KR102259592B1 - Apparatus and method of providing coffee bean recommendation service based on user preference - Google Patents

Abstract

Disclosed are an apparatus and method for providing a coffee bean recommendation service based on user preferences. A method of providing a coffee bean recommendation service based on user propensity according to an embodiment of the present invention determines a selected bean group based on selection information received from a user terminal, obtains user propensity information from the user terminal, and selects a selected bean group and calculating the match rate of the bean profiles based on the user propensity information, generating a recommended bean group including at least one of a selected bean group and a bean group corresponding to the user propensity information based on the match rate, and providing the recommended bean group to the user terminal It may include transmitting the coffee bean recommendation information including the.

Description

Apparatus and method for providing coffee bean recommendation service based on user preferences {APPARATUS AND METHOD OF PROVIDING COFFEE BEAN RECOMMENDATION SERVICE BASED ON USER PREFERENCE}

The present invention relates to an apparatus and method for providing a coffee bean recommendation service based on user propensity, and more particularly, to an apparatus and method for providing coffee bean recommendation information generated by considering the user's selected beans and user propensity information together. it's about

Coffee beans (brown beans or roasted beans) are roasted coffee beans after harvesting coffee cherries, removing the rind and pulp, and drying the seeds obtained inside.

Coffee is a beverage brewed by grinding coffee beans, and it depends on various factors such as the type of coffee fruit, the characteristics of the cultivation area, the cultivation method, climatic conditions, the harvesting method, the drying method, the blending method, the roasting method, and the grinding method. There is a difference in taste and smell.

As such, because of the numerous factors that determine the taste and aroma of coffee, it is difficult for people to know which coffee is right for them.

An object of the present invention is to provide information on whether a user has selected an appropriate coffee bean suitable for his/her preference based on the beans selected by the user and user preference information and coffee bean recommendation information suitable for the user preference.

An object of the present invention is to provide both a coffee bean item and a recommended roasting strength suitable for the coffee bean item as coffee bean recommendation information suitable for a user's preference.

In a method for providing a coffee bean recommendation service based on user propensity according to an embodiment of the present invention for achieving the above object, the selection information for at least one of the preset reference bean groups is received from the user terminal, and the selection information determining a selected bean group based on , obtaining user propensity information from the user terminal, obtaining a first bean profile corresponding to the selected bean group, and obtaining a second bean profile based on the user propensity information generating a bean profile, calculating a match rate between the first bean profile and the second bean profile, determining a bean group corresponding to a user preference based on the second bean profile, and selecting the selected bean group based on the match rate and generating a recommended bean group including at least one of the user preference-corresponding bean groups, and transmitting coffee bean recommendation information including the recommended bean group to the user terminal.

The first bean profile may include first bean characteristic parameters corresponding to each of the preset coffee bean characteristics, and the second bean profile may include second bean characteristic parameters corresponding to each of the coffee bean characteristics. .

The method of providing a coffee bean recommendation service based on user propensity according to an embodiment of the present invention includes, after transmitting the coffee bean recommendation information, obtaining user feedback information from the user terminal, and the user propensity The method may further include recalculating a second coffee bean profile based on the information and the user feedback information, and recalculating a matching rate between the first coffee bean profile and the second coffee bean profile.

The calculating of the match rate may include calculating the match rate based on a value of a first bean property parameter corresponding to any first property among the coffee bean properties and a value of a second bean property parameter corresponding to the first property. can

The calculating of the coincidence rate may include calculating the coincidence rate based on normalized correlation values for the first bean characteristic parameters and the second bean characteristic parameters.

In the generating of the coffee bean recommendation information, one of the reference bean groups having the highest matching rate with the second bean profile may be determined as the bean group corresponding to the user tendency.

In the step of creating the recommended bean group, a combination ratio between the selected bean group and the user tendency-corresponding bean group is determined based on the match rate, and the combination ratio is applied to the selected bean group and the user tendency-corresponding bean group. The recommended bean group may be created.

The generating of the recommended bean group may include: the first ratio of the user-propensity-corresponding bean group among the combination ratios when the matching ratio is the first matching ratio is a second matching ratio, wherein the matching ratio is higher than the first matching ratio Among the combination ratios, the combination ratio may be determined to be higher than the second ratio of the bean group corresponding to the user tendency.

