Recipe recommendation system, recipe recommendation method and program

Abstract

This food recommendation system includes: an acquisition unit that acquires in advance event data including identification information of a food eaten by a user, food type information including taste sensation information for classifying the food by taste sensation, and mealtime information; an extraction unit that calculates, by a time-series association analysis, a characteristic index including at least one of a support degree, a reliability degree, and a lift value related to the dietary habit of the user on the basis of the event data, and extracts a specific characteristic index corresponding to specific food type information input by the user; and a recommendation unit that outputs a food candidate that is proposed to the user on the basis of the specific characteristic index.

Description

[Technical Field]

[0001] The present disclosure relates to a dish recommendation system, a dish recommendation method, and a program. [Background technology]

[0002] Conventionally, there have been proposed dish recommendation systems that suggest dishes based on a user's meal history. For example, Patent Literature 1 discloses a dish suggestion device that suggests the next dish to be eaten using eating patterns, preferences, meal amounts, etc. based on the user's past meal history. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-124701 Summary of the Invention [Problem to be solved by the invention]

[0004] However, the device of Patent Document 1 automatically suggests dishes without confirming the user's desires, so there is a risk that dishes different from the ones the user wants to eat may be suggested.

[0005] The present disclosure aims to provide a dish recommendation system that suggests appropriate dishes to a user. [Means for solving the problem]

[0006] The dish recommendation system according to the present disclosure includes an acquisition unit that acquires in advance event data including identification information of dishes eaten by a user, dish type information including taste sensation information that classifies dishes by taste sensation, and mealtime information; an extraction unit that calculates characteristic indicators including at least one of support, confidence, and lift value related to the user's eating habits based on the event data using time-series association analysis, and extracts specific characteristic indicators corresponding to specific dish type information input by the user; and a recommendation unit that outputs dish candidates to be suggested to the user based on the specific characteristic indicators.

[0007] The dish recommendation method according to the present disclosure includes: The computer The method comprises: acquiring in advance event data including identification information of the dishes eaten by the user, dish type information including taste sensation information that classifies the dishes by taste sensation, and mealtime information; calculating, through time series association analysis, characteristic indices including at least one of support, confidence, and lift value related to the user's eating habits based on the event data; extracting specific characteristic indices according to the specific dish type information input by the user; and outputting candidate dishes to be suggested to the user based on the specific characteristic indices.

[0008] The program disclosed herein causes a computer to perform the following steps: acquire in advance event data including identification information of the dishes eaten by a user, dish type information including taste sensation information that classifies dishes by taste sensation, and mealtime information; calculate characteristic indicators including at least one of support, confidence, and lift value related to the user's eating habits based on the event data using time-series association analysis; extract specific characteristic indicators corresponding to specific dish type information input by the user; and output candidate dishes to be suggested to the user based on the specific characteristic indicators. [Effects of the Invention]

[0009] According to the present disclosure, it is possible to suggest appropriate dishes to the user. [Brief explanation of the drawings]

[0010] [Figure 1] FIG. 1 is a diagram showing an overview of one embodiment of the present disclosure. [Figure 2] FIG. 2 is a block diagram showing the functional configuration of a dish recommendation system according to an embodiment of the present disclosure. [Figure 3] FIG. 3 is a block diagram showing a hardware configuration of a dish recommendation system according to an embodiment of the present disclosure. [Figure 4] FIG. 4 is a flowchart illustrating the operation of one embodiment of the present disclosure. [Figure 5] FIG. 5 is a diagram showing dish type information determined based on ingredient information. [Figure 6] FIG. 6 is a diagram showing event data. [Figure 7] FIG. 7 shows analysis data obtained by time-series association analysis of user event data. [Figure 8] FIG. 8 is a diagram showing analytical data extracted based on specific cuisine type information. [Figure 9] FIG. 9 is a diagram showing how time-series association analysis is performed on event data of other users. [Figure 10] FIG. 10 is a diagram showing comparison data comparing the characteristic indexes of a user with other users. [Figure 11] FIG. 11 is a diagram showing proposal data including recipe candidates output from the recommendation unit. [Figure 12] FIG. 12 is a diagram showing the eating habit data output from the recommendation unit. DETAILED DESCRIPTION OF THE INVENTION

[0011] Hereinafter, embodiments of the present disclosure will be described with reference to the drawings.

