smart TV, a mobile phone, a personal digital assistant (PDA), a laptop, a media player, a micro server, a global positioning system (GPS) device, an e-book device, a digital broadcasting terminal. , Navigation, kiosks, MP3 players, digital cameras, home appliances, camera-equipped devices, and other mobile or non-mobile computing devices. In addition, the user device 1000 may be a wearable device such as a watch, glasses, a hair band, and a ring having a communication function and a data processing function. However, the present invention is not limited thereto, and the user device 1000 may include all kinds of devices capable of transmitting and receiving data with the store recommendation server 2000 and the account book service DB 3000 through a network.

The store recommendation server 2000 may be operated by one or a plurality of servers. The store recommendation server 2000 may obtain movement path information, user registration information, and expenditure history for the first user from the user device 1000 through the network. In addition, the store recommendation server 2000 may obtain user registration information and expenditure history for the second user stored in the account book service DB 3000 based on the obtained movement route information.

In addition, the store recommendation server 2000 may receive various data from an external server, and in one embodiment, the store recommendation server 2000 may receive data from a card company server, a store server, an open market server, or the like. The store recommendation server 2000 may perform a process for determining a next visit store of the first user by using the received various information, and provide the user device 1000 with store information regarding the determined next visit store.

Meanwhile, a store recommendation server 2000 solution may be provided to a seller who wants to use a store recommendation system. The seller can use the store recommendation system by executing a service equipped with a store recommendation server 2000 solution.

The account book service DB 3000 may store user registration information and expenses for a plurality of users who use the account book service. In one embodiment, the account book service may be provided in the form of a mobile application, and information collected by a plurality of users in the account book service and data collected while using the account book service may be stored in the account book service DB 3000.

Networks include Local Area Networks (LANs), Wide Area Networks (WANs), Value Added Networks (VANs), mobile radio communications networks, satellite networks, and their combinations. In addition, a comprehensive data communication network that enables each network constituent illustrated in FIG. 1 to communicate smoothly with each other may include a wired internet, a wireless internet, and a mobile wireless communication network. In addition, the wireless communication is, for example, wireless LAN (Wi-Fi), Bluetooth, Bluetooth low energy (ZiBee), Zigbee, WFD (Wi-Fi Direct), UWB (ultra wideband), infrared communication (IrDA, infrared) Data Association), Near Field Communication (NFC), and the like, but are not limited thereto.

2 is a diagram for describing information stored in an account book service DB, according to an exemplary embodiment.

Referring to FIG. 2, the account book service DB 3000 may include user registration information 210 and expense history 220. The user registration information 210 and the spending history 220 may be matched and stored for each of a plurality of users who use the account book service.

The user registration information 210 may be information registered to the account book service by a plurality of users who use the account book service. For example, the account book service may be provided to the user in the form of an application. The user may register various information on the account book service application, and the registered information may be transmitted to and stored in the account book service DB 3000.

In one embodiment, the user registration information 210 may include personally identifiable information, retention card information, and a limit for each consumption category. Personally identifiable information may include gender, age, and residence, and retained card information may include a card company, a card name, at least some card numbers, and a card limit.

On the other hand, consumption categories can be divided into dining, shopping, culture / art, life / mart, cafe / snacks / transportation, hospital, communication, education, leisure, etc., the user registers the limit (amount) for each consumption category can do.

The spending history 220 may be a set of information generated by the user making a payment in a store. The spending history 220 may be generated by grouping and accumulating the information generated for each payment activity.

For example, when a user makes a payment at a store, a third party (for example, a card company and / or a van company) generates information related to a payment, and an application that provides an account book service is provided by a third party. Information related to payment can be received. The application providing the account book service may generate the expense history 220 by continuously storing the information related to the payment received in the above manner in the account book service DB 3000.

In one embodiment, the spending history 220 may include spending amount, spending date / time, spending store information, and spending card information, and spending store information includes store name, location, category, promotion information, and deadline time. Can be.

