KR101917866B1 - Method and apparatus for recommending restaurant - Google Patents

Abstract

The present invention relates to a method for recommending a restaurant and an apparatus thereof. The apparatus for recommending a restaurant comprises: a communication part receiving restaurant data and information on a current location; a storage part storing meal data acquired from a user and restaurant data received through the communication part; and a control part searching for and recommending a restaurant matching user requirements by receiving a restaurant recommendation request from the user, wherein the reference data can include information on a current location received in the communication part, the restaurant data, and the meal data acquired from the user.

Description

[0001] METHOD AND APPARATUS FOR RECOMMENDING RESTAURANT [0002]

The present invention relates to a method and apparatus for recommending a restaurant. More particularly, the present invention relates to a method and apparatus for predicting a restaurant desired by a user on the basis of a user's current location and a record of the user's visit restaurant using an electronic device, and recommending the restaurant to the user.

As the proportion of Internet searches increases in everyday life, there are more and more people who visit the Internet after performing an Internet search. However, in the case of a restaurant search through the Internet, a batch result is displayed without taking the user's taste and situation into account, so that the user has to inconvenience such as trying to perform additional searches several times in order to obtain information about a restaurant desired. Also, in the past, information on restaurants visited by users has not been recorded, and accordingly, a function of automatically searching for new restaurants that the user has not visited in the past has not been provided.

On the other hand, the prior art related to the present invention is disclosed in Japanese Patent Application Laid-Open No. 10-2017-0080152 (method and apparatus for providing a store recommendation service).

SUMMARY OF THE INVENTION The present invention has been made to solve the above-described problems, and it is an object of the present invention to search for and recommend a restaurant in a new field that the user has not visited in the past. There is a purpose to search and recommend the corresponding restaurant.

A restaurant recommendation apparatus according to an embodiment of the present invention may include a communication unit for receiving the restaurant data and information on the current location, a storage unit for storing the meal data acquired from the user and the restaurant data received through the communication unit, The restaurant data and the meal data obtained from the user are received from the communication unit, and the reference data is transmitted from the communication unit to the restaurant, .

Various embodiments of the present invention may conveniently record information about a restaurant used by a user.

Further, the present invention can recommend a different type of restaurant to the user, based on the collected information on the restaurants used by the user.

Also, the present invention can recommend a restaurant reflecting the sensitivity of the user to the price when using the restaurant and the sensitivity of the restaurant to the same category.

1 is a block diagram showing the configuration of a restaurant recommendation apparatus according to an embodiment of the present invention.
2 is a flowchart illustrating a procedure of a restaurant recommendation and a meal data accumulation operation according to an embodiment of the present invention.
3 is a flowchart illustrating a redundant sensitivity calculation process according to an embodiment of the present invention.
4A and 4B are diagrams illustrating an example of a meal data and a meal data input window according to an embodiment of the present invention.
5 is a flowchart illustrating a procedure of calculating a price sensitivity according to an embodiment of the present invention.
6 is a graph for explaining the concept of price sensitivity calculation.
FIG. 7 is a table showing a method of classifying a restaurant according to an embodiment of the present invention.
8 is a block diagram showing a configuration of a price sensitivity determining unit according to an embodiment of the present invention.

While the invention is susceptible to various modifications and alternative forms, specific embodiments thereof are shown by way of example in the drawings and will herein be described in detail.

It should be understood, however, that the invention is not intended to be limited to the particular embodiments, but includes all modifications, equivalents, and alternatives falling within the spirit and scope of the invention. Like reference numerals are used for like elements in describing each drawing.

It is to be understood that when an element is referred to as being "connected" or "connected" to another element, it may be directly connected or connected to the other element, . On the other hand, when an element is referred to as being "directly connected" or "directly connected" to another element, it should be understood that there are no other elements in between.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The singular expressions include plural expressions unless the context clearly dictates otherwise. In this application, the terms "comprises", "having", and the like are intended to specify the presence of stated features, integers, steps, operations, components, parts, or combinations thereof, But do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, or combinations thereof.

1 is a block diagram showing the configuration of a restaurant recommendation apparatus according to an embodiment of the present invention.

More specifically, FIG. 1 illustrates a configuration of a restaurant recommendation apparatus implemented on a user terminal according to an embodiment of the present invention. Referring to FIG. 1, the restaurant recommendation apparatus may include a communication unit 110, a storage unit 120, an input unit 130, a display unit 140, a camera unit 150, a sensor unit 160, and a controller 170. The storage unit 120 may store the meal data 121, the restaurant data 122, the user data 123, and the sensitivity data 124. The control unit 170 may include a redundancy sensitivity determination unit 171, a price sensitivity determination unit 172, a restaurant classification unit 173, a restaurant recommendation unit 174, and a travel distance sensitivity determination unit 175.

