Dish recommendation system, dish recommendation method, and program

Abstract

The dish recommendation system according to the present invention comprises: an acquisition unit for pre-acquiring event data including identification information, dish category information, and eating time information of dishes that have been eaten by a user, the dish category information including taste feeling information in which the dishes are classified by taste feelings; an extraction unit that calculates, on the basis of the event data, a characteristic index including at least one of a degree of support, a degree of confidence, and a degree of elevation relating to a dietary habit of the user by time-series correlation analysis, and extracts a specific characteristic index corresponding to specific dish category information input by the user; and a recommendation unit that outputs dish candidates proposed to the user on the basis of the specific characteristic index.

Description

Technical Field

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

[0002] In the past, a dish recommendation system for proposing dishes based on a user's eating history has been proposed. For example, Patent Document 1 discloses a dish recommendation device for proposing a dish to be eaten next using an eating pattern, preference, and food intake, wherein the eating pattern is an eating pattern based on past eating history.

[0003] Prior art literature

[0004] Patent Literature

[0005] Patent Document 1: Japanese Patent Application Publication No. 2022-124701 Summary of the invention

[0006] Problem that the invention aims to solve

[0007] However, since the device of Patent Document 1 automatically proposes dishes without confirming the user's request, there is a possibility that the proposed dishes are different from the dishes that the user wants to eat.

[0008] The present invention aims to provide a dish recommendation system for suggesting suitable dishes to users.

[0009] Solutions to the problem

[0010] The dish recommendation system disclosed in the present invention includes: an acquisition unit, which pre-acquires event data including identification information of dishes that the user has eaten, dish category information and eating time information, wherein the dish category information includes taste sensation information for classifying dishes according to taste sensation; an extraction unit, which calculates characteristic indicators including at least one of support, confidence and lift related to the user's eating habits through time series association analysis based on the event data, and extracts specific characteristic indicators corresponding to specific dish category information input by the user; and a recommendation unit, which outputs dish candidates proposed to the user based on the specific characteristic indicators.

[0011] The dish recommendation method disclosed in the present invention includes the following steps: pre-acquiring event data including identification information of dishes that the user has eaten, dish category information and eating time information, wherein the dish category information includes taste sensation information for classifying dishes according to taste sensation; based on the event data, calculating characteristic indicators including at least one of support, confidence and lift related to the user's eating habits through time series association analysis, and extracting specific characteristic indicators corresponding to the specific dish category information input by the user; and outputting dish candidates proposed to the user based on the specific characteristic indicators.

[0012] The program disclosed in the present invention enables a computer to perform the following processing: pre-acquiring event data including identification information of dishes that a user has eaten, dish category information and eating time information, wherein the dish category information includes taste sensation information for classifying dishes according to taste sensation; based on the event data, calculating characteristic indicators including at least one of support, confidence and lift related to the user's eating habits through time series association analysis, and extracting specific characteristic indicators corresponding to the specific dish category information input by the user; and outputting dish candidates proposed to the user based on the specific characteristic indicators.

[0013] Effects of the Invention

[0014] According to the present disclosure, suitable dishes can be suggested to users. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a diagram showing an outline of one embodiment of the present disclosure.

[0016] Figure 2 1 is a block diagram showing the functional structure of a dish recommendation system according to one embodiment of the present disclosure.

[0017] Figure 3 1 is a block diagram showing the hardware structure of a dish recommendation system according to one embodiment of the present disclosure.

[0018] Figure 4 It is a flowchart showing the operation of one embodiment of the present disclosure.

[0019] Figure 5 This is a diagram showing dish category information determined based on ingredient information.

[0020] Figure 6 is a graph representing event data.

[0021] Figure 7 This is a diagram showing analysis data obtained by performing a time-series correlation analysis on user event data.

[0022] Figure 8 This is a graph showing analytical data extracted based on specific dish category information.

[0023] Fig. 9 This is a diagram showing a state of performing time series correlation analysis on event data of other users.

[0024] Fig.10 This is a graph showing comparative data obtained by comparing the characteristic indexes of the user with those of other users.

[0025] Fig.11 This is a diagram showing suggestion data including menu candidates output from the recommendation unit.