In the method of providing a coffee bean recommendation service based on user propensity according to an embodiment of the present invention, after generating the recommended bean group, based on the second bean profile and a third bean profile corresponding to the recommended bean group, , calculating a recommended roasting intensity for the recommended group of beans, wherein the coffee bean recommendation information may further include the recommended roasting intensity.

The calculating of the recommended roasting intensity includes determining at least one reference roasting profile among roasting profiles of bean items included in the recommended bean group, and the bean items included in the recommended bean group based on the reference roasting profile. grouping them into at least one roasting group, and calculating the recommended roasting intensity for each roasting group.

In addition, the apparatus for providing a coffee bean recommendation service based on user preferences according to an embodiment of the present invention includes a memory in which at least one program is recorded, and a processor executing the program, wherein the program is preset from the user terminal. receiving selection information on at least one of the reference bean groups, determining a selected bean group based on the selection information, obtaining user tendency information from the user terminal, and a first corresponding to the selected bean group obtaining a bean profile, generating a second bean profile based on the user tendency information, and calculating a matching rate between the first bean profile and the second bean profile; based on the second bean profile determining a bean group corresponding to user propensity, and generating a recommended bean group including at least one of the selected bean group and the user propensity-corresponding bean group based on the match rate, and including the recommended bean group in the user terminal It may include instructions for performing the step of transmitting the coffee bean recommendation information.

The first bean profile may include first bean characteristic parameters corresponding to each of the preset coffee bean characteristics, and the second bean profile may include second bean characteristic parameters corresponding to each of the coffee bean characteristics. .

After transmitting the coffee bean recommendation information, the program recalculates a second bean profile based on the steps of obtaining user feedback information from the user terminal, and the user tendency information and the user feedback information, , may further include instructions for performing the step of re-calculating the matching rate between the first bean profile and the second bean profile.

The calculating of the match rate may include calculating the match rate based on a value of a first bean property parameter corresponding to any first property among the coffee bean properties and a value of a second bean property parameter corresponding to the first property. can

The calculating of the coincidence rate may include calculating the coincidence rate based on normalized correlation values for the first bean characteristic parameters and the second bean characteristic parameters.

In the generating of the coffee bean recommendation information, one of the reference bean groups having the highest matching rate with the second bean profile may be determined as the bean group corresponding to the user tendency.

In the step of creating the recommended bean group, a combination ratio between the selected bean group and the user tendency-corresponding bean group is determined based on the match rate, and the combination ratio is applied to the selected bean group and the user tendency-corresponding bean group. The recommended bean group may be created.

The generating of the recommended bean group may include: the first ratio of the user-propensity-corresponding bean group among the combination ratios when the matching ratio is the first matching ratio is a second matching ratio, wherein the matching ratio is higher than the first matching ratio Among the combination ratios, the combination ratio may be determined to be higher than the second ratio of the bean group corresponding to the user tendency.

After the generating of the recommended bean group, the program calculates a recommended roasting intensity for the recommended bean group based on the second bean profile and a third bean profile corresponding to the recommended bean group It further includes instructions for performing, the coffee bean recommendation information may further include the recommended roasting strength.

The calculating of the recommended roasting intensity includes determining at least one reference roasting profile among roasting profiles of bean items included in the recommended bean group, and the bean items included in the recommended bean group based on the reference roasting profile. grouping them into at least one roasting group, and calculating the recommended roasting intensity for each roasting group.

According to the present invention, it is possible to provide coffee bean recommendation information generated in consideration of the user's selected beans and user tendency information.

According to the present invention, it is possible to provide both a bean item and a roasting strength suitable for the coffee bean item as coffee bean recommendation information suitable for a user's preference.

1 is a block diagram illustrating subjects for providing a coffee bean recommendation service based on user preferences according to an embodiment of the present invention.
2 is an operation flowchart illustrating a method of providing a coffee bean recommendation service based on user preferences according to an embodiment of the present invention.
3 is an operation flowchart illustrating a method of providing a coffee bean recommendation service based on user preferences according to another embodiment of the present invention.
4 is an operation flowchart illustrating a method of providing a coffee bean recommendation service based on user preferences according to another embodiment of the present invention.
5 is an operation flowchart illustrating a method of providing a coffee bean recommendation service based on user preferences according to another embodiment of the present invention.
6 is a view showing a bean profile according to an embodiment of the present invention.
7 is a diagram illustrating a computer system according to an embodiment of the present invention.