The communication unit 110 may include, for example, a cellular module, a WiFi module, a Bluetooth module, a GPS module (e.g., a GPS module, a Glonass module, a Beidou module, or a Galileo module), an NFC module and a radio frequency (RF) module.

The cellular module may provide voice calls, video calls, text services, or Internet services, for example, over a communication network. According to one embodiment, the cellular module may utilize a subscriber identity module (e.g., a SIM card) to perform identification and authentication of the electronic device within the communication network. According to one embodiment, the cellular module may perform at least some of the functions that the processor can provide. According to one embodiment, the cellular module may include a communication processor (CP).

Each of the WiFi module, the Bluetooth module, the GPS module, or the NFC module may include a processor for processing data transmitted and received through the corresponding module, for example. According to some embodiments, at least some (e.g., two or more) of the cellular module, the WiFi module, the Bluetooth module, the GPS module, or the NFC module may be included in one integrated chip (IC) or IC package.

The RF module is capable of transmitting and receiving, for example, a communication signal (e.g., an RF signal). The RF module may include, for example, a transceiver, a power amplifier module (PAM), a frequency filter, a low noise amplifier (LNA), or an antenna. According to another embodiment, at least one of the cellular module, the WiFi module, the Bluetooth module, the GPS module or the NFC module can transmit and receive RF signals through separate RF modules.

The communication unit 110 according to an exemplary embodiment of the present invention can perform an operation of determining current location and visit store information using, for example, a GPS module, an LTE module, a Wifi module (e.g., beacon reception) In addition, according to various embodiments, the communication unit 110 can measure the degree of change of the current position, and thus the moving speed of the user can be determined based on the measured position. Based on this, the restaurant recommendation apparatus according to the embodiment of the present invention can determine whether the user is using the moving means. On the basis of this, the restaurant recommendation device can be set by expanding the range of the user's location for recommending a restaurant when the user is on board the vehicle (for example, assuming that it is set to search for a restaurant within a radius of 100M when walking, The setting may be changed so as to search for restaurants within a radius of 2 Km). Also, the communication unit 110 can receive restaurant data from a web server.

The storage unit 120 may include, for example, an internal memory or an external memory. The internal memory may be, for example, a volatile memory (e.g., dynamic RAM, SRAM, or synchronous dynamic RAM (SDRAM)), a non-volatile memory (e.g., an OTPROM time programmable ROM (ROM), programmable ROM (PROM), erasable and programmable ROM (EPROM), electrically erasable and programmable ROM (EEPROM), mask ROM, flash ROM, flash memory (such as NAND flash or NOR flash) Or a solid state drive (SSD).

The external memory may be a flash drive such as a compact flash (CF), a secure digital (SD), a micro secure digital (SD), a mini secure digital (SD), an extreme digital A multi-media card (MMC), a memory stick, or the like. The external memory may be functionally and / or physically connected to the electronic device through various interfaces.

The storage unit 120 according to an embodiment of the present invention may store the meal data 121, the restaurant data 122, the user data 123, and the sensitivity data 124. The meal data 121 may refer to information on a meal menu and a restaurant to be used according to a user's date stored on the basis of a user's input. According to various embodiments, the meal data 121 may be automatically collected after a restaurant recommendation operation is performed according to a restaurant recommendation request from a user. For example, if the user terminal stays at the restaurant for a predetermined time or longer after the restaurant recommendation operation, it can be determined that the user has performed the meal at the restaurant, and data on the use of the restaurant can be automatically recorded.

The restaurant data 122 may include a name of a restaurant registered in the web server and location information of the restaurant. In addition, the restaurant data 122 may include information such as a menu of a restaurant, price, price per menu, availability of a meal, availability of a parking space, availability of a room, availability of delivery, And the restaurant data 122 may include a result of classifying the restaurants in a stepwise manner with respect to the feature item. For example, the restaurant data 122 may include information that classifies a restaurant according to a price range, and the price range may be set at various stages. For example, a price range can be set for each stage by one stage (less than 5000 won), a second stage (less than 10,000 won), a third stage (less than 15,000 won), and a fourth stage (less than 20000 won) It can be classified to belong to any one step according to the price range. Accordingly, the restaurant data 122 may include information on the stage in which the price information of a specific restaurant belongs.

Similarly, the restaurant data 122 may include information on the diversity index of the sales menu. The menu diversity index can be set based on the type of menu sold at the restaurant, which can also be classified into several stages. For example, the menu diversity index can be set to indicate that the menu is diversified as the number increases. The menu diversity index can be set to one level (eg, one to three), two levels (four to eight levels) ), 4 levels (14 ~ 18 levels), 5 levels (19 levels or more), and a diversity index (numerical value) corresponding to each level can be applied. According to various embodiments, the menu diversity index may be designated as a number corresponding to each step (the menu diversity index of the first stage may be designated as 1 or 2 and designated as 2), or a separate value corresponding to each step Step is 0.3, step 2 is 1.4, etc.).