[0026] Fig.12 This is a diagram showing the eating habit data output from the recommendation unit. DETAILED DESCRIPTION

[0027] Hereinafter, embodiments of the present disclosure will be described with reference to the drawings.

[0028] The summary of the present disclosure is described. Figure 1 As shown, if the eating data (such as images of dishes, etc.) that the user has eaten is input, the user's terminal 1 sends the eating data to the server 2 in sequence. Here, it is assumed that the server 2 has the dish recommendation system disclosed in the present invention. If the dish recommendation system of the server 2 receives the eating data from the terminal 1, based on the eating data, it obtains event data including the identification information (such as name, etc.) of the dishes that the user has eaten, the dish category information (such as light, etc.) and the eating time information (such as date, etc.), and analyzes the characteristics of the user's eating habits based on the event data. At this time, if the user's eating habits are analyzed only based on the identification information and eating time information of the dishes, the eating habits for the dishes themselves are analyzed, and it is difficult to analyze the eating habits based on the dish category, such as "I want to eat something strong every 3 days". Here, when the specific dish category information (such as light, etc.) that the user wants to eat is input into the terminal 1, the terminal 1 sends the specific dish category information to the server 2. If the dish recommendation system of the server 2 receives specific dish category information from the terminal 1, it analyzes the characteristics of the user's eating habits based on the event data containing the dish category information, and extracts the characteristic index of the eating habits corresponding to the specific dish category information received from the terminal 1. Then, the dish recommendation system outputs a dish candidate proposed to the user based on the extracted characteristic index, and sends the dish candidate to the terminal 1. As a result, the terminal 1 displays the dish candidate corresponding to the specific dish category information on the display unit.

[0029] Next, the structure of the dish recommendation system disclosed in the present invention is described in detail. Figure 2 As shown, the menu 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, and the storage unit 5 is connected to the recommendation unit 7 via the extraction unit 6.

[0030] The acquisition unit 4 acquires event data including identification information of dishes that the user has eaten, dish category information, and eating time information. Here, it can be that the dishes include single dishes (such as hamburger steaks, etc.), and menus representing multiple dishes (such as rice and hamburger steaks, etc.). In addition, as identification information of dishes, for example, the name of the dish, the identification number, etc. can be cited. In addition, the dish category information is information that classifies dishes by category, for example, it can include taste sensation information that classifies dishes by taste. Here, as taste sensation information, for example, mouthfeel information representing physical taste sensation, taste information representing chemical taste sensation, etc. can be cited. Specifically, the taste sensation information can include information representing onomatopoeia, that is, information representing onomatopoeia or onomatopoeia, which is an onomatopoeia that expresses the feeling of taste. As onomatopoeia, for example, light, strong, tender, chewy, soft, crisp, sticky, etc. can be cited. Here, the information representing the onomatopoeia can be composed of at least a part of the onomatopoeia. In addition, the eating time information is information related to the time of eating, and the eating time information can include the time, date, time section (e.g., breakfast, lunch, dinner) of eating.

[0031] For example, the acquisition unit 4 can receive the eating data input by the user from the terminal 1. Here, the eating data is data related to the dishes that the user has eaten, such as images (photos, etc.) of the dishes, menus, questionnaires about the dishes (for example, preference information of the dishes, avoidance of ingredients information, attribute information of the user, etc.), and dish category information. The preference information of the dishes is information related to the dishes preferred by the user, such as the names of the dishes or the names of the ingredients preferred by the user. In addition, the avoidance of ingredients information is information related to the ingredients that the user avoids using in the dishes, such as ingredients that the user does not like, ingredients that have allergic reactions, etc. In addition, the attribute information of the user can include gender, whether the user is losing weight, whether there is a chronic disease (such as high blood pressure, etc.). If the acquisition unit 4 receives the eating data, based on the eating data, it creates corresponding event data for the identification information of the dishes that the user has eaten, the dish category information, and the eating time information.

[0032] The storage unit 5 stores the event data acquired by the acquisition unit 4. That is, the storage unit 5 stores the event data in which the identification information of the dish eaten by the user, the dish category information, and the eating time information are associated with each other.

[0033] The extracting unit 6 includes an analyzing unit 8 and an extracting unit 9. The analyzing unit 8 is connected to the extracting unit 9. The storing unit 5 is connected to the analyzing unit 8, and the extracting unit 9 is connected to the recommending unit 7. The acquiring unit 4 is connected to the extracting unit 9.