The present invention will be described in detail with reference to the accompanying drawings as follows. Here, repeated descriptions, well-known functions that may unnecessarily obscure the subject matter of the present invention, and detailed descriptions of configurations are omitted. Embodiments of the present invention are provided to more completely explain the present invention to those with average knowledge in the art. Accordingly, the shapes and sizes of elements in the drawings may be exaggerated for clearer explanation.

Throughout the specification, when a part "includes" a certain element, it means that other elements may be further included, rather than excluding other elements, unless otherwise stated.

Hereinafter, preferred embodiments according to the present invention will be described in detail with reference to the accompanying drawings.

1 is a block diagram illustrating subjects for providing a coffee bean recommendation service based on user preferences according to an embodiment of the present invention.

Referring to FIG. 1 , subjects for providing a coffee bean recommendation service based on user propensity according to an embodiment of the present invention include a coffee bean recommendation service providing apparatus 110 and a user terminal 120 .

The coffee bean recommendation service providing apparatus 110 refers to a server that provides a coffee bean recommendation service to a user through the user terminal 120 .

The user terminal 120 refers to a communication terminal capable of using a terminal application in a wired/wireless communication environment. For example, the user terminal 120 may be a portable terminal such as a smart phone, the spirit of the present invention is not limited thereto, and a terminal equipped with a terminal application may be borrowed without limitation.

The user terminal 120 may be equipped with a terminal application for receiving data for providing the coffee bean recommendation service from the coffee bean recommendation service providing device 110 and displaying the data to the user.

Here, the terminal application may be an application dedicated to the coffee bean recommendation service or a browser for displaying the coffee bean recommendation service in the form of a web page.

The coffee bean recommendation service providing apparatus 110 and the user terminal 120 may be interconnected through a communication network.

The communication network refers to an access path for transmitting and receiving data between the coffee bean recommendation service providing apparatus 110 and the user terminal 120 . For example, the communication network includes wired networks such as LANs (Local Area Networks), WANs (Wide Area Networks), MANs (Metropolitan Area Networks), and ISDNs (Integrated Service Digital Networks), or wireless LANs such as wireless LANs, CDMA, Bluetooth, and satellite communication. The network may be encompassed, but the scope of the communication network applicable to the present invention is not limited thereto.

2 is an operation flowchart illustrating a method of providing a coffee bean recommendation service based on user preferences according to an embodiment of the present invention.

Referring to FIG. 2 , in the method of providing a coffee bean recommendation service based on user propensity according to an embodiment of the present invention, first, the coffee bean recommendation service providing apparatus 110 selects preset reference bean groups from the user terminal 120 . It is possible to receive selection information on at least one of them, and determine a selection bean group based on the selection information (S210).

Here, the reference bean group may be a reference by grouping at least one coffee bean that can be a reference because of strong taste and aroma among a plurality of coffee beans.

For example, the reference bean group may be preset in the form of reference-beans, "Taste-Mocha", "Fragrance-Egacheffe", "Sweet-El Salvador (SHB)", "Sour-Blue Mountain", "Bitterness (body feeling)-Robusta", "Balance-Jamaica", "Reverberation-Colombia", and "Umami-Mandeling" may be set, and the reference bean group of the present invention is not limited to the above-described example.

Next, the coffee bean recommendation service providing apparatus 110 may acquire user tendency information from the user terminal 120 (S220).

Here, when a questionnaire is provided to the user through the user terminal 120 , the user tendency information may be information including a response value to the corresponding questionnaire.

For example, word sets may be presented to the user through the user terminal 120 as follows, one word may be selected for each word set by the user, and the selected words may be included in the user tendency information.

1, extroverted, introverted

2, sweet, sour, salty, bitter

3, Nose scent, mouth scent

4, strong reverberation, weak reverberation

5, specialized taste and aroma, balanced taste and aroma

6, instant coffee, ground coffee

7, Americano, Latte, Espresso

8, green tea, black tea

9, morning coffee, after-meal coffee

10, drinking coffee on a rainy day, drinking coffee on a sunny day

"Extroverted", "Sour taste", "Nose-smelling", "Strong reverberation", "Specialized taste and aroma", "Bean coffee", "Americano", "Black tea", " When "morning coffee" and "coffee to drink on a sunny day" are selected, corresponding words may be included in the user tendency information, but the information included in the user tendency information of the present invention is not limited to the above-described example.