[0012] An overview of the present disclosure will be described. For example, as shown in FIG. 1, when a user's terminal 1 receives meal data (e.g., images of dishes) of the user's meals, the terminal 1 sequentially transmits the meal data to a server 2. Here, the server 2 is assumed to have the dish recommendation system of the present disclosure. When the dish recommendation system of the server 2 receives the meal data from the terminal 1, the system acquires event data based on the meal data, including identification information (e.g., name) of the dish eaten by the user, dish type information (e.g., light), and mealtime information (e.g., date), and analyzes the characteristics of the user's eating habits based on the event data. In this case, analyzing the user's eating habits based only on the dish identification information and mealtime information would result in analyzing the eating habits of the dishes themselves, making it difficult to analyze eating habits based on dish type, such as "I want to eat something rich once every three days." Therefore, when a specific dish type information (e.g., light) that the user wants to eat is input to the terminal 1, the terminal 1 transmits the specific dish type information to the server 2. When the dish recommendation system of the server 2 receives the specific dish type information from the terminal 1, it analyzes the characteristics of the user's eating habits based on the event data including the dish type information and extracts characteristic indicators of the eating habits corresponding to the specific dish type information received from the terminal 1. The dish recommendation system then outputs dish candidates to be suggested to the user based on the extracted characteristic indicators and transmits the dish candidates to the terminal 1. As a result, the terminal 1 displays the dish candidates corresponding to the specific dish type information on the display unit.

[0013] Next, the configuration of the dish recommendation system according to the present disclosure will be described in detail. As shown in Fig. 2, the dish recommendation system 3 includes an acquisition unit 4, a storage unit 5, an extraction unit 6, and a recommendation unit 7. The acquisition unit 4 is connected to the storage unit 5, which is connected to the recommendation unit 7 via the extraction unit 6.

[0014] The acquisition unit 4 acquires event data including identification information of a dish eaten by a user, dish type information, and mealtime information. Here, the dish may include a single dish (e.g., hamburger steak) or a menu indicating multiple dishes (e.g., rice and hamburger steak). The identification information of a dish may include, for example, the name of the dish or an identification number. The dish type information is information for classifying dishes by type and may include, for example, taste sensation information for classifying dishes by taste sensation. Here, the taste sensation information may include, for example, texture information indicating a physical taste sensation, or taste sensation information indicating a chemical taste sensation. Specifically, the taste sensation information may include information indicating onomatopoeia expressing the taste sensation, i.e., information indicating onomatopoeic words or mimetic words. Examples of onomatopoeia include refreshing, rich, firm, crunchy, fluffy, crispy, crunchy, and creamy. Here, the information indicating onomatopoeia may be composed of, for example, at least a partial character string of an onomatopoeia. Furthermore, mealtime information is information related to the time of a meal, and examples thereof include the time, date, and time period (for example, breakfast, lunch, or dinner) of the meal.

[0015] For example, the acquisition unit 4 may receive meal data entered by the user from the terminal 1. Here, the meal data is data related to the dishes eaten by the user, and may include, for example, images (e.g., photos) of the dishes, menus, a questionnaire about the dishes (e.g., food preference information, avoided ingredient information, user attribute information, etc.), dish type information, etc. The food preference information is information related to the dishes preferred by the user, and may include, for example, the names of dishes or ingredients preferred by the user. The avoided ingredient information is information related to ingredients that the user avoids using in cooking, and may include, for example, ingredients that the user dislikes or ingredients that cause allergic reactions. The user attribute information may include gender, whether the user is on a diet, whether the user has a chronic illness (e.g., high blood pressure), etc. Upon receiving the meal data, the acquisition unit 4 creates event data based on the meal data, in which identification information of the dishes eaten by the user, dish type information, and mealtime information are associated with each other.

[0016] The storage unit 5 stores the event data acquired by the acquisition unit 4. That is, the storage unit 5 stores event data in which identification information of the dish eaten by the user, dish type information, and mealtime information are associated with each other.

[0017] The extraction unit 6 has an analysis processing unit 8 and an extraction processing unit 9. The analysis processing unit 8 is connected to the extraction processing unit 9. Furthermore, the storage unit 5 is connected to the analysis processing unit 8, and the extraction processing unit 9 is connected to the recommendation unit 7. Furthermore, the acquisition unit 4 is connected to the extraction processing unit 9.

[0018] The analysis processing unit 8 analyzes the characteristics of the user's eating habits based on the event data stored in the memory unit 5 using time-series association analysis. Here, the characteristics of the eating habits indicate the user's tendency (e.g., rules or conditions) to select dishes over multiple meals, and may indicate, for example, a tendency to select "light" dishes followed by "heavy" dishes over three meals. For example, the analysis processing unit 8 calculates a characteristic index including at least one of support, confidence, and lift value related to the user's eating habits.

[0019] The extraction processing unit 9 extracts characteristic indicators of eating habits corresponding to specific cuisine type information input by the user, based on the characteristics of the user's eating habits analyzed by the analysis processing unit 8. For example, when the acquisition unit 4 receives the specific cuisine type information input by the user to the terminal 1 from the terminal 1, the acquisition unit 4 may output the specific cuisine type information to the extraction processing unit 9. For example, when the user inputs specific taste sensory information "refreshing" as the specific cuisine type information, the extraction processing unit 9 may calculate characteristic indicators indicating eating habits corresponding to "refreshing."