Meanwhile, at least some of the information belonging to the spending store information may be updated periodically. In one embodiment, the server including the account book service DB and provides the account book service (hereinafter, the account book server) receives the latest information per store from an external server (for example, store server, open market server), and received the store By storing the latest star information in the account book service DB 3000, it is possible to periodically update the expenditure store information of the expenditure history 220. For example, the account book server may update the promotion information in the spending store information of the spending history 220 by receiving the latest promotion information for each store from an external server.

However, the information constituting the account book service DB 3000 is not limited to the above description, and likewise, the information constituting the user registration information 210 and the expenditure history 220 and their sub-items is also not limited to the above description. Do not.

The information stored in the account book service DB 3000 may then be used by the store recommendation server 2000 to perform a process for determining a next visit store of the first user.

3 is an exemplary diagram for describing information stored in an account book service DB according to an embodiment.

Referring to FIG. 3, the account book service DB 3000 may include user registration information 310, expenditure details 320, and purchase item information 330. The user registration information 310, the spending history 320, and the purchase item information 330 may be matched and stored for each of a plurality of users who use the account book service. Meanwhile, since the user registration information 310 and the expenditure history 320 of FIG. 3 are the same as those described above with reference to FIG. 2, the description thereof will be omitted for convenience.

The purchase item information 330 may be information about an item being sold in an online / offline store. In an embodiment, a server providing an account book service (hereinafter, referred to as an account book server) may obtain purchase item information 330 for a plurality of users who use the account book service from an external server (eg, an open market server). . The external server may systematically collect various pieces of information provided by the plurality of users and / or sellers to the external server and use the generated information 330 for the plurality of users. The account book server may obtain the purchase item information 330 from an external server.

In addition, the account book server may determine the purchase goods information 330 for a plurality of users using the account book service by obtaining a receipt image photographed by a camera mounted on the user device. In addition, the account book server may determine purchase item information 330 for a plurality of users using the account book service by obtaining information directly input to the user device 1000 by the first user.

However, the method of acquiring the purchase information 330 by the account book server is not limited thereto. On the other hand, the account book server may store the purchase goods information 330 obtained by the above-described method in the account book service DB (3000).

In one embodiment, the purchase item information 330 may include the item name, price, brand name, sales frequency, promotion information, vendor information, specification, category, sale date and buyer information for each purchase item.

Meanwhile, at least some of the information belonging to the purchased goods information 330 may be updated periodically. In one embodiment, the account book server receives the latest item-specific information from an external server (for example, the store server, open market server), and stores the latest item-specific information received in the account book service DB (3000), The information 330 may be updated periodically. For example, the account book server may update the price and promotion information of the purchase goods information 330 by receiving the latest price information and promotion information for each item from an external server.

In one embodiment, the account book service DB 3000 may further include vehicle rental information (not shown). For example, the vehicle rental information (not shown) may include information such as a vehicle model, a time, a departure point / destination, a waypoint, but is not limited thereto.

However, the information constituting the account book service DB 3000 is not limited to the above description, and similarly, the purchase item information 330 and the vehicle rental information (not shown) and the information constituting subordinate items thereof are also described above. It is not limited.

The information stored in the account book service DB 3000 may then be used by the store recommendation server 2000 to perform a process for determining a next visit store of the first user.

4 is a flowchart illustrating a method of recommending a store based on spending history information and movement path information, according to an exemplary embodiment.

Referring to FIG. 4, in operation 410, the store recommendation server 2000 may obtain movement route information indicating a movement route between a departure point and a destination set by a first user from the user device 1000.

The movement route information may include not only basic information such as movement distance and movement time, but also store information of nearby stores located around the movement route. However, the information included in the movement path information is not limited thereto.

In an embodiment, the user device 1000 may be an electronic device in which a navigation or map app is installed. The user device 1000 may receive an input for setting a departure point and a destination from the first user. In addition, the user device 1000 may be equipped with a global positioning system (GPS) to set the location of the user device 1000 as a starting point, and receive an input for setting a destination from the first user.