[0034] The analysis processing unit 8 analyzes the characteristics of the user's eating habits through time series association analysis based on the event data stored in the storage unit 5. Here, the characteristics of the eating habits represent the tendency (e.g., rules or conditions, etc.) of the user to select dishes during multiple meals. For example, it can represent the tendency to select "strong-flavored" dishes after "light" dishes during three meals. For example, the analysis processing unit 8 calculates a characteristic index including at least one of support, confidence, and lift related to the user's eating habits.

[0035] The extraction processing unit 9 extracts the characteristic index of the eating habits corresponding to the specific dish category 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 dish category information input by the user to the terminal 1 from the terminal 1, the acquisition unit 4 outputs the specific dish category information to the extraction processing unit 9. For example, when the specific taste sensation information "light" is input by the user as the specific dish category information, the extraction processing unit 9 calculates the characteristic index representing the eating habits corresponding to the "light".

[0036] The recommendation unit 7 outputs the dish candidates proposed to the user based on the characteristic index obtained by the extraction processing unit 9. At this time, the recommendation unit 7 can output the dish candidates based on the event data that satisfies at least one of the three conditions of "support is above the specified threshold", "confidence is above the specified threshold", and "lift is above the specified threshold". In addition, the recommendation unit 7 can also analyze the characteristics of the eating habits of other users based on the event data containing the identification information of the dishes that other users have eaten, the dish category information, and the eating time information. Then, the recommendation unit 7 can search for the dish candidates proposed to the user based on the characteristic index of the user calculated by the extraction processing unit 9 and the characteristic index of other users.

[0037] Figure 3 It shows the hardware structure of the dish recommendation system 3.

[0038] The menu 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.

[0039] In addition, the programs or instructions for implementing the various functions and processes described later in the dish recommendation system 3 can be downloaded from an external device via the network, etc., or provided by a removable storage medium such as a CD-ROM (Compact Disk-Read Only Memory) or a flash memory.

[0040] The storage device 11 is implemented by one or more non-transitory storage media such as a random access memory, a flash memory, a hard disk drive, etc., and stores installed programs or instructions and files and data used in executing the programs or instructions.

[0041] The processor 12 may be implemented by one or more CPUs (Central Processing Units) that may be composed of one or more processor cores, GPUs (Graphics Processing Units), processing circuits, etc. The processor 12 executes various functions and processes of the dish recommendation system 3 described later according to the program, instructions, and data such as parameters required to execute the program or instructions stored in the storage device 11.

[0042] The UI device 13 may be composed of input devices such as a keyboard, a mouse, a camera, a microphone, output devices such as a display, a speaker, a headset, a printer, and input and output devices such as a smart phone, a tablet computer, and a touch panel, to realize an interface between the manager and the dish recommendation system 3. For example, the manager may use a keyboard, a mouse, etc. to operate a GUI (Graphical User Interface) displayed on a display or a touch panel to operate the dish recommendation system 3.

[0043] In addition, the above hardware structure is only an example, and the dish recommendation system 3 of the present disclosure can also be implemented by any other appropriate hardware structure.

[0044] Next, refer to Figure 4 The flowchart shown explains the operation of this embodiment.

[0045] First, in step S1, Figure 2The acquisition unit 4 of the dish recommendation system 3 shown acquires event data including identification information, dish category information and eating time information of the dish that the user has eaten. For example, the acquisition unit 4 can acquire event data based on the user's eating data. For example, if the user inputs eating data such as a photographed image of the dish that has been eaten into the terminal 1, the terminal 1 sends the eating data to the server 2 in sequence. Then, in the server 2, when the acquisition unit 4 of the dish recommendation system 3 receives the user's eating data from the terminal 1, it acquires the identification information, dish category information and eating time information of the dish that the user has eaten based on the eating data. At this time, the acquisition unit 4 can also further acquire the food information of the dish that the user has eaten based on the eating data. In addition, the food information is the information of the food used in the dish, for example, it can include information such as ingredients (such as white radish, carrots, etc.), seasonings (such as soy sauce, sauce, etc.), and the amount of food.