[0020] The recommendation unit 7 outputs dish candidates to be suggested to the user based on the characteristic indexes acquired by the extraction processing unit 9. In this case, the recommendation unit 7 may output dish candidates based on event data that meets at least one of the following conditions: a support level equal to or greater than a predetermined threshold, a confidence level equal to or greater than a predetermined threshold, and a lift value equal to or greater than a predetermined threshold. The recommendation unit 7 may also analyze the characteristics of the eating habits of other users based on event data including identification information of dishes eaten by the other users, dish type information, and mealtime information. The recommendation unit 7 may then search for dish candidates to be suggested to the user based on the user's characteristic indexes calculated by the extraction processing unit 9 and characteristic indexes of other users.

[0021] FIG. 3 shows the hardware configuration of the dish recommendation system 3.

[0022] The dish recommendation system 3 includes a storage device 11, a processor 12, a user interface (UI) device 13, and a communication device 14, which are interconnected via a bus B.

[0023] In addition, the programs or instructions that realize the various functions and processes described below in the dish recommendation system 3 may be downloaded from any external device via a network, or may be provided from a removable storage medium such as a CD-ROM (Compact Disk-Read Only Memory) or flash memory.

[0024] Storage device 11 may be implemented by one or more non-transitory storage media, such as random access memory, flash memory, or a hard disk drive, and stores installed programs or instructions as well as files, data, etc. used in the execution of the programs or instructions.

[0025] The processor 12 may be realized by one or more central processing units (CPUs), which may be configured with one or more processor cores, graphics processing units (GPUs), processing circuitry, etc. The processor 12 executes various functions and processes of the dish recommendation system 3, which will be described later, in accordance with programs, instructions, data such as parameters required to execute the programs or instructions, etc., stored in the storage device 11.

[0026] The UI device 13 may be composed of input devices such as a keyboard, mouse, camera, microphone, etc., output devices such as a display, speaker, headset, printer, etc., and input / output devices such as a smartphone, tablet, touch panel, etc., and realizes an interface between the administrator and the dish recommendation system 3. For example, the administrator may operate the dish recommendation system 3 by operating a GUI (Graphical User Interface) displayed on a display or touch panel using a keyboard, mouse, etc.

[0027] It should be noted that the above-described hardware configuration is merely an example, and the dish recommendation system 3 according to the present disclosure may be realized by any other appropriate hardware configuration.

[0028] Next, the operation of this embodiment will be described with reference to the flowchart shown in FIG.

[0029] First, in step S1, the acquisition unit 4 of the dish recommendation system 3 shown in FIG. 2 acquires event data including identification information of the dish eaten by the user, dish type information, and meal time information. For example, the acquisition unit 4 may acquire the event data based on the user's meal data. For example, when the user inputs meal data, such as photographed images of the eaten dish, into terminal 1, terminal 1 sequentially transmits the meal data to server 2. Then, when the acquisition unit 4 of the dish recommendation system 3 in server 2 receives the user's meal data from terminal 1, it acquires identification information of the dish eaten by the user, dish type information, and meal time information based on the meal data. At this time, the acquisition unit 4 may further acquire ingredient information of the dish eaten by the user based on the meal data. Note that the ingredient information is information about ingredients used in the dish and may include information such as toppings (e.g., radish, carrot, etc.), seasonings (e.g., soy sauce, miso, etc.), and the amount of ingredients.

[0030] For example, the acquisition unit 4 may recognize the name (identification information) and ingredient information of the dish eaten by the user based on the photographed image of the dish received as meal data, and may estimate taste sensory information (dish type information) that classifies the dish by taste based on the ingredient information. For example, as shown in FIG. 5, even if the same dish is recognized as simmered chicken 1 to simmered chicken 5, the acquisition unit 4 may recognize the difference in the ingredient information used in the simmered chicken 1 to 5 and estimate the taste sensory information for each dish based on the ingredient information. For example, if the acquisition unit 4 recognizes the ingredient information of simmered chicken 1 as "yam" based on the photographed image of simmered chicken 1, it may estimate the taste sensory information as "fluffy" based on the texture of the yam. Note that although "daikon" is indicated as the ingredient information for both simmered chicken 4 and simmered chicken 5, they may be classified as "refreshing" and "mellow" based on other ingredients, respectively. In other words, the taste sensory information may be estimated comprehensively from the information on multiple ingredients used in the dishes. The acquisition unit 4 may also acquire mealtime information based on the image capture time attached to the meal data received from the terminal 1, for example.

[0031] In the above embodiment, the acquisition unit 4 acquires the identification information, dish type information, meal time information, and ingredient information of the dish eaten by the user based on the user's meal data, but the acquisition unit 4 may acquire such information as long as it can acquire such information. For example, the acquisition unit 4 may acquire the identification information, dish type information, meal time information, and ingredient information of the dish eaten by the user directly from the terminal 1. That is, the terminal 1 may accept input of the identification information, dish type information, meal time information, and ingredient information of the dish eaten by the user from the user, and transmit the dish identification information, dish type information, meal time information, and ingredient information input by the user to the server 2.