The user device 1000 may generate movement route information based on a departure point and a destination, and provide the generated movement route information to the store recommendation server 2000. According to an embodiment, the user device 1000 may generate the shortest moving route between the starting point and the destination and provide the moving route information to the store recommendation server 2000, but the method of generating the moving route is not limited thereto. .

In operation 420, the store recommendation server 2000 may obtain a first spending history for the first user from the account book service DB 3000 in which spending history for the plurality of users is stored.

The first spending history may be a set of information generated by the first user making a payment at a specific store. The first spending history may include a payment amount, payment date / time, payment store information, and payment card information for each spending of the first user. The first spending history may include usage history of a plurality of expenses incurred as the first user uses the account book service.

The account book service may be provided to the first user in the form of an application installed in the user device 1000, and the store recommendation server 2000 may be provided to the first user from the account book service DB 3000 communicating with the account book service application through a network. A first expense statement can be obtained.

In addition, the store recommendation server 2000 may obtain first user registration information for the first user from the account book service DB 3000. The first user registration information may be information registered by a first user using an account book service. The first user registration information may include personally identifiable information registered in the application, holding card information, and a limit for each consumption category.

In one embodiment, the first user registration information and the first expense history may be stored in a memory of the user device 1000. In this case, the store recommendation server 2000 may obtain the first spending history and the first user registration information for the first user from the user device 1000.

The first user registration information and the first expenditure may then be used to calculate a similarity with the second user in step 440.

In operation 430, the store recommendation server 2000 may obtain a second expense history for the second user from the account book service DB 3000 based on the movement route information.

The second expenditure history may include a payment amount, payment date / time, payment store information, and payment card information for each expenditure of the second user. The second expenditure history may include usage history of a plurality of expenditures generated as the second user uses the household accounting service.

Meanwhile, as described above in step 410, the movement route information may include not only basic information such as movement distance and movement time, but also store information of neighboring stores located around the movement route.

In one embodiment, the store recommendation server 2000 may compare the store information included in the movement information about the first user with the store information included in the expense history stored in the account book service DB 3000.

The store recommendation server 2000 may determine the user having the same store information as the store information of the first user among the plurality of users stored in the account book service DB 3000 as the second user by comparing the store information. That is, among the plurality of users stored in the account book service DB 3000, the user who has previously visited the store located near the moving path to which the first user wants to move may be determined as the second user.

In operation 440, the store recommendation server 2000 may calculate a similarity between the first user and the second user by comparing the first expense history with the second expense history.

In one embodiment, the store recommendation server 2000 may calculate the similarity between the first user and the second user by comparing the spending amount by category included in the first spending history and the spending amount by category included in the second spending history. Can be.

In addition, the account book service DB 3000 may store user registration information for a plurality of users. The user registration information may include, but is not limited to, personally identifiable information, retention card information, and limits for consumption categories.

The store recommendation server 2000 may obtain first user registration information for the first user from the user device 1000, and obtain second user registration information for the second user from the account book service DB. The store recommendation server 2000 may calculate the similarity between the first user and the second user by comparing the first user registration information with the second user registration information.

In one embodiment, the store recommendation server 2000 may calculate similarity between the first user and the second user by comparing the personal identification information included in the first user registration information with the personal identification information included in the second user registration information. Can be.

In addition, the account book service DB 3000 may store the purchase goods information. The purchased goods information may include, but is not limited to, the item name, price, brand name, number of sales, promotion information, vendor information, specifications, categories, and sales date for each purchased item.

The store recommendation server 2000 obtains first purchased goods information on the goods purchased by the first user from the user device 1000 from the user device 1000, and stores the second purchased goods from the household account book DB. Purchase goods information can be obtained. The store recommendation server 2000 may calculate a similarity between the first user and the second user by comparing the first purchase item information with the second purchase item information.

The account book service DB 3000 may store vehicle rental information. For example, the vehicle rental information may include information such as a car model, time, origin / destination, stopover, etc., but is not limited thereto.

The store recommendation server 2000 may calculate similarity between the first user and the second user by obtaining vehicle rental information for each of the first user and the second user from the account book service DB 3000 and comparing them. The store recommendation server 2000 may obtain vehicle rental information from an external server (or an external DB) instead of the account book service DB 3000.