The user data 123 may mean data related to a user characteristic directly input by the user. For example, the user data 123 may include personal information such as a user age and sex. The user data 123 may include status information such as whether the vehicle is boarded. In addition, the user data 123 may include disease and taste information related to allergy information, digestive disorders, preference menus, preference materials, and the like.

Sensitivity data 124 may include price sensitivity and redundancy sensitivity. The sensitivity data 124 may be assigned a default value before a separate update operation is performed. According to various embodiments, the sensitivity data 124 may be updated according to the result of an operation performed by the controller 170. [ The sensitivity data 124 may be calculated and stored based on a predetermined amount or more of the user's meal data 121 accumulated. The process of calculating the sensitivity data 124 will be described later in more detail.

The sensitivity data 124 may be referred to when recommending a restaurant meeting the user's needs. For example, the price sensitivity constituting the sensitivity data 124 may be regarded as being highly sensitive to a higher cost as the value is higher, and accordingly, the controller 170 may set the restaurant sensitivity to a value sensitive The recommendation cycle of a restaurant classified into a price range can be set relatively long. Accordingly, the recommendation frequency of the restaurant, which is considered to be relatively expensive for the user to judge, may be lowered.

The sensitivity data 124 may also include redundancy sensitivity, which may indicate sensitivity to revisiting the same restaurant. As a result, it is expected that the longer the time taken to revisit the same restaurant, the higher the number of duplicate sensitivity values. Based on this, the present invention can apply the redundant sensitivity of the user when recommending a restaurant. For example, according to the embodiment of the present invention, the restaurant recommendation apparatus may be set not to recommend a restaurant having a history visited by the user within a predetermined period to the user. The higher the number of users who request restaurant recommendation, the longer the reference period for selecting restaurants to be reserved for recommendation. Or the higher the number of duplicate susceptibility values, the stronger the criteria for selecting a sheltered restaurant. For example, (1) except for the same restaurants, (2) except for the same restaurants and restaurants, (3) excluding the same restaurants, same franchise restaurants and same restaurants as the same restaurants Etc., and the higher the level, the more the range of restaurants to be put on hold can be increased. In addition, various methods can be applied (eg, the higher the overlap sensitivity value, the greater the range of areas where the recommended restaurants are extracted).

The input unit 130 may include, for example, a touch panel, a pen sensor, a key, or an ultrasonic input device. The touch panel 252 may include, for example, The touch panel may further include a control circuit. The touch panel may further include a tactile layer, and the touch panel may include a touch panel, And can provide a tactile response to the user.

The input unit 130 according to the embodiment of the present invention can receive a key input and a touch input by a user. Specifically, the input unit 130 may receive an input to the user data 123 input by the user. Also, the input unit 130 may receive the meal data 121 from the user. The meal data 121 may be collected in such a manner that the user inputs information on the name of the restaurant used on a specific date, the ordered menu, and the like. Also, the meal data 121 may be collected by selecting an answer button for a used restaurant confirmation message (eg, 'Did you use the OO restaurant? Y / N') displayed when the preset criteria are satisfied. The input 130 may receive various user inputs required to collect the meal data 121 from the user.

In addition, the input unit 130 may receive various user inputs (e.g., a restaurant recommendation request button, a specific restaurant selection input in a recommended restaurant list, a button input for a restaurant reservation, etc.) required for using a restaurant recommendation function .

The display unit 140 may include a panel, a hologram device, or a projector. The panel may be embodied, for example, flexible, transparent, or wearable. The panel may comprise a touch panel and one module. The hologram device can display stereoscopic images in the air using the interference of light. The projector can display images by projecting light onto the screen. The screen may be located, for example, inside or outside the electronic device. According to one embodiment, the display unit may further include a control circuit for controlling the panel, the hologram device, or the projector.

The display unit 140 according to an embodiment of the present invention can display information required for a restaurant recommendation operation and a result of a restaurant recommendation operation. In addition, the display unit 140 may display detailed information about a recommended restaurant (e.g., a map image for displaying location information, a navigation operation screen for guiding the restaurant to the restaurant, a seat selection screen for reserving a seat, etc.) Can be displayed.

According to one embodiment, the camera unit 150 may include at least one image sensor (e.g., a front sensor or a rear sensor), a lens, an image signal processor (ISP), or the like And may include a flash (e.g., LED or xenon lamp).

The camera unit 150 according to the embodiment of the present invention may photograph the photograph information for reference to the restaurant visit information. The photographed photographs can be used for reviewing the restaurant and preparing meal data. For example, when a user visits a specific restaurant and photographs a food photograph, menu information ordered by the user based on the photographed time information and image information may be recorded as the meal data 121.