[0032] Next, in step S2, the acquisition unit 4 stores the acquired event data in the storage unit 5. At this time, the acquisition unit 4 may store in the storage unit 5 event data that associates at least one of the dish and ingredient information eaten by the user with dish type information. For example, as shown in FIG. 6, the acquisition unit 4 may store in the storage unit 5 event data 15 that associates the name (identification information) of the dish eaten by the user, taste sensation information (dish type information), and ingredient information for each date of mealtime information. In this way, the acquisition unit 4 updates the event data 15 every time it receives the user's meal data, and builds event data 15 of dishes eaten by the user in the past.

[0033] Meanwhile, terminal 1 may receive from the user a request for a proposal of a dish that the user is about to make. At this time, terminal 1 may also receive input of specific dish type information. For example, terminal 1 may receive specific taste sensation information that classifies dishes by taste sensation. At this time, terminal 1 may receive information indicating onomatopoeia that expresses the taste sensation as taste sensation information. When specific dish type information is input by the user, terminal 1 transmits the specific dish type information to server 2. Here, it is assumed that the user has input the onomatopoeia "refreshing" to terminal 1 as the specific taste sensation information.

[0034] In this way, the cuisine type information includes taste sensory information that classifies cuisines by taste sensation. For example, if cuisines are classified based on a food culture such as "French cuisine," there are various types of cuisines (for example, "light" cuisine and "rich" cuisine), making it difficult for the user to properly indicate the cuisine they desire. Therefore, by specifying the type of cuisine using taste sensory information, the user can properly specify the range of cuisines they desire. Furthermore, the taste sensory information includes information indicating onomatopoeia that express the sensation of taste. This allows the user to easily specify the taste sensory information.

[0035] When the dish recommendation system 3 of the server 2 receives specific taste sensory information "refreshing" input by the user from the terminal 1, the extraction unit 6 analyzes the characteristics of the user's eating habits based on the event data 15 stored in the memory unit 5. For example, in step S3, the analysis processing unit 8 of the extraction unit 6 extracts event data 15 for a predetermined period (e.g., 14 days) from the memory unit 5. Then, in step S4, the analysis processing unit 8 may calculate a characteristic index indicating the user's eating habits by performing time-series association analysis on the extracted event data 15. Examples of the characteristic index include support, confidence, and lift value for a combination of events. For example, the analysis processing unit 8 may calculate a characteristic index including at least one of support, confidence, and lift value. Note that the analysis processing unit 8 may automatically calculate the characteristic index based on the event data 15 stored in the memory unit 5 without receiving specific taste sensory information from the user.

[0036] Specifically, as shown in FIG. 6, the analysis processing unit 8 recognizes the names of dishes, taste sensory information, and ingredient information for each column (e.g., each day) of event data 15 for 14 days (a predetermined period) as event data 15a, event data 15b, and so on, and may calculate characteristic indexes based on combinations of two event data included in a preset analysis period (e.g., three days). For example, assuming April 10th as the reference date, the analysis processing unit 8 sets the analysis period to the three days of April 10th, April 9th ​​(one day before), and April 8th (two days before), and calculates characteristic indexes for combinations of two event data included in the analysis period. For example, as shown in FIG. 7, the analysis processing unit 8 calculates characteristic indexes for all combinations of event data 15z1 corresponding to event A before the reference date (an event on April 9th ​​or April 8th) and event data 15z2 corresponding to event B on the reference date (an event on April 10th). Next, the analysis processing unit 8 shifts the reference date by one day to April 9, sets the next analysis period to the three days of April 9, April 8 (one day before), and April 7 (two days before), and similarly calculates characteristic indicators based on all combinations of two event data 15z1 and 15z2 included in the analysis period. In this way, the analysis processing unit 8 shifts the analysis period sequentially within the 14-day (predetermined period) period and calculates characteristic indicators based on combinations of two event data 15z1 and 15z2 included in each analysis period. Then, the analysis processing unit 8 generates analysis data 16 for 14 days (predetermined period) that includes event data 15z1 of event A, event data 15z2 of event B, and characteristic indicators.

[0037] Here, the support indicates, for example, the frequency of combinations of event data 15z1 of event A and event data 15z2 of event B, and may be calculated using the following formula (1). The confidence indicates, for example, the probability of eating a dish of event A one or two days before eating a dish of event B, and may be calculated using the following formula (2). The lift value indicates, for example, the degree of increase in the probability of eating a dish of event A one or two days before eating a dish of event B, and may be calculated based on the following formula (3). The total number of data items may be calculated, for example, by multiplying the number of dish identification information items by the number of dish type information items by the number of ingredient information items by the number of analysis periods (e.g., 3 days).