However, the method of calculating the similarity between the first user and the second user is not limited thereto.

In operation 450, the store recommendation server 2000 selects some of the second users based on the similarity calculation result, and stores at least one of the stores near the moving route visited by the selected second user as the next visited store of the first user. You can decide.

The store recommendation server 2000 may select some of the second users whose high similarity with the first user is calculated based on the similarity result calculated in operation 440.

In one embodiment, the store recommendation server 2000 may determine at least one store near a moving route visited by the selected second user as the next visit store of the first user based on the frequency of visits of the selected second user by store. Can be.

In an embodiment, the store recommendation server 2000 may store at least one of the stores near the moving path visited by the selected second user based on the promotion information about the plurality of stores visited by the selected second user. You can decide to visit the store.

In one embodiment, the store recommendation server 2000 compares the popular payment time slots for a plurality of stores visited by the selected second user with the estimated time of arrival when the first user arrives at a store near a moving route, thereby visiting the selected second user. At least one of the stores around the movement route may be determined as the next visit store of the first user.

However, the method of determining the next visit store of the first user is not limited thereto.

In operation 460, the store recommendation server 2000 may provide store information regarding the next visit store to the user device 1000.

In one embodiment, the store recommendation server 2000 may determine at least one of stores near a moving route or a destination as a next visit store to the first user, and may provide store information about the next visit store. Store information for the next visit store may be displayed on the screen of the user device 1000.

In addition, the store recommendation server 2000 may determine a portion of the stores visited by the second user determined as having a high similarity with the first user in step 450 as a via store of the first user. The store recommendation server 2000 may determine a waypoint store based on the frequency of visits by second users for each store, promotion information, popular payment time zones, and estimated time of arrival.

At this time, the store recommendation server 2000 may generate a new movement route different from the previously generated movement route based on the determined diesel store, and recommend it to the first user. The store recommendation server 2000 may generate a new movement route based on the positional relationship between the waypoints and the visit order of the selected second user for the waypoint.

The store recommendation server 2000 may provide the user device 1000 with store information about the generated new movement route and the waypoint store.

FIG. 5 is a diagram illustrating an example of comparing store information included in moving route information and store information included in spending history according to an exemplary embodiment.

Referring to FIG. 5, the user device 1000 may generate movement route information 510 indicating a movement route between a departure point and a destination, based on a departure point and a destination set by a first user. The movement route information may include not only basic information such as movement distance and movement time, but also store information of nearby stores located near the movement route, but is not limited thereto. The store recommendation server 2000 may receive the movement route information 510 from the user device 1000.

In addition, the store recommendation server 2000 may obtain a spending history 520 for a plurality of users stored in the account book service DB 3000. The spending history 520 may include, but is not limited to, an expenditure amount, an expiration date / time, and expenditure store information.

The store recommendation server 2000 may determine the moving path information 510 received from the user device 1000 and the account book service DB 3000 to determine a part of the plurality of users stored in the account book service DB 3000 as the second user. Compare spending history 520 for a plurality of users stored in the). The store recommendation server 2000 may calculate a similarity degree with the first user with respect to the second user determined based on the comparison result.

In one embodiment, the store recommendation server 2000 may compare the neighboring store information included in the movement route information 510 and the spending store information included in the spending history 520. The store information may include a name, a location, a category, and the like of the store. For example, the store recommendation server 2000 may compare the surrounding store information and the spent store information by using the store name. Through this, the store recommendation server 2000 may determine some users who have visited the same store as the first user among the plurality of users stored in the account book service DB 3000 as the second user. However, the store recommendation server 2000 may compare the payment information 410 and the spending history 420 using various information in addition to the name of the store included in the store information.

6 is a diagram for describing an example of calculating a similarity between a first user and a second user, according to an exemplary embodiment.