Next, a first bean profile corresponding to the selected bean group may be obtained, a second bean profile may be generated based on user tendency information, and a matching rate between the first bean profile and the second bean profile may be calculated. (S230).

Here, the bean profile may mean a set of parameters corresponding to each characteristic of coffee beans.

The characteristics of the coffee beans include dry fragrance, wet aroma, brightness, taste and flavor in the mouth, body, finish, and sweetness. ), clean cup, complexity or uniformity may be, and the characteristics of the coffee beans of the present invention are not limited to the above-described examples.

The parameter may be composed of a pair of any one of the characteristics of the coffee beans and an evaluation value within a predetermined range for the characteristic.

For example, the evaluation value may be a value within the range of 0 or more and 10 or less, and the parameter may be configured such as "ground coffee aroma, 8.9" or "extracted coffee aroma, 9.3".

The first bean profile and the second bean profile may include parameters corresponding to each of coffee bean characteristics.

Here, since at least one coffee bean included in the selected bean group has preset coffee bean characteristics, the first bean profile may include the preset coffee bean characteristic values as first bean characteristic parameters.

The second bean profile is calculated based on user tendency information, and may include coffee bean characteristic values expected to be preferred by the user as second bean characteristic parameters.

Here, the coffee bean recommendation service providing apparatus 110 may calculate a matching rate based on the value of the first bean characteristic parameter and the value of the second bean characteristic parameter corresponding to any first characteristic among the coffee bean characteristics.

Here, the coffee bean recommendation service providing device 110 calculates a first comparison value based on the value of the first bean characteristic parameter and the value of the second bean characteristic parameter corresponding to any first characteristic among the coffee bean characteristics, An average of the comparison values calculated for the coffee bean characteristics may be calculated as a matching rate.

In an optional embodiment, the first comparison value may be a ratio of the value of the first bean characteristic parameter and the value of the second bean characteristic parameter.

For example, when the value of the first bean characteristic parameter is 8.9 and the value of the second bean characteristic parameter is 7.9, the comparison value may be 88.76% (7.9/8.9).

In an optional embodiment, the first comparison value may be a ratio corresponding to a value obtained by subtracting a difference value between the value of the first bean characteristic parameter and the value of the second bean characteristic parameter from the maximum value of the evaluation value.

For example, if the value of the first bean property parameter is 8.9 and the value of the second bean property parameter is 7.9, the difference between the value of the first bean property parameter and the value of the second bean property parameter is 1, and the evaluation value is When the maximum value is 10, a value obtained by subtracting the difference value from the maximum value of the evaluation value may be 9, and the comparison value may be 90% (9/10).

In addition, the coffee bean recommendation service providing apparatus 110 performs a normalized correlation with respect to the first bean characteristic parameters included in the first bean profile and second bean characteristic parameters included in the second bean profile. Based on the value, the matching rate can be calculated.

For example, the coincidence rate may be calculated by Equation 1 below.

Here, x[n] may be the first coffee bean profile, y[n] may be the second coffee bean profile, and k may be a value obtained by subtracting 1 from the total number of parameters included in the first or second coffee bean profile.

Next, the coffee bean recommendation service providing device 110 determines a user preference-corresponding bean group based on the second bean profile, and recommends beans including at least one of a selected bean group and a user preference-corresponding bean group based on the match rate A group may be created (S240).

Here, the coffee bean recommendation service providing apparatus 110 may determine one of the reference bean groups that has the highest matching rate with the second bean profile as the user preference-corresponding bean group.

Here, the coffee bean recommendation service providing apparatus 110 determines a combination ratio between the selected bean group and the user tendency-corresponding bean group based on the matching rate, and applies the combination ratio to the selected bean group and the user tendency-corresponding bean group to recommend a bean group can create

Here, the coffee bean recommendation service providing apparatus 110 may create a recommended bean group by using the selected bean group as 100% when the match rate is equal to or greater than the first reference match rate, and when the match rate is less than or equal to the second criterion match rate , it is possible to create a recommended bean group by setting the bean group corresponding to the user tendency as 100%.