[0046] For example, the acquisition unit 4 can identify the name (identification information) and ingredient information of the dish that the user has eaten based on the photographed image of the dish received as the eating data, and estimate the taste sensation information (dish category information) that classifies the dish by taste sensation based on the ingredient information. Figure 5 When the same dishes such as chicken stew 1 to chicken stew 5 are identified as shown, the acquisition unit 4 may also identify the differences in the food information of the food materials used in the chicken stews 1 to 5, and estimate the taste sensory information based on the food information. For example, when the acquisition unit 4 recognizes that the food information of chicken stew 1 is yam based on the captured image of chicken stew 1, the taste sensory information is estimated to be "soft" based on the taste of the yam. In addition, although the food information of chicken stew 4 and chicken stew 5 both show "white radish", they can be classified as "light" and "sticky" respectively based on other ingredients. That is, the taste sensory information can be comprehensively estimated based on the food information of multiple ingredients used in the dishes. In addition, the acquisition unit 4 can, for example, acquire the eating time information based on the shooting time attached to the eating data received from the terminal 1.

[0047] In addition, in the above-mentioned embodiment, the acquisition unit 4 acquires the identification information, dish category information, eating time information and ingredient information of the dish that the user has eaten based on the user's eating data, but is not limited to this, as long as this information can be acquired. For example, the acquisition unit 4 can also directly acquire the identification information, dish category information, eating time information and ingredient information of the dish that the user has eaten from the terminal 1. That is, it can also be that the terminal 1 receives the input of the identification information, dish category information, eating time information and ingredient information of the dish that the user has eaten from the user, and sends the identification information, dish category information, eating time information and ingredient information of the dish input by the user to the server 2.

[0048] 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 the event data that establishes a correspondence between at least one of the dish and the ingredient information that the user has eaten and the dish category information. Figure 6 As shown, the acquisition unit 4 can store the name (identification information), taste information (dish category information) and ingredient information of the dish that the user has eaten in the storage unit 5 according to the date shown in the eating time information, and establish the corresponding event data 15. In this way, the acquisition unit 4 updates the event data 15 every time the user's eating data is received, so as to construct the event data 15 of the dishes that the user has eaten in the past.

[0049] On the other hand, the terminal 1 may also accept a proposal commission from the user, which is a commission for proposing a dish that the user is going to start cooking from now on. At this time, the terminal 1 may accept input of specific dish category information. For example, the terminal 1 may accept specific taste sensation information that classifies dishes by taste sensation. At this time, the terminal 1 may accept information representing onomatopoeia as taste sensation information, which onomatopoeia is an onomatopoeia that expresses the sensation of taste. When specific dish category information is input by the user, the terminal 1 sends the specific dish category information to the server 2. Here, it is assumed that the user inputs the onomatopoeia "light" as specific taste sensation information into the terminal 1.

[0050] As described above, the dish category information includes taste sensation information for classifying dishes by the sensation of taste. For example, if dishes are classified by food culture such as "French dishes", it will be difficult to appropriately represent the dishes desired by the user because there are various categories of dishes in this category (for example, "light" dishes and "strong" dishes). Here, the user specifies the category of the dish using the taste sensation information, thereby being able to appropriately specify the range of the desired dishes. In addition, the taste sensation information includes information representing onomatopoeia, which is an onomatopoeia that expresses the sensation of taste. Therefore, the user can easily specify the taste sensation information.

[0051] In the dish recommendation system 3 of the server 2, when the specific taste sensation information "light" input by the user is received 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 storage unit 5. For example, in step S3, the analysis processing unit 8 of the extraction unit 6 extracts the event data 15 of a specified time period (for example, 14 days) from the storage unit 5. Then, in step S4, the analysis processing unit 8 can calculate the characteristic index representing the user's eating habits by performing a time series association analysis on the extracted event data 15. As the characteristic index, for example, support, confidence, and lift for the combination of events can be cited. For example, the analysis processing unit 8 can calculate a characteristic index including at least one of support, confidence, and lift. In addition, it can also be that the analysis processing unit 8 automatically calculates the characteristic index based on the event data 15 stored in the storage unit 5 without receiving the specific taste sensation information from the user.