The sensor unit 160 may measure the physical quantity or sense the operating state of the electronic device, for example, and convert the measured or sensed information into an electrical signal. The sensor unit may be, for example, a gesture sensor, a gyro sensor, an air pressure sensor, a magnetic sensor, an acceleration sensor, a grip sensor, a proximity sensor, a color sensor / Humidity sensor, an illuminance sensor, or an ultraviolet (UV) sensor. Additionally or alternatively, the sensor section may be, for example, an E-nose sensor, an electromyography sensor, an electroencephalogram sensor, an electrocardiogram sensor, an infrared ) Sensor, an iris sensor, and / or a fingerprint sensor. The sensor unit may further include a control circuit for controlling at least one or more sensors belonging to the sensor unit.

The sensor unit 160 according to an embodiment of the present invention includes an on / humidity sensor and can determine a current temperature or a user's body temperature. The temperature information measured by the sensor unit 160 may be reflected in the restaurant recommendation operation. The sensor unit 160 may include various biosensors. When the user is a patient suffering from a specific disease, the body condition of the user on the day may be checked according to information such as blood glucose, blood pressure, etc. sensed by the biosensor, and based on this, You can browse restaurants.

The controller 170 may also be referred to as a processor, a controller, a microcontroller, a microprocessor, a microcomputer, or the like. Meanwhile, the control unit may be implemented by hardware or firmware, software, or a combination thereof.

In the case of an implementation by firmware or software, an embodiment of the present invention may be implemented in the form of a module, a procedure, a function, or the like which performs the functions or operations described above. The software codes can be stored in a memory and driven by the control unit. The memory may be located inside or outside the user terminal and the server, and may exchange data with the control unit by various well-known means.

The controller 170 according to the embodiment of the present invention can control to display the information about the restaurant that is determined to match the user's requirement in response to the restaurant recommendation request of the user. The controller 170 may perform classification and processing of information required for the restaurant recommendation function.

Specifically, the controller 170 may include a redundancy sensitivity determination unit 171, a price sensitivity determination unit 172, a restaurant classification unit 173, a restaurant recommendation unit 174, and a travel distance sensitivity determination unit 175. The redundancy sensitivity determination unit 171 and the price sensitivity determination unit 172 may perform a sensitivity determination operation when the predetermined amount of time or more (or a predetermined amount or more) of the meal data 121 is accumulated. The overlap sensitivity determining unit 171 calculates the overlapping sensitivity, the price sensitivity determining unit 172 calculates the price sensitivity, and the moving distance sensitivity determining unit 175 can calculate the moving distance sensitivity.

First, the redundancy sensitivity judging unit 171 collects information on the redundancy element and the redundancy period (for example, the same restaurant re-visit period) existing in the meal data 121 in the reference period and calculates the redundancy sensitivity of the user based on the collected information .

The price sensitivity judging unit 172 can calculate the sensitivity of the user to the meal cost. The price sensitivity judging unit 172 can calculate the price sensitivity by a method of determining the frequency of payment of the meal cost of the reference amount or more from the user's meal data. The payment related information is collected by the received payment confirmation message or when the user inputs the visit information for a specific restaurant, the user can input the price information (for example, 'A restaurant, 1 popular 20,000 won' The price range information corresponding to the price information may be preset).

The movement distance sensitivity determination unit 175 may determine the movement distance sensitivity of the user based on the record of the distance from the detected user position to the restaurant visited by the user at the time when the user requests the restaurant recommendation. The travel distance sensitivity determining unit 175 classifies the travel distance based on the user location at the time of recommendation request and the distance information on the restaurant to be visited based on the meal data for a predetermined period of time (for example, two months) . Then, the movement distance sensitivity determination unit 175 calculates a relatively high level of movement distance sensitivity when the movement distance record of the user is calculated at a predetermined level or more at a predetermined level, The sensitivity of the moving distance of the vehicle is calculated. For example, the first stage (less than 100m), the second stage (less than 300m), the third stage (less than 500m), and the fourth stage (less than 1km). In the case where the step is classified according to the travel distance, such as 5 steps (exceeding 1 km), when the travel distance of the user is measured in only one step, the travel distance sensitivity of the user can be calculated as the highest step. On the other hand, when the moving distance of the user is uniformly measured from the first stage to the fifth stage, the sensitivity of the moving distance of the user can be calculated as the lowest level.

After the movement distance sensitivity is calculated, the restaurant recommendation unit 174 can decrease the distance range value for detecting the restaurant when the user is highly sensitive to the movement distance when displaying the recommended restaurant. For example, if it is assumed that the distance range for detecting the restaurants of the first-stage user, which is the lowest moving distance sensitivity, is set to a radius of 2 km, Can be set to a radius of 100 m.