Referring to FIG. 6, the store recommendation server 2000 may use a first user by using information about a first user obtained from the user device 1000 and information about a second user obtained from the account book service DB 3000. And similarity between the second user and the second user. In an embodiment, the store recommendation server 2000 may obtain at least some of the information about the first user from the account book service DB 3000.

The store recommendation server 2000 may compare the first user registration information 611 with the second user registration information 621, compare the first spending history 612 with the second spending history 622, or purchase a first purchase. By comparing the goods information 613 and the second purchase goods information 623, it is possible to calculate the similarity between the first user and the second user.

In one embodiment, the store recommendation server 2000 compares the personal identification information included in the first user registration information 611 with the personal identification information included in the second user registration information 621. Similarity between users can be calculated. Personally identifiable information may include gender, age and place of residence. For example, if the personal information of the first user is “Women”, “35 years old” and “Resident Gangnam”, store recommendations The server 2000 analyzes the personal information of the second user so that the second user having the personal information of "Women", "35 years old", and "resident of Gangnam" has a high similarity with the first user. It can be determined to have.

The store recommendation server 2000 may calculate the similarity between the first user and the second user by using the retention card information and the consumption category limit in addition to the personal information included in the user registration information.

In one embodiment, the store recommendation server 2000 may calculate the expenditure amount for each category using the expenditure amount and expenditure store information included in the expenditure history. The categories are divided into meal, shopping, culture / art, life / mart, cafe / snacks, transportation / hospital, hospital, communication, education, leisure, etc. The store recommendation server 2000 selects each store category from expenditure store information. After extracting, the amount of spending by category can be calculated by summing the amount of money spent in stores belonging to the same (or similar) category. Meanwhile, the calculated spending amount for each category may be stored in the spending history.

The store recommendation server 2000 compares the expenditure of each category included in the first expenditure 612 and the expenditure of each category included in the second expenditure 622 to determine the similarity between the first user and the second user. Can be calculated. For example, if the spending amount of each category of the first user is ““ Meal: 500,000 won, shopping: 200,000 won, leisure: 200,000 won ””, the store recommendation server 2000 may calculate the spending amount of each category by the second user. By analyzing, “Scale: 500,000 won, Shopping: 200,000 won, Leisure: 300,000 won” or “Meal: 50%, Shopping: 20%, Leisure: 30%” It can be determined that the second user has a high similarity with the first user.

The store recommendation server 2000 may calculate a similarity between the first user and the second user by using the expiration date / time in addition to the expenditure of each category included in the expenditure history.

In one embodiment, the store recommendation server 2000 may calculate the similarity between the first user and the second user by comparing the first user purchase item information 613 and the second user purchase item information 623. For example, if the first user purchased the brand B sneakers in the B market, the first purchase item information 613 may be ““ Seller information: market A, brand: B brand, item name: C sneakers ”” Can be. The store recommendation server 2000 analyzes the second user purchase item information 623 and purchases items in which at least a part of the information matches “seller information: A market, brand: B brand, item name: C sneakers”. It can be determined that the second user with the information has a high similarity with the first user.

FIG. 7 is a diagram for explaining an example of recommending a store located around a moving route, according to an exemplary embodiment; FIG.

Referring to FIG. 7, the user device 1000 may generate movement route information about a movement route 710 and a movement route 710 between a departure point and a destination based on a departure point and a destination set by a first user. . The movement path 710 may be displayed on the screen of the user device 1000. The movement route information may include not only basic information such as movement distance and movement time, but also store information of nearby stores located around the movement route, and the movement route information may be stored in the user device 1000. On the other hand, the starting point (place A) may be set to the current position of the user device 1000 using the GPS function mounted on the user device 1000.

The store recommendation server 2000 may receive movement route information from the user device 1000. The store recommendation server 2000 may select some of a plurality of stores on the movement route 710 based on the movement route information. In one embodiment, the store recommendation server 2000 may select a popular store among a plurality of stores on the movement route 710 or select some stores among the plurality of stores in consideration of the consumption tendency of the first user. This is not restrictive. For example, the store recommendation server 2000 may select a B store 711 and a C store 712 from a plurality of stores on the movement route 710.