For example, the coffee bean recommendation service providing device 110 generates a recommended bean group by using the selected bean group as 100% when the match rate is 80% or more, and sets the user preference corresponding bean group to 100% when the match rate is 50% or less Thus, a recommended bean group can be created.

Here, the coffee bean recommendation service providing device 110 determines that the first ratio of the bean group corresponding to the user preference among the combination ratios when the matching rate is the first matching ratio is the combination ratio when the matching ratio is a second matching ratio that is higher than the first matching ratio The combination ratio may be determined to be higher than the second ratio of the bean group corresponding to the user tendency.

For example, when the match rate is 70%, the coffee bean recommendation service providing device 110 sets the ratio of the bean group corresponding to the user propensity to 40%, and when the match rate is 60%, the ratio of the bean group corresponding to the user propensity is 60%. You can create a recommended bean group.

Next, the coffee bean recommendation service providing apparatus 110 may transmit coffee bean recommendation information including the recommended bean group to the user terminal 120 ( S250 ).

Here, the coffee bean recommendation service providing apparatus 110 may transmit coffee bean recommendation information including guide information corresponding to a matching rate between the first bean profile and the second bean profile.

For example, the coffee bean recommendation service providing apparatus 110 transmits coffee bean recommendation information including guide information guiding that the bean group selected by the user matches the user's tendency when the match rate is equal to or greater than the preset first reference match rate, When the matching rate is less than the preset first reference matching rate, coffee bean recommendation information including guide information guiding that the bean group selected by the user does not match the user's tendency may be transmitted.

When the matching rate is less than the first standard matching rate, the guide information may include information corresponding to the characteristic of at least one coffee bean on which the low matching rate is calculated.

For example, when the user selects "sour taste-Blue Mountain" beans and selects "sweet" in the questionnaire for user propensity survey, among the characteristics of coffee beans, acidity (brightness), taste and flavor in the mouth, A low concordance rate can be calculated in sweetness, etc., and the guide information guides the user's inconsistency in the bean group selected by the user in acidity, taste and flavor in the mouth, and sweetness. may contain information.

Here, the coffee bean recommendation service providing apparatus 110 recommends roasting intensity for the recommended bean group based on the second bean profile corresponding to the user tendency-corresponding bean group and the third bean profile corresponding to the recommended bean group. , and may transmit the recommended bean group and the recommended roasting strength together as the coffee bean recommendation information to the user terminal 120 .

Accordingly, the coffee bean recommendation service providing apparatus 110 may provide both the bean item and the roasting strength suitable for the coffee bean item as coffee bean recommendation information suitable for the user's preference.

A more detailed description of a method for calculating the recommended roasting strength will be described later with reference to FIG. 5 .

Although not shown in FIG. 2 , the user may follow the coffee bean purchase procedure with reference to the coffee bean recommendation information. A more detailed description of the procedure for purchasing coffee beans will be described later with reference to FIG. 3 .

3 is an operation flowchart illustrating a method of providing a coffee bean recommendation service based on user preferences according to another embodiment of the present invention.

In particular, Figure 3 is an operation flowchart showing an embodiment of the user's coffee bean purchase procedure.

Referring to FIG. 3 , in the method of providing a coffee bean recommendation service based on user propensity according to another embodiment of the present invention, the coffee bean recommendation service providing apparatus 110 transmits coffee bean recommendation information to the user terminal 120 ( After S250), selection information on the bean item the user wants to purchase may be received from the user terminal 120, and order information may be generated based on the selection information (S310).

Here, the user may select a coffee bean item to be purchased by referring to the coffee bean recommendation information displayed through the user terminal 120 .

Next, the coffee bean recommendation service providing apparatus 110 receives payment information corresponding to the payment means from the user terminal 120 and performs payment for the bean item (S320), and the delivery destination from the user terminal 120 Delivery information may be generated by receiving address information corresponding to ( S330 ).

As an optional embodiment, payment and generation of delivery information may be performed at the same time, and payment may be performed after generation of delivery information.

4 is an operation flowchart illustrating a method of providing a coffee bean recommendation service based on user preferences according to another embodiment of the present invention.

Referring to FIG. 4 , in the method of providing a coffee bean recommendation service based on user propensity according to another embodiment of the present invention, the coffee bean recommendation service providing apparatus 110 transmits coffee bean recommendation information to the user terminal 120 . After (S250), user feedback information may be obtained from the user terminal (S410).