The restaurant classification unit 173 may collect information on the restaurants, and classify and group the restaurants according to their types. At this time, the restaurant classification unit 173 can perform each user-customized classification operation based on existing information and user data. For example, the restaurant classification unit 173 may specify a price level of each restaurant by comparing the average meal price of the user. Accordingly, the price level may be set differently depending on the user, even if it is the same restaurant.

In addition to the price step, the restaurant classification unit 173 may classify the restaurants according to whether the restaurants are available for meals, whether they can be delivered, whether they can be parked, and the like. Also, the restaurant classification unit 173 can classify restaurants according to a conventional classification method. For example, the restaurant classification unit 173 can classify restaurants according to a conventional classification method such as Korean food, Korean food, Japanese food, Western food, and fast food. On the basis of this, if the restaurant which the user recently used is included in the fast food item, the restaurant classified as the fast food restaurant in the recommendation operation of the restaurant may be excluded from the recommended restaurant or lowered in priority.

Also, the restaurant classification unit 173 can classify the restaurants according to the menu variety. The menu variety can be set based on the number of kinds of menus sold at the restaurant. In addition, the restaurant classification unit 173 may group the restaurants according to the recommended situations. For example, the restaurant classification unit 173 may designate a group of at least one item among a situation such as a meeting, anniversary, date, banquet, and business meeting with respect to some restaurants.

The restaurant recommendation unit 174 can confirm the input of the user's restaurant recommendation request and can perform the matching function based on the reference data (including the meal data, the restaurant data, the user data, the sensitivity data, etc.) have. The restaurant recommendation unit 174 may perform an operation of matching the user demand information extracted from the user's meal data 120 and the user data 123 with the restaurant data 122. The restaurant recommendation unit 174 may change the criterion for extracting the user request information according to the value of the sensitivity data 124.

The restaurant recommendation unit 174 may collect information on the user's current location and recommend restaurants based on the restaurant data 122 based on the predetermined location distance range.

More specifically, when the restaurant recommendation unit 174 confirms the restaurant recommendation request from the user, the restaurant recommendation unit 174 may perform an operation for collecting basic request information to the user. For example, the restaurant recommendation unit 174 may display an input window for collecting personal information (sex, age, etc.) of the user. If such one-time input operation is performed for the user personal information, the re-input operation may be omitted. Also, the restaurant recommendation unit 174 may display an input window for collecting status information among information corresponding to user data. For example, the restaurant recommendation unit 174 may display an input window for receiving information on whether or not the user boarded the vehicle, accompanied by the infant, and the like. In addition, the restaurant recommendation unit 174 may display an input window for collecting user information on health status. For example, the user information on the health state may be allergy information, whether or not the digestive function is impaired, and the like. Also, the restaurant recommendation unit 174 may display an input window for collecting user data related to the favorite food, and the corresponding items may include, for example, a preference material, a preference menu name, and the like.

The restaurant recommendation unit 174 can acquire schedule information of a user in conjunction with a schedule application of the user according to various embodiments. As described above, the restaurant recommendation unit 174 can collect various types of user data and calculate demand information of the user based on the collected user data.

The restaurant recommendation unit 174 may determine whether or not the user's meal data 121 exists and whether the meal data is accumulated above a reference value (for example, a reference period or longer). The restaurant recommendation unit 174 may refer to the restaurant information referring to the restaurant information stored in the meal data 120 if the user's meal data 120 exists, and may determine the elapsed time from the date when the restaurant visited the restaurant. If the predetermined reference period (eg, three days) has not elapsed, the restaurant recommendation unit 174 may exclude the restaurant from the recommendation list or set a lower priority. According to the above-described method, the restaurant recommendation unit 174 can perform a restaurant-based recommendation that the user has not visited recently based on the user's meal data 121.

 In addition, the restaurant recommendation unit 174 may apply the setting information in the restaurant search operation differently according to the sensitivity data. For example, the restaurant recommendation unit 174 may set a recommendation frequency of a restaurant with a price range higher than a reference value to a lower value or set a recommendation priority of a restaurant with a price higher than a reference value to a lower value, as the price sensitivity of the user is higher. Further, the restaurant recommendation unit 174 may set the reference period of the meal data to be longer as the overlap sensitivity is higher.

The restaurant recommendation unit 174 refers to restaurants that do not correspond to the user's request information among restaurants existing within a predetermined distance range based on the current location of the user (for example, a restaurant having a history visited within a predetermined period, Restaurants, etc.) can be extracted and recommended. The restaurant recommendation unit 174 may control to display a restaurant having a large number of items matching the user's request information at the top of the recommendation list.