[0052] Specifically, it can be, Figure 6 As shown, the analysis and processing unit 8 identifies the name of the dish, taste sensory information, and ingredient information in each column (for example, each day) of the event data 15 of 14 days (a specified time period) as event data 15a, event data 15b, ..., and then calculates the characteristic index for the combination of two event data contained in a preset analysis time period (for example, 3 days). For example, the analysis and processing unit 8 uses April 10 as the base date, and sets April 10, April 9 (1 day ago), and April 8 (2 days ago) as the analysis time period, and calculates the characteristic index for the combination of two event data contained in the analysis time period. For example, the event before the base date is regarded as event A, and the event on the base date is regarded as event B, as shown in FIG. Figure 7 As shown, the analysis processing unit 8 calculates the characteristic index for each combination of the event data 15z1 belonging to the A event (the event on April 9 or April 8) and the event data 15z2 belonging to the B event (the event on April 10). Next, the analysis processing unit 8 shifts the analysis time period by one day and sets April 9 as the reference date, and sets the three days of April 9, April 8 (one day ago), and April 7 (two days ago) as the next analysis time period. Similarly, the analysis processing unit 8 calculates the characteristic index for each combination of the two event data 15z1 and 15z2 included in the analysis time period. In this way, the analysis processing unit 8 sequentially shifts the analysis time period in 14 days (prescribed time period) and calculates the characteristic index for the combination of the two event data 15z1 and 15z2 included in each analysis time period. Then, the analysis processing unit 8 generates the analysis data 16 of 14 days (prescribed time period), and the analysis data 16 includes the event data 15z1 of the A event, the event data 15z2 of the B event, and the characteristic index.

[0053] Here, the support degree, for example, represents the number of combinations of event data 15z1 of event A and event data 15z2 of event B, and can be calculated using the following formula (1). In addition, the confidence degree, for example, represents the probability of eating the dish of event A one day before or two days before eating the dish of event B, and can be calculated using the following formula (2). In addition, the lift degree, for example, represents the increase in the probability of eating the dish of event A one day before or two days before eating the dish of event B, and can be calculated based on the following formula (3). In addition, the total number of data can be calculated, for example, as follows: the number of dish identification information × the number of dish category information × the number of ingredient information × the number of analysis time periods (for example, 3 days).

[0054] Support = the number of data representing the same combination of event data 15z1 of event A and event data 15z2 of event B ÷ the total number of data...(1)

[0055] Confidence = the number of data indicating that event data 15z1 of event A and event data 15z2 of event B are in the same combination ÷ the number of data including event data 15z2 of event B...(2)

[0056] Lift = the number of data representing the same combination of event data 15z1 of event A and event data 15z2 of event B ÷ the number of data including event data 15z2 of event B ÷ the number of data including event data 15z1 of event A ÷ the total number of data...(3)

[0057] At this time, the analysis processing unit 8 may also search the analysis data 16 based on the temporal sequence of the event data 15a, 15b, ... in the event data 15, and filter out the data in the sequence that is not included in the event data 15. For example, when the event data 15z2 (equivalent to the event B) is recorded on the next day of the event data 15z1 (equivalent to the event A) in the event data 15, the analysis processing unit 8 may filter out the data indicating that the event A is the event data 15z2 and the event B is the event data 15z1 (the sequence is opposite to that of the event data 15) in the analysis data 16. Thus, the event data 15 is subjected to association analysis in accordance with the time sequence.

[0058] Thus, the analysis processing unit 8 analyzes the characteristics of the eating habits of the user P by performing time series correlation analysis on the event data 15 and outputs characteristic indices such as support, confidence, and lift. Thus, the analysis processing unit 8 can accurately show the eating habits of the user P with characteristic indices.

[0059] In addition, the event data 15 includes the food information of the dishes eaten by the user P. Thus, the analysis processing unit 8 can analyze the characteristics of the eating habits of the user P more accurately based on the difference in the food information.

[0060] The event data 15 is data in which at least one of the information on dishes and ingredients eaten by the user P is associated with the dish category information. Thus, the analysis processing unit 8 can easily analyze the characteristics of the user P's eating habits.