Here, the coffee bean recommendation service providing apparatus 110 may obtain user feedback information on the bean item purchased by the user.

The user feedback information may include whether the user is satisfied with the purchased bean item or the reason for his dissatisfaction.

Here, when the user feedback information includes information corresponding to the user's "satisfaction", the coffee bean recommendation service providing apparatus 110 receives a regular delivery request for the corresponding bean item from the user through the user terminal 120 . can

Here, the regular delivery may mean that the coffee bean item is delivered at regular intervals for a period specified by the user, and in the case of purchase through regular delivery, a predetermined discount rate may be applied to the price set for the coffee bean item.

Through this, the user can periodically purchase and receive coffee without creating a purchase order for coffee, and can purchase coffee at a more discounted price.

When the user feedback information includes information corresponding to the user's "dissatisfaction", the coffee bean recommendation service providing apparatus 110 provides an additional questionnaire to the user through the user terminal 120, and provides information on the user's additional questionnaire. A response value may be received.

In this case, the coffee bean recommendation service providing apparatus 110 may recalculate the second bean profile based on the user tendency information and the user feedback information, and recalculate the coincidence rate between the first bean profile and the second bean profile (S420) ).

Although not shown in FIG. 4 , the coffee bean recommendation service providing apparatus 110 generates a recommended bean group based on the recalculated match rate, and provides coffee bean recommendation information including the recommended bean group to the user terminal 120 . can provide

5 is an operation flowchart illustrating a method of providing a coffee bean recommendation service based on user preferences according to another embodiment of the present invention.

In particular, FIG. 5 is an operation flowchart illustrating an embodiment of a method for calculating a recommended roasting intensity.

Referring to FIG. 5 , in the method of providing a coffee bean recommendation service based on user propensity according to another embodiment of the present invention, the coffee bean recommendation service providing apparatus 110 creates a recommended bean group (S240) and then recommends At least one reference roasting profile among roasting profiles of bean items included in the bean group may be determined (S510).

Here, the roasting profile may refer to a set of parameters for controlling the roasting process of coffee beans, or information such as temperature and rate of rise (ROR) according to time in the roasting process of coffee beans.

For example, the roasting profile is a set of parameters for controlling the roasting process of beans, and may include an input temperature and a turning point, or the internal temperature of the roaster over time, hot air temperature and ROR values.

The reference roasting profile may be a roasting profile corresponding to a base bean item that occupies the highest ratio of 40% or more among the bean items included in the recommended bean group.

Next, the coffee bean recommendation service providing apparatus 110 may group bean items included in the recommended bean group into at least one roasting group based on the reference roasting profile ( S520 ).

Here, the coffee bean recommendation service providing device 110 sequentially starts with a coffee bean item that occupies a high proportion among the bean items included in the recommended bean group. The bean items may be grouped into the same roasting group as the reference bean item corresponding to the reference roasting profile.

Here, the coffee bean recommendation service providing apparatus 110 is based on the input temperature and the turning point value included in the reference roasting profile, and the roasting profile including the input temperature and the turning point value within the error range as the reference roasting profile can be determined to be within the compatibility range.

Next, the coffee bean recommendation service providing apparatus 110 may calculate a recommended roasting intensity for each roasting group ( S530 ).

Here, the recommended roasting intensity may be expressed as "light (10 minutes)" or "strong (20 minutes)" according to a predetermined standard (eg, roasting time) of a standard roasting profile for each roasting group.

Here, bean items included in the same roasting group may be roasted together after pre-blending, and bean items included in different roasting groups may be blended after roasting.

6 is a view showing a bean profile according to an embodiment of the present invention.

Referring to FIG. 6 , the bean profile according to an embodiment of the present invention may include a bean item name 610 , a characteristic evaluation value 620 , and a total evaluation value 630 .

The characteristic evaluation value 620 is a dry fragrance, a wet aroma, an acidity, a taste in the mouth, a body, and an aftertaste, which are characteristics of coffee beans. finish), sweetness, clean cup, complexity, or uniformity.

As an optional embodiment, a correction value 640 (cupper's correction) according to a coffee differentiator may be added to the total evaluation value 630 in addition to the characteristic evaluation value 620 .

7 is a diagram illustrating a computer system according to an embodiment of the present invention.