According to an embodiment of the present invention, a restaurant recommendation apparatus may be implemented on a user terminal as illustrated in FIG. 1, but is not limited thereto. According to various embodiments of the present invention, the restaurant recommendation device may be implemented in a server. In this case, the restaurant recommendation device receives meal data, user data, and the like from the user terminal, performs a restaurant recommendation operation based on the received information, It is possible to provide the restaurant recommendation information to the user by transmitting the information to the user terminal. In this case, the server may perform the functions of the communication unit 110, the storage unit 120, and the controller 170, and other configurations may be included in the user terminal to perform the corresponding operation. In addition, it is also possible that the restaurant recommendation apparatus (system) is implemented in such a manner that the user terminal performs the function of the controller 170 and the data stored in the storage unit 120 is stored in the external server.

2 is a flowchart illustrating a procedure of a restaurant recommendation and a meal data accumulation operation according to an embodiment of the present invention.

The controller 170 according to the embodiment of the present invention may perform the operation 210 of acquiring the reference data for recommending a restaurant when the operation 205 of receiving the restaurant recommendation request is performed as shown in FIG. The reference data required for restaurant recommendation may include meal data 121, restaurant data 122, user data 123, sensitivity data 124, and the like. The control unit 170 may then perform an operation 215 for recommending a restaurant matching the user's request based on the reference data and displaying information about the recommended restaurant.

The control unit 170 may then perform an operation 220 to determine whether the user terminal has stayed at the recommended location for a predetermined period of time or longer. If it is determined that the user terminal has stayed at the location of the restaurant for a preset time or longer, the controller 170 can perform the operation 230 for confirming the meal at the restaurant. The operation 230 may be an operation for displaying an input window composed of, for example, 'Did you eat at the OO restaurant (recommended restaurant name)?' And 'Yes' or 'No' button for responding to the question.

Then, the controller 170 may perform an operation 240 for determining whether a meal is confirmed at the corresponding restaurant. The control unit 170 may be operable to store the meal data at the recommended restaurant (for example, when the user selects the 'Yes' button in the input window displayed in the operation 230) Can be performed.

As a result of the determination in operation 220, if the stay of the user terminal is not confirmed at the recommended restaurant location for a predetermined time or longer, the controller 170 may perform operation 225 to determine whether a predetermined time has elapsed since the recommendation. At this time, if the predetermined time has not been exceeded, the controller 170 may continue to display the information about the recommended restaurant (e.g., the location of the restaurant) to the user. However, if it is determined that the predetermined time has elapsed since the recommendation of the restaurant, the controller 170 can perform the operation 235 for requesting the input of the meal data.

If it is determined that the user does not eat at the recommended restaurant in operation 240, the control unit 170 determines whether or not the user directly inputs the meal data. If the user inputs the meal data directly, And if not, the restaurant recommendation function can be terminated without storing the meal data.

Hereinafter, the operation of calculating the redundancy sensitivity based on the meal data will be described with reference to FIGS. 3 to 4B.

3 is a flowchart illustrating a redundant sensitivity calculation process according to an embodiment of the present invention.

4A and 4B are diagrams illustrating an example of a meal data and a meal data input window according to an embodiment of the present invention.

Referring to FIG. 3, the controller 170 may perform operation 305 to determine whether the accumulated rate of the meal data exceeds a reference value. The accumulation rate of the meal data can be determined by whether the period in which the meal data exists exceeds the reference period or the like. The meal data may be recorded by date as shown in FIG. 4A. Accordingly, the controller 170 can determine whether the period in which the meal data is written exceeds the preset reference period.

The controller 170 may not perform the redundancy sensitivity calculation function if it is determined that the meal data accumulation rate does not exceed the reference value. On the other hand, if it is determined that the meal data accumulation rate exceeds the reference value, the controller 170 may perform 310 operations of extracting redundant elements (e.g., twice) within the reference period. Then, the controller 170 may perform the operation 315 of calculating the redundancy sensitivity based on the data structure of the redundant elements. For example, as a result of data determination for a specific duplicate element in the reference period, the more the number of duplicates for a particular item is, the more the user guesses that there is no sense of resistance to frequently touching the item, and the controller 170 adjusts the redundancy sensitivity .

After operation 315 of calculating the redundancy sensitivity, the controller 170 may perform the operation 320 of updating the redundancy sensitivity.

The meal data can be created and stored as shown in FIG. 4A. Referring to FIG. 4A, the meal data is divided into a case where information on a restaurant to be used is not recorded, and a case where information on the restaurant is recorded. The meal data can be automatically written if the setting conditions are satisfied after the recommendation function operation. However, if the setting conditions do not match after the specific information or the recommendation function operation (for example, the user does not respond to the question of whether or not the recommended restaurant is used), the control unit 170 displays an input button 410 can be displayed.