[0038] Support = Number of data showing the same combination of event data 15z1 of event A and event data 15z2 of event B ÷ Total number of data (1) Confidence = Number of data showing the same combination of event data 15z1 of event A and event data 15z2 of event B ÷ Number of data including event data 15z2 of event B (2) Lift value = number of data showing the same combination of event data 15z1 of event A and event data 15z2 of event B ÷ number of data including event data 15z2 of event B ÷ number of data including event data 15z1 of event A ÷ total number of data (3)

[0039] In this case, the analysis processing unit 8 may search the analysis data 16 based on the chronological order of the event data 15a, 15b, ... in the event data 15, and may filter out data in an order that is not included in the event data 15. For example, if event data 15z2 (corresponding to event B) is recorded on the day after event data 15z1 (corresponding to event A) in the event data 15, the analysis processing unit 8 may filter out data in the analysis data 16 in which event A is event data 15z2 and event B is event data 15z1 (data in the reverse order from event data 15). In this way, the event data 15 is subjected to association analysis in chronological order.

[0040] In this way, the analysis processing unit 8 analyzes the characteristics of the eating habits of the user P by performing time-series association analysis on the event data 15, and outputs characteristic indices such as support, confidence, and lift value. This allows the analysis processing unit 8 to accurately indicate the eating habits of the user P with characteristic indices.

[0041] The event data 15 also includes information about ingredients of the food eaten by the user P. This allows the analysis processing unit 8 to more accurately analyze the characteristics of the eating habits of the user P based on the differences in the ingredient information.

[0042] The event data 15 is configured by associating at least one of the food and ingredient information eaten by the user P with the food type information. This allows the analysis processing unit 8 to easily analyze the characteristics of the user P's eating habits.

[0043] The dish type information also includes taste sensory information that classifies dishes by taste sensation. The analysis processing unit 8 performs analysis based on taste sensory information, which has a significant influence on the user's eating habits, and therefore can accurately analyze the characteristics of user P's eating habits. The taste sensory information also includes information indicating onomatopoeia that express the taste sensation. The analysis processing unit 8 performs analysis based on onomatopoeia that accurately expresses taste sensory information, and therefore can more accurately analyze the characteristics of user P's eating habits.

[0044] Next, the analysis processing unit 8 outputs the generated analysis data 16 to the extraction processing unit 9. Note that in the above embodiment, the analysis processing unit 8 performs time-series association analysis on the event data 15 after receiving the specific cuisine type information entered by the user P from the terminal 1, but this is not limiting as long as time-series association analysis can be performed. For example, the analysis processing unit 8 may store analysis data 16 obtained by performing time-series association analysis on the event data 15 in the storage unit in advance. Then, when the specific cuisine type information entered by the user P is received from the terminal 1, the analysis processing unit 8 may output the analysis data 16 stored in the storage unit to the extraction processing unit 9.

[0045] The extraction processing unit 9 receives the analytical data 16 from the extraction processing unit 9. The extraction processing unit 9 also receives the specific taste sensory information "refreshing" that the user P inputs to the terminal 1 from the acquisition unit 4. Then, in step S5, the extraction processing unit 9 filters the analytical data 16 based on the specific taste sensory information "refreshing."

[0046] For example, as shown in FIG. 8, the extraction processing unit 9 may extract analytical data 16a in which the cuisine type information of event B indicates specific taste sensory information 29 "refreshing" from the analytical data 16. The variable names indicate combinations of event data 15z1 of event A and event data 15z2 of event B. Here, the variable names X, Y, and Z indicate the same combination of event data 15z1 of event A and event data 15z2 of event B, separated into support (X1, X2...), confidence (Y1,...), and lift value (Z1,...). The variable values ​​are normalized values ​​of the support, confidence, and lift value of the characteristic indicators, respectively.

[0047] In this way, the extraction processing unit 9 extracts characteristic indicators 17 indicating eating habits corresponding to specific taste sensory information 29 input by the user, based on the analysis data 16 indicating the characteristics of the user's eating habits.

[0048] The extraction processing unit 9 may further extract characteristic indicators 17 using at least one of user P's preference information and avoided ingredient information. For example, the extraction processing unit 9 may search the analysis data 16a based on user P's preference information and preferentially extract event data including characteristic indicators 17 in which dishes or ingredients corresponding to the preference information are registered. The extraction processing unit 9 may also search the analysis data 16a based on user P's avoided ingredient information and extract event data including characteristic indicators 17 in which avoided ingredient information is not registered. In other words, the extraction processing unit 9 may delete event data in which avoided ingredient information is registered from the analysis data 16a. The preference information and avoided ingredient information may be calculated based on user P's dietary data or may be set by user P. This allows the extraction processing unit 9 to appropriately extract characteristic indicators 17 in accordance with user P's preference information or avoided ingredient information.

[0049] The extraction processing unit 9 may also extract the characteristic index 17 by further using attribute information of the user P. For example, if the attribute information of the user P is set as being on a diet, the extraction processing unit 9 may extract event data of dishes with calories equal to or less than a predetermined value. The attribute information may be set by the user P.