The coffee bean recommendation service providing apparatus 110 according to an embodiment of the present invention may be implemented in the computer system 1100 such as a computer-readable recording medium.

Referring to FIG. 7 , the computer system 1100 includes one or more processors 1110 , a memory 1130 , a user interface input device 1140 , a user interface output device 1150 , and storage that communicate with each other via a bus 1120 . (1160) may be included. Further, the computer system 1100 may further include a network interface 1170 connected to the network 1180. The processor 1110 may be a central processing unit or a semiconductor device that executes processing instructions stored in the memory 1130 or the storage 1160. The memory 1130 and the storage 1160 may be various types of volatile or nonvolatile storage media. For example, the memory may include a ROM 1131 or a RAM 1132.

Specific implementations described in the present invention are examples, and do not limit the scope of the present invention in any way. For brevity of the specification, descriptions of conventional electronic configurations, control systems, software, and other functional aspects of the systems may be omitted. In addition, the connection or connection members of the lines between the components shown in the drawings exemplarily represent functional connections and/or physical or circuit connections. It may be referred to as a connection, or circuit connections. In addition, if there is no specific mention such as “essential” or “important”, it may not be a necessary component for the application of the present invention.

Therefore, the spirit of the present invention should not be limited to the above-described embodiments, and the scope of the spirit of the present invention is not limited to the scope of the scope of the present invention. will be said to belong to

110: coffee bean recommendation service providing device
120: user terminal
1100: computer system 1110: processor
1120: bus 1130: memory
1131: ROM 1132: RAM
1140: user interface input device
1150: user interface output device
1160: storage 1170: network interface

Claims (5)

receiving selection information on at least one of preset reference bean groups from a user terminal, and determining a selection bean group based on the selection information;
obtaining user tendency information from the user terminal;
obtaining a first bean profile corresponding to the selected bean group, generating a second bean profile based on the user tendency information, and calculating a matching rate between the first bean profile and the second bean profile; ;
determining a bean group corresponding to user propensity based on the second bean profile and generating a recommended bean group including at least one of the selected bean group and the bean group corresponding to user propensity based on the match rate; and
Transmitting coffee bean recommendation information including the recommended bean group to the user terminal,
The first bean profile includes first bean characteristic parameters corresponding to each of the preset coffee bean characteristics, and the second bean profile includes second bean characteristic parameters corresponding to each of the coffee bean characteristics,
The calculating of the match rate may include calculating the match rate based on a value of a first bean property parameter corresponding to any first property among the coffee bean properties and a value of a second bean property parameter corresponding to the first property, ,
The calculating of the matching rate includes calculating the matching rate based on normalized correlation values for the first bean characteristic parameters and the second bean characteristic parameters,
In the step of creating the recommended bean group, the combination ratio between the selected bean group and the user tendency corresponding bean group is determined based on the matching rate, and the combination ratio is applied to the selected bean group and the user tendency corresponding bean group. creating the recommended bean group,
The generating of the recommended bean group is performed when the first ratio of the user-propensity-corresponding bean group among the combination ratios when the matching ratio is the first matching ratio is a second matching ratio, the matching ratio being higher than the first matching ratio A method of providing a coffee bean recommendation service based on a user preference, wherein the combination ratio is determined to be higher than a second ratio of the user preference-corresponding bean group among the combination ratios.
The method of claim 1,
After the step of creating the recommended bean group,
The method further comprising: calculating a recommended roasting intensity for the recommended bean group based on the second bean profile and a third bean profile corresponding to the recommended bean group;
The coffee bean recommendation information further includes the recommended roasting strength,
Calculating the recommended roasting strength comprises:
determining at least one reference roasting profile from among roasting profiles of bean items included in the recommended bean group;
grouping the bean items included in the recommended bean group into at least one roasting group based on the reference roasting profile; and
Calculating the recommended roasting intensity for each roasting group; a method of providing a coffee bean recommendation service based on user tendencies, comprising:
The method of claim 2,
After transmitting the coffee bean recommendation information,
obtaining user feedback information from the user terminal; and
recalculating a second coffee bean profile based on the user tendency information and the user feedback information, and recalculating a matching rate between the first coffee bean profile and the second coffee bean profile
Further comprising, a method of providing a coffee bean recommendation service based on user preferences.
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