If the input button 410 is selected, an input window for directly inputting the meal data by the user can be displayed as shown in FIG. 4B. In the input window, there may be an area 420 where a business name of a restaurant can be input, and a drop-down button 421 for displaying a restaurant list so that a restaurant can be selected from the restaurant list. Similarly, a menu name can be input in the input window. When a specific restaurant is selected, the menu name may be displayed in the menu only for menus sold at the restaurant. According to various embodiments, a button area 430 for selecting an event type may exist in the meal data input window instead of the restaurant information. Accordingly, even if the user does not visit the restaurant, the control unit 170 can grasp the diary history information of the user so as not to select a duplicate menu.

Hereinafter, the operation of calculating the price sensitivity will be described with reference to FIGS. 5 and 6. FIG.

5 is a flowchart illustrating a procedure of calculating a price sensitivity according to an embodiment of the present invention.

6 is a graph for explaining the concept of price sensitivity calculation.

Referring to FIG. 5, the controller 170 according to an embodiment of the present invention may perform an operation 510 for determining whether a meal data storage rate exceeds a reference value. Then, the control unit 170 may perform an operation 515 of sorting the meal prices in stages based on the meal data. Then, the controller 170 may calculate the price sensitivity based on the classification result of the price data of the meal data, and perform the operation 520 to update the price sensitivity with the calculated result.

Reference will be made to Fig. 6 for explaining the classification according to the price level. The graphs shown in the graphs 610 and 620 show the results of classifying the records of restaurants used by different users during the same period according to their price ranges. For reference, the price range is increasing from step 1 to step 3.

610 graph and 620 graph show that users of the 610 graph are relatively similar to each other and the users of the 610 graph are significantly lower than those of the first and second stages. In this case, the price sensitivity can be calculated to be a larger value for a user having a large difference in the utilization rate by price step like 620 users.

FIG. 7 is a table showing a method of classifying a restaurant according to an embodiment of the present invention.

As shown in FIG. 7, each restaurant may be categorized by price level, and each restaurant may be coded into alphabetical categories according to the menu category. For example, Korean can be coded as A, Chinese as B, and C as style.

8 is a block diagram showing a configuration of a price sensitivity determining unit according to an embodiment of the present invention.

According to various embodiments of the present invention, the price sensitivity determination unit 172 may include a menu price sensitivity determination unit 172a and a duplicate price sensitivity determination unit 172b.

The per-menu price sensitivity determining unit 172a can determine whether a specific category of a restaurant visited by a user or a specific category of an ordered menu is positively correlated with a price range of the corresponding restaurant based on the user's meal data 121.

For example, the menu-based price sensitivity determining unit 172a may determine that the user has visited the restaurant of the specific category more than a predetermined number of times (for example, five times or more) during a predetermined period (for example, Price sensitivity for each menu can be calculated. In addition, the price sensitivity determination unit 172a for each menu determines an average price level of a restaurant (e.g., a pizza house) of a specific category satisfying the condition for visiting the price more than the predetermined number of times and calculating the price sensitivity for each menu Can be confirmed. Then, the per-menu price sensitivity determining unit 172a can analyze the visit rate to the restaurant of the specific category of the user, and can determine whether or not the visit rate at a price level higher than the average price level is equal to or greater than a preset reference value .

If the average price level of the pizza house is 3 and the user visits the place corresponding to the 4th stage (for example, the price range of 20000 won to 30,000 won per person), the price sensitivity of each menu is judged The unit 172a can set the price sensitivity of the user's pizza house to a low value. This is due to the expectation that the user will not be sensitive to price when paying at the pizzeria and will be willing to pay a relatively high price. Accordingly, when the restaurant recommendation unit 174 provides a list of recommended restaurants, the restaurant price recommendation unit 174 can cancel the restriction of the price level only for the restaurants (for example, pizza houses) of which the price sensitivity for each menu is set. As a result, when a restaurant recommendation is made, users will be able to check out the recommended list of restaurants (pizzas only) and expensive restaurants (ie, all of them can be provided).

Alternatively, in accordance with various embodiments of the present invention, the per-menu price sensitivity determining unit 172a performs a function of determining a preferred price range for a restaurant of a specific classification based on the user's meal data, can do.

For example, when a user visits a restaurant in a specific category (eg, a pizza house), if the rate of visit to the restaurant in the first stage (one popular 5,000 won) is higher than the predetermined reference value %). The menu price sensitivity determiner 172a can determine the price sensitivity of each menu (e.g., pizza house-1) by matching the restaurant price with the corresponding restaurant price. Accordingly, the restaurant recommendation unit 174 may be set to display a restaurant recommendation list in response to a user request, to exclude the pizza items other than the first step from the list, or to be in a subordinate order in display order.