In one embodiment, the store recommendation server 2000 obtains expenditure history for a plurality of users from the account book service DB 3000, and stores B stores 711 or a plurality of users based on the expenditure store information included in the expenditure history. The user who has visited the C store 712 may be determined as the second user.

The store recommendation server 2000 may calculate the similarity between the first user and the second user based on the similarity of demographic classification between the first user and the second user, the similarity of the consumption propensity, and the like. The store recommendation server 2000 may determine a store visited by a second user having a high similarity with the first user among neighboring stores near the store B 711 or the store C 712 as the next visit store of the first user. have.

Here, the surrounding stores are among the stores located within a radius of a distance 730 from a particular store (eg, B store 711 or C store 712). It may be a store visited by a second user having a high similarity with the first user.

For example, the store recommendation server 2000 may include a neighbor store 1 and a neighbor store 2 722 among stores located within a radius of a predetermined distance 730 from each of the store B 711 and the store C 712. May be determined as the next visited store of the first user. In this case, the surrounding store 1 721 and the surrounding store 2 722 may be stores visited by a second user having a high similarity with the first user. In addition, the store recommendation server 2000 may determine the neighboring store 3 723 located within a radius of a predetermined distance from the destination as the next visited store of the first user.

The store recommendation server 2000 may include a plurality of stores visited by a second user having a high similarity with the first user among neighboring stores located within a radius of a predetermined distance 730 of the B store 711 and the C store 712. In this case, at least some of the nearby stores may be determined as the next visited store of the first user in consideration of the visit frequency for each store, the promotion information, and the popular payment time. For example, when the store 1 721 has the highest frequency of visits among stores for the store B 711, the store recommendation server 2000 selects the store 1 721 after the first user. You can decide to visit the store. In the same manner, the store recommendation server 2000 may determine the neighbor stores 2 to 3 (722, 723) as the next visit store of the first user.

The store recommendation server 2000 may transmit store information about the surrounding stores 1 to 3 (721, 722, 723) to the user device 1000, and the user device 1000 may display the surrounding stores 1 to 3 (721) on the screen. , 722, 723 may be displayed. In addition, the user device 1000 may provide store information for the surrounding stores 1 to 3 (721, 722, 723) according to a request of the first user.

8 is a diagram for explaining an example of recommending a new moving path according to one embodiment.

Hereinafter, the content overlapping with FIG. 7 will be omitted for convenience.

Referring to FIG. 8, the user device 1000 may display the movement route 820 between the origin and the destination on the screen of the user device 1000 based on the origin and the destination set by the first user.

The store recommendation server 2000 may calculate the similarity between the first user and the second user based on the similarity of demographic classification between the first user and the second user, the similarity of the consumption propensity, and the like. The store recommendation server 2000 may determine a store visited by a second user having a high similarity with the first user among the stores near the store B or store C as the via store of the first user. For example, the store recommendation server 2000 may determine the diesel store 1 to 2 (811, 812) as the diesel store of the first user.

The store recommendation server 2000 may generate a new movement route 810 based on the diesel store 1 to 2 (811, 812) determined as the diesel store. The user device 1000 may display the new movement route 810 and the locations of the first and second stores 811 and 812 on the screen.

9 is a diagram for describing an example of determining a next visit store, according to an exemplary embodiment.

Referring to FIG. 9, the store recommendation server 2000 may select users A to C as a user having a high similarity with the first user among the second users.

The store recommendation server 2000 may extract a store visited by users A to C, and determine the next visited store of the first user in consideration of the visit frequency for each store, promotion information, popular payment time, and the like.

In one embodiment, the store recommendation server 2000 may determine the next visit store of the first user based on the store frequency of each user A to C. For example, if a place where the user A to C visited the highest frequency is a movie theater, the store recommendation server 2000 may determine the movie theater as the next visit store and recommend it to the first user.

In one embodiment, the store recommendation server 2000 may determine the next visited store of the first user based on the promotion information of the store visited by users A to C. For example, if a ““ 30% discount ”” promotion is in progress at a cosmetics store visited by user A and user B, the store recommendation server 2000 may select the cosmetic store as the next visit store and recommend it to the first user. Can be.