[0050] The extraction processing unit 9 may also extract characteristic indicators 17 by further using recipe browsing history. For example, when user P searches the web for a cooking recipe, terminal 1 may transmit the recipe browsing information to server 2. The extraction processing unit 9 may calculate preference information of user P based on the recipe browsing history transmitted from terminal 1. Then, the extraction processing unit 9 may preferentially extract event data including characteristic indicators 17 in which dishes or ingredients corresponding to the calculated preference information of user P are registered.

[0051] Next, the extraction processing unit 9 outputs analytical data 16a including the extracted characteristic indicators 17 to the recommendation unit 7. When the recommendation unit 7 receives the analytical data 16a, it compares the user's analytical data 16a with analytical data obtained by performing a time-series association analysis on the characteristics of the eating habits of other users in step S6. Here, the recommendation unit 7 may store in advance in the storage unit analytical data obtained by analyzing the characteristics of the eating habits of other users, as well as the user, based on event data including identification information of dishes eaten by the other users, dish type information, and meal time information.

[0052] For example, as shown in Fig. 9, the recommendation unit 7 may acquire event data 18a, 18b, etc. of multiple other users P1, P2, etc. in advance and analyze the characteristics of the eating habits of each of the other users based on the event data 18a, 18b, etc. In this case, the recommendation unit 7 may calculate a characteristic index 19 indicating the eating habits of each of the other users by performing a time-series association analysis on the event data 18a, 18b, etc. Then, the recommendation unit 7 may generate analysis data 21a, 21b, etc., including event data 20z1 of event A, event data 20z2 of event B, and the characteristic index 19.

[0053] If the variable names do not match among the other users P1, P2, ... (there is a missing variable name for a specific user), the recommendation unit 7 may complete the missing variable name for the specific user based on the average value of the variable values ​​of the other users P1, P2, ... for which the variable name exists. The recommendation unit 7 may also acquire the preference information, avoided ingredient information, attribute information, or recipe browsing history of user P from the extraction processing unit 9, and extract the characteristic index 19 using this information in the same way as the extraction processing unit 9. For example, between steps S6 and S7, the recommendation unit 7 may filter the analysis data 21a, 21b, ... based on the preference information, avoided ingredient information, attribute information, or recipe browsing history of user P.

[0054] Here, analytical data 21a may be generated by type of cuisine type information of event B. For example, analytical data 21a of another user P1 may be generated by type of taste sensory information, such as analytical data 21a1 in which the taste sensory information of event B is indicated as "refreshing," analytical data 21a2 in which the taste sensory information of event B is indicated as "firm," analytical data 21a3 in which the taste sensory information of event B is indicated as "crunchy," etc. The recommendation unit 7 stores analytical data 21a, 21b, etc. of other users P1, P2, etc. in a storage unit in advance.

[0055] Next, when the recommendation unit 7 receives user analytical data 16a indicating the taste sensory information "refreshing" of event B from the extraction processing unit 9, the recommendation unit 7 extracts analytical data 21a1, 21b1, ... corresponding to the taste sensory information "refreshing" from the analytical data 21a, 21b, ... of other users P1, P2, ... stored in the storage unit. Then, the recommendation unit 7 compares the analytical data 21a1, 21b1, ... corresponding to the taste sensory information "refreshing" with the user's analytical data 16a.

[0056] At this time, the recommendation unit 7 may search for dish candidates to suggest to the user from analysis data 21a1, 21b1, ... including eating habits corresponding to the specific taste sensory information "refreshing" of other users, based on a characteristic index indicating eating habits corresponding to the specific taste sensory information "refreshing" input by the user. For example, as shown in FIG. 10, the recommendation unit 7 may use collaborative filtering to search for dish candidates to suggest to the user P by comparing the variable value 23 (characteristic index) of the user P with the variable values ​​24 (characteristic index) of the other users P1, P2, ... in the comparison data 22. At this time, the recommendation unit 7 may calculate the similarity between the variable value 23 of the user P and the variable values ​​24 of the other users P1, P2, ... for all variables X1, X2, ..., Y1, ..., Z1, .... As an example, the recommendation unit 7 may calculate a cosine similarity based on the vector of the variable values ​​23 of user P and the vectors of the variable values ​​24 of other users P1, P2, .... The recommendation unit 7 then selects other users whose similarity is a predetermined value or more, for example, other users P1 and P2 whose similarity is 0.8 or more, as users whose eating habits are highly similar to user P's. Next, the recommendation unit 7 counts, for each variable, the number of variable values ​​24a that are within a predetermined value (e.g., a matching value) of the variable value 23 of user P from the variable values ​​24 of other users P1 and P2 who have similar eating habits. The recommendation unit 7 then selects variables X2, Y1, and Z1 with the largest number of variable values ​​24a, and in step S7, outputs dish candidates based on the selected variables X2, Y1, and Z1. At this time, the recommendation unit 7 may preferentially select, from the selected variables X2, Y1, and Z1, a variable having an event A that matches the name or taste sensory information of a dish that user P ate one or two days ago.