Also, the overlapping price sensitivity determining unit 172b can calculate the price sensitivity according to the number of overlaps within a predetermined period. The overlapping price sensitivity determining unit 172b may analyze the change in the price range of the restaurant or the menu when selecting the restaurant or the same menu of the same classification for a predetermined period (for example, two weeks). Based on the analyzed change trend, the duplicate price sensitivity determiner 172b can calculate the duplicate price sensitivity of the user based on the change rate of the first-time price range, the second-time price range, and the third-time price range. In this case, if there is no correlation between the number of duplicates and the price, the duplicate price sensitivity of the user may not be calculated separately or may be calculated as zero. However, if the number of duplicates is positively correlated with the price, the sensitivity is calculated as a positive number. If the number of duplicates is negatively correlated with the price, the sensitivity can be calculated as a negative number. The larger the variation of the price according to the number of duplication, the greater the absolute value of the sensitivity can be calculated.

The criterion for determining the overlapping price sensitivity in the overlapping price sensitivity determining unit 172b may be determined based on the redundancy sensitivity of the corresponding user. Specifically, the overlapping price sensitivity determining unit 172b may calculate the reference period for calculating the overlapping price sensitivity in proportion to the reference period (for example, one week) of the user's meal data according to the overlapping sensitivity. For example, the overlapping price sensitivity determining unit 172b may set the reference period of data to be referenced for calculating the overlapping price sensitivity to twice (for example, two weeks) the reference data of the user's meal data (for example, one week). This is due to the fact that the users with high sensitivity to duplication can measure the discomfort value for duplication and the price sensitivity depending on the number of duplication, and the possibility of duplication price sensitivity is not likely to be calculated because the frequency of duplication frequency detection is low in the same period do.

In addition, the controller 170 according to the embodiment of the present invention may perform various additional functions related to the restaurant recommendation function. For example, the controller 170 checks whether the user is boarding the vehicle based on the user data, and if the user is determined to be boarding the vehicle, the controller 170 may increase the distance range value applied when searching for restaurants existing within a predetermined range have.

Although the present invention has been described in detail with reference to the above examples, those skilled in the art will be able to make adaptations, changes and modifications to these examples without departing from the scope of the present invention. In other words, in order to achieve the intended effect of the present invention, all the functional blocks shown in the drawings are separately included or all the steps shown in the drawings are not necessarily followed in the order shown, It can be in the range.

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Claims (9)

A restaurant recommendation apparatus comprising:
A communication unit for receiving the restaurant data and information on the current location of the user;
A storage unit for storing the meal data acquired from the user and the restaurant data received through the communication unit; And
A restaurant classifying unit for classifying the restaurant data according to a feature item;
The method according to claim 1, further comprising the step of searching for restaurants recommended by the user according to the reference data and recommending the restaurants according to the user's request, Restaurant recommendation department recommending restaurants other than restaurants;
A redundancy sensitivity judging unit for judging a repetition period for each specific restaurant by the meal data and calculating a redundancy sensitivity for a restaurant selection based on a repetition period for a plurality of restaurants;
And a price sensitivity determining unit for determining the frequency of payment and the average price information to be paid for the meal based on the meal data and calculating the price sensitivity based on the frequency of payment and average price information of the reference value or more. Lt; / RTI >
The restaurant recommendation section
When the recommendation of a restaurant including the above-mentioned redundancy sensitivity is included in the reference data, the reference period of the meal data is set to increase as the redundancy sensitivity of the user increases. As the price sensitivity of the restaurant increases, Increase recommendation cycle of restaurants that are above the reference price range,
The price sensitivity determination unit
A price sensitivity determining unit for determining a price sensitivity of a menu for each category to a lower value as a visit rate to a restaurant of a specific category that is higher than an average price level is higher; And
When visiting a restaurant of the same category within a predetermined period, the duplicate price sensitivity is calculated based on the change in the price range according to the visiting diary, and the reference period of data to be referenced for calculating the duplicate price sensitivity is calculated based on the dietary data And a redundant price sensitivity judging section for setting the redundant price sensitivity judging section in proportion to the reference period of the reference price,
Wherein the reference data includes information on a current position of the user received from the communication unit, the restaurant data, and the meal data acquired from the user.
delete delete delete delete delete ◈ Claim 7 is abandoned due to registration fee. The method according to claim 1,
Wherein the reference data includes user data that is personal information directly input by the user,
Wherein the user data includes information on a user's age, sex, presence of infant, presence of a vehicle, allergy information, digestive disorder, preference menu, and preference material. ◈ Claim 8 is abandoned due to the registration fee. 8. The method of claim 7,
The control unit
Wherein the controller determines whether or not the user boarding the vehicle based on the user data and increases the distance range value applied when searching for a restaurant when the user is confirmed to be on board the vehicle. delete

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