In one embodiment, the store recommendation server 2000 may determine the next visit store of the first user based on the popular payment time of the store visited by users A to C. In this case, the store recommendation server 2000 may receive the current location of the first user from the user device 1000, and calculate an estimated arrival time for the first user to arrive at each store.

The store recommendation server 2000 may recommend one of the stores visited by users A to C to the first user by comparing the popular payment time of the store with the estimated time of arrival when the first user arrives at each store.

The store recommendation server 2000 may determine the next visited store of the first user by considering various factors in addition to the above-described elements.

FIG. 10 is a diagram for explaining an example of providing store information about a next visit store, according to an exemplary embodiment; FIG.

The store recommendation server 2000 may determine a next visit store of the first user and transmit store information about the next visit store to the user device 1000. The user device 1000 may display store information about a next visit store on a screen.

Referring to FIG. 10, the store recommendation server 2000 may determine the next visited store of the first user as an “Italian pizza” and transmit store information about the “Italian pizza” to the user device 1000. The user device 1000 may display store name, location, category, promotion information, and the like on the screen as store information about 'Italian pizza'. However, the displayed store information is not limited thereto.

11 is a block diagram of a store recommendation server according to an exemplary embodiment.

Referring to FIG. 11, the store recommendation server 2000 may include a communication unit 1110, a processor 1120, and a DB 1130. Only store components related to the embodiment are shown in the store recommendation server 2000 of FIG. 11. Accordingly, it will be understood by those skilled in the art that other general purpose components may be further included in addition to the components shown in FIG. 8.

The communication unit 1110 may include one or more components that enable wired / wireless communication with the user device 1000 and the account book service DB 3000. For example, the communication unit 2100 may include at least one of a short range communication unit (not shown), a mobile communication unit (not shown), and a broadcast receiving unit (not shown).

The DB 1130 is hardware that stores various data processed in the store recommendation server 2000, and may store a program for processing and controlling the processor 1120. The DB 1130 may store various data for allowing the first user to determine a next visit store.

The DB 1130 may include random access memory (RAM) such as dynamic random access memory (DRAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), CD- ROM, Blu-ray or other optical disk storage, hard disk drive (HDD), solid state drive (SSD), or flash memory.

The processor 1120 controls the overall operation of the store recommendation server 2000. For example, the processor 1120 may control the input unit (not shown), the display (not shown), the communication unit 1110, the DB 1130, and the like by executing programs stored in the DB 1130. The processor 1120 may control an operation of the store recommendation server 2000 of FIGS. 1 to 10 by executing programs stored in the DB 1130.

The processor 2200 may include application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, and microcontrollers. (micro-controllers), microprocessors (microprocessors), may be implemented using at least one of the electrical unit for performing other functions.

The embodiments may also be implemented in the form of a recording medium including instructions executable by a computer, such as a program module executed by the computer. Computer readable media can be any available media that can be accessed by a computer and includes both volatile and nonvolatile media, removable and non-removable media. In addition, computer readable media may include both computer storage media and communication media. Computer storage media includes both volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Communication media typically includes computer readable instructions, data structures, other data in modulated data signals such as program modules, or other transmission mechanisms, and includes any information delivery media.

Further, in this specification, a “part” may be a hardware component such as a processor or a circuit, and / or a software component executed by a hardware component such as a processor.

The foregoing description of the specification is intended to be illustrative, and it is understood that those skilled in the art can easily modify the present invention into other specific forms without changing the technical spirit or essential features of the present invention. Could be. Therefore, it should be understood that the embodiments described above are exemplary in all respects and not restrictive. For example, each component described as a single type may be implemented in a distributed manner, and similarly, components described as distributed may be implemented in a combined form.

The scope of the present embodiment is indicated by the following claims rather than the above description, and should be construed as including all changes or modifications derived from the meaning and scope of the claims and their equivalents.