[0057] The method of searching for dish candidates to be suggested to the user by the recommendation unit 7 is not limited to collaborative filtering. For example, based on a characteristic index indicating eating habits corresponding to specific taste sensory information "refreshing" input by the user, dish candidates to be suggested to the user may be searched for using statistical analysis, machine learning, deep learning, or the like from analysis data 21a1, 21b1, ... including eating habits corresponding to the taste sensory information "refreshing" of other users.

[0058] For example, as shown in FIG. 11, if the dishes of the B event of variables X2, Y1, and Z1 are "curry," "simmered chicken," and "beef bowl," the recommendation unit 7 may output these dishes as candidate dishes 25a to 25c. Furthermore, the recommendation unit 7 may output the dish of the A event of variables X2, Y1, and Z1, "nikujaga" (meat and potatoes), as reference dishes 26a to 26c. In this case, if the dish eaten by user P one day ago was "nikujaga," the recommendation unit 7 may preferentially select the variables X2, Y1, and Z1 in which the dish of the A event is recorded as "nikujaga." Furthermore, the recommendation unit 7 may output condition-related information 27 related to the conditions under which variables X2, Y1, and Z1 were selected. As a result, proposal data 28 is created, including the candidate dishes 25a to 25c, the reference dishes 26a to 26c, and the condition-related information 27.

[0059] In this way, the recommendation unit 7 outputs the dish candidates 25a to 25c to be suggested to the user P based on the characteristic indices 17, such as support, confidence, and lift value. Here, the characteristic indices 17 are extracted in the extraction unit 6 in accordance with the specific dish type information input by the user P based on the characteristics of the eating habits of the user P, by analyzing the characteristics of the eating habits of the user P based on the event data 15, which includes identification information of the dishes eaten by the user P, dish type information, and mealtime information. This allows the recommendation unit 7 to suggest appropriate dishes to the user P.

[0060] The recommendation unit 7 also analyzes the characteristics of the eating habits of the other users P1, P2, ..., and searches for dish candidates 25a-25c based on the characteristic index 17 of user P and the characteristic indexes 19 of the other users P1, P2, .... In this way, the recommendation unit 7 outputs dish candidates 25a-25c based on the eating habits of the other users P1, P2, .... This allows the recommendation unit 7 to output various dish candidates 25a-25c other than those based on user P's eating history, and can prevent the recommendation unit 7 from repeatedly suggesting dishes that user P has eaten before.

[0061] Here, the recommendation unit 7 may output information other than the candidate dishes 25a-25c. For example, the recommendation unit 7 may output eating habit data indicating the relationship over time between event data 15z1 and event data 15z2, including dish identification information and dish type information, based on the analysis data 16a of user P output from the extraction processing unit 9. The recommendation unit 7 may also output eating habit data indicating the relationship over time between event data 20z1 and event data 20z2, including dish identification information and dish type information, based on the analysis data 21a1 and 21b1 of other users P1 and P2. For example, as shown in FIG. 12, the recommendation unit 7 may output eating habit data 32, based on the analysis data 16a of user P, indicating the relationship over time between event data 15z1 and event data 15z2, including taste sensory information (dish type information), dish names (identification information), and ingredient information, using nodes 30 and edges 31. Here, the nodes 30 may be formed larger, for example, as the frequency or support of the event data 15z2 of event B is selected increases. Furthermore, the edges 31 may be formed thicker as the lift value or confidence increases. In this case, the recommendation unit 7 may create the eating habit data 32 by limiting the edges 31 to a predetermined number of edges 31 with the highest lift values ​​or confidence values ​​(for example, the top 26 edges 31). The recommendation unit 7 may then select a path 33 that connects the nodes 30 over time in descending order of thickness for a predetermined period (for example, four days), and display the path 33 by changing the display format.

[0062] In this way, the recommendation unit 7 outputs eating habit data 32 indicating the relationship over time between the event data 15z1 and 15z2 based on the characteristic index 17 indicating the characteristics of the eating habits of the user P. This visualizes the unconscious eating habits of the user P, allowing the user P to easily understand the intention behind the suggestion of the dish candidates 25a to 25c in the proposal data 28.

[0063] Next, the recommendation unit 7 transmits the created proposal data 28 to the terminal 1. At this time, the recommendation unit 7 may also transmit eating habit data 32 to the terminal 1. Upon receiving the proposal data 28 and the eating habit data 32, the terminal 1 displays the proposal data 28 and the eating habit data 32 on the display unit.