Claims (14)

In the method of recommending a store based on the expense information and travel path information,
Obtaining movement route information indicating a movement route between a starting point and a destination set by the first user from the user device;
Obtaining first expenses for the first user from an account book service DB in which expenses for a plurality of users are stored;
Obtaining a second expenditure history for a second user from the account book service DB based on the movement path information;
Calculating a similarity between the first user and the second user by comparing the first spending history with the second spending history;
Selecting some of the second users based on a result of the similarity calculation and determining at least one of shops near the moving path visited by the selected second user as the next visited shop of the first user; And
Providing store information on the next visited store to the user device;
Including, the method. The method of claim 1,
The determining step,
Selecting some of the second users based on a result of the similarity calculation and determining at least one store visited by the selected second user among stores near the destination as the next visited store of the first user;
Including, the method. The method of claim 1,
The determining step,
Selecting some of the second users based on a result of the similarity calculation, and determining at least one of the stores visited by the selected second user as a via store of the first user;
Including,
The providing step,
Generating a new movement route based on the diesel store; And
Providing the generated movement path to the user device;
Including, the method. The method of claim 1,
Acquiring the second expense history,
By comparing the store information included in the movement route information and the store information included in the spending history stored in the account book service DB, the user who previously visited at least one of the stores on the movement route among the plurality of users; Determining as a second user; And
Obtaining the second expense history for the second user from the account book service DB;
Including, the method. The method of claim 1,
The method,
Obtaining first user registration information for the first user from the user device; And
Obtaining second user registration information for the second user from the account book service DB;
More,
Computing the similarity,
Calculating a similarity degree between the first user and the second user by comparing the first user registration information with the second user registration information;
Including, the method. The method of claim 5,
Computing the similarity,
Calculating a similarity degree between the first user and the second user by comparing the personal identification information included in the first user registration information with the personal identification information included in the second user registration information;
Including, the method. The method of claim 1,
Computing the similarity,
Calculating a similarity between the first user and the second user by comparing the spending amount by category included in the first spending history with the spending amount by category included in the second spending history;
Including, the method. The method of claim 1,
Computing the similarity,
Calculating similarity between the first user and the second user by comparing the purchase item information included in the first expenditure history with the purchase item information included in the second expenditure history;
Including, the method. The method of claim 8,
The purchase item information,
The purchase goods information is determined by acquiring the purchase goods information for the plurality of users using the account book service from an external server, or by obtaining a receipt image photographed by a camera mounted on the user device, And the purchase item information is determined by obtaining information input to the user device. The method of claim 1,
The determining step,
Determining at least one of stores near the moving route visited by the selected second user as the next visit store based on the frequency of visits of the selected second users by stores;
Including, the method. The method of claim 1,
The determining step,
Determining at least one of the stores near the movement route visited by the selected second user as the next visit store based on the promotion information about the stores near the movement route visited by the selected second user;
Including, the method. The method of claim 1,
The determining step,
The mobile route visited by the selected second user is compared by comparing a popular payment time for each store near the movement route visited by the selected second user with an estimated time of arrival when the first user arrives at a store near the movement route Determining at least one store of nearby stores as the next visited store;
Including, the method. A storage unit for storing at least one program;
Communication unit for transmitting and receiving data to and from the user device and the account book service DB; And
At least one processor for recommending a store using the user device and the account book service DB by executing the at least one program;
Including;
The storage unit, the at least one processor,
Obtaining movement route information indicating a movement route between a starting point and a destination set by a first user from the user device;
Obtaining first expenses for the first user from the account book service DB in which expenses for a plurality of users are stored;
Obtaining a second expenditure history for a second user from the account book service DB based on the movement path information;
Calculating a similarity between the first user and the second user by comparing the first spending history with the second spending history;
Selecting some of the second users based on a result of the similarity calculation and determining at least one of shops near the moving path visited by the selected second user as the next visited shop of the first user; And
Providing store information on the next visited store to the user device;
And instructions for causing the server to execute. A computer-readable recording medium having recorded thereon a program for executing the method of at least one of claims 1 to 12.

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