[0064] According to this embodiment, the extraction unit 6 acquires in advance event data 15 including identification information of dishes eaten by user P, dish type information, and mealtime information, analyzes the characteristics of user P's eating habits based on the event data 15, and extracts eating habit characteristic indicators 17 corresponding to specific dish type information input by user P based on the characteristics of user P's eating habits. Then, the recommendation unit 7 outputs dish candidates 25a to 25c to be suggested to user P based on the characteristic indicators 17. This allows the recommendation unit 7 to suggest appropriate dishes to user P.

[0065] In this embodiment, the recommendation unit 7 outputs the dish candidates 25a-25c based on the eating habits of other users P1, P2, ..., but is not limited to this as long as it can output the dish candidates 25a-25c. For example, the recommendation unit 7 may select the dish candidates 25a-25c from the analysis data 16a indicating the eating habits of user P based on the characteristic index 17. For example, the recommendation unit 7 may select the dish candidates 25a-25c in descending order of the value of the characteristic index 17 in the analysis data 16a.

[0066] In addition, in this embodiment, the dish recommendation system 3 is disposed in the server 2, but is not limited to this as long as it can output dish candidates. For example, the dish recommendation system 3 may be disposed in the terminal 1.

[0067] In addition, although the present disclosure has been described using names of Japanese dishes or ingredients in the present embodiment, the present disclosure is not limited thereto. For example, the names of dishes or ingredients may be changed to suit the food culture of the country in which the dish recommendation system, dish recommendation method, and program are used.

[0068] Although specific examples of the present disclosure have been described in detail above, these are merely examples and do not limit the scope of the claims. The technology described in the claims includes various modifications and alterations of the specific examples exemplified above.

[0069] This application is based on a Japanese patent application (Patent Application No. 2023-051611) filed on March 28, 2023, the contents of which are incorporated herein by reference. [Industrial Applicability]

[0070] The dish recommendation system according to the present disclosure can be used as a system that suggests dish candidates to a user. [Explanation of symbols]

[0071] 1 device 2 Server 3. Food recommendation system 4 Acquisition part 5 Storage section 6 Extraction part 7 Recommendation Department 8 Analysis Processing Unit 9 Extraction processing section 11 Storage device 12 processors 13 UI device 14. Communications equipment 15 Event Data 16,16a Analysis Data 17 Characteristic indicators 18a, 18b Event data 21a, 21b Analysis data 22 Comparative Data 23 Variable Values 24 Variable Values 25a~25c Culinary Candidates 26a~26c Reference dishes 27 Condition-related information 28 Proposal Data 29 Specific taste sensory information 30 nodes 31 Edge 32 Eating Habits Data 33 Routes P User P1,P2 Other users

Claims

1. an acquisition unit that acquires in advance event data including identification information of a dish eaten by a user, dish type information including taste sensation information that classifies the dish by taste sensation, and meal time information; an extraction unit that calculates, based on the event data, a characteristic index including at least one of a support factor, a confidence factor, and a lift value related to the user's eating habits by time-series association analysis, and extracts a specific characteristic index according to specific cuisine type information input by the user; a recommendation unit that outputs dish candidates to be suggested to the user based on the specific characteristic index; A food recommendation system with

2. The food recommendation system according to claim 1 , wherein the taste sensation information includes information indicating onomatopoeia that expresses the sensation of taste.

3. The dish recommendation system according to claim 1 , wherein the event data includes information about ingredients of the dish eaten by the user.

4. The dish recommendation system according to claim 3 , wherein the event data is configured by associating at least one of the dish identification information and the ingredient information with the dish type information.

5. The dish recommendation system according to claim 1 , wherein the extraction unit further extracts the specific characteristic index by using at least one of preference information and avoided ingredient information of the user.

6. The dish recommendation system according to claim 1 , wherein the recommendation unit analyzes characteristics of eating habits of other users and searches for the dish candidates based on the specific characteristic index of the user and the characteristic index of the other users.

7. The cooking recommendation system according to claim 1 , wherein the recommendation unit outputs eating habit data that indicates a temporal relationship of the event data using nodes and edges based on the specific characteristic index.

8. A computer comprising: Acquiring in advance event data including identification information of a dish eaten by a user, dish type information including taste sensation information that classifies the dish by taste sensation, and meal time information; Calculating a characteristic index including at least one of a support factor, a confidence factor, and a lift value related to the user's eating habits based on the event data through time-series association analysis, and extracting a specific characteristic index according to specific cuisine type information input by the user; outputting dish candidates to the user based on the specific characteristic indicators; A dish recommendation method having the above.

9. Acquiring in advance event data including identification information of a dish eaten by a user, dish type information including taste sensation information that classifies the dish by taste sensation, and meal time information; Calculating a characteristic index including at least one of a support factor, a confidence factor, and a lift value related to the user's eating habits based on the event data through time-series association analysis, and extracting a specific characteristic index according to specific cuisine type information input by the user; outputting dish candidates to the user based on the specific characteristic indicators; A program that causes a computer to execute the following.

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