[0061] In addition, the dish classification information includes taste information for classifying dishes according to the taste sensation. Since the analysis processing unit 8 performs analysis based on the taste information that has a great influence on the user's eating habits, the characteristics of the eating habits of the user P can be accurately analyzed. In addition, the taste information includes information indicating onomatopoeia and mimetic words that express the taste sensation. Since the analysis processing unit 8 performs analysis based on the onomatopoeia and mimetic words that accurately express the taste information, the characteristics of the eating habits of the user P can be more accurately analyzed.

[0062] Next, the analysis processing unit 8 outputs the generated analysis data 16 to the extraction processing unit 9. In addition, in the above-mentioned embodiment, after receiving the specific dish category information input by the user P from the terminal 1, the analysis processing unit 8 performs a time series correlation analysis on the event data 15, but it is not limited to this, as long as the time series correlation analysis can be performed. For example, the analysis processing unit 8 may also store the analysis data 16 obtained by performing a time series correlation analysis on the event data 15 in the storage unit in advance. Then, when receiving the specific dish category information input by the user P 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.

[0063] The extraction processing unit 9 receives the analysis data 16 from the extraction processing unit 9. In addition, the extraction processing unit 9 receives the specific taste sensation information “light” input to the terminal 1 by the user P from the acquisition unit 4. Then, in step S5, the extraction processing unit 9 filters the analysis data 16 based on the specific taste sensation information “light”.

[0064] For example, Figure 8 As shown, the extraction processing unit 9 can extract the dish category information of event B from the analysis data 16, which represents the analysis data 16a of the specific taste sensation information 29 "light". In addition, the variable name is the name of the combination of event data 15z1 of event A and event data 15z2 of event B. Here, for the same combination of event data 15z1 of event A and event data 15z2 of event B, the support, confidence, and lift are separately represented in the form of variable names X, Y, and Z, that is, they are divided into support (X1, X2...), confidence (Y1,...), and lift (Z1,...) for representation. In addition, the variable value is the value after the support, confidence, and lift of the characteristic index are normalized.

[0065] In this way, the extraction processing unit 9 extracts the characteristic index 17 indicating the eating habits corresponding to the specific taste information 29 input by the user, based on the analysis data 16 indicating the characteristics of the user's eating habits.

[0066] In addition, the extraction processing unit 9 may also use at least one of the preference information and the avoidance food information of the user P to extract the characteristic index 17. For example, the extraction processing unit 9 may also retrieve the analysis data 16a based on the preference information of the user P, and preferentially extract the event data including the characteristic index 17, which is the characteristic index 17 in which the dish or food corresponding to the preference information is registered. In addition, the extraction processing unit 9 may also retrieve the analysis data 16a based on the avoidance food information of the user P, and extract the event data including the characteristic index 17, which is the characteristic index 17 in which no avoidance food information is registered. That is, the extraction processing unit 9 may delete the event data in which the avoidance food information is registered from the analysis data 16a. In addition, the preference information and the avoidance food information may be calculated based on the eating data of the user P, or may be set by the user P. Thus, the extraction processing unit 9 can appropriately extract the characteristic index 17 according to the preference information or avoidance food information of the user P.

[0067] In addition, the extraction processing unit 9 may further use the attribute information of the user P to extract the characteristic index 17. For example, when the attribute information of the user P is set to "diet", the extraction processing unit 9 may extract the event data of dishes with calories below a predetermined value. In addition, the attribute information may be set by the user P.

[0068] In addition, the extraction processing unit 9 may also use the cooking recipe browsing history to extract the characteristic index 17. For example, when the user P searches for the cooking recipe of a dish on the Internet, the terminal 1 may send the cooking recipe browsing information to the server 2. The extraction processing unit 9 may calculate the preference information of the user P based on the cooking recipe browsing history sent from the terminal 1. Furthermore, the extraction processing unit 9 may also preferentially extract event data including the characteristic index 17, which is a characteristic index 17 of a dish or ingredient corresponding to the calculated preference information of the user P.

[0069] Next, the extraction processing unit 9 outputs the analysis data 16a including the extracted characteristic index 17 to the recommendation unit 7. After the analysis data 16a is input, in step S6, the recommendation unit 7 compares the analysis data 16a of the user with the analysis data obtained by performing a time series correlation analysis on the characteristics of the eating habits of other users. Here, the recommendation unit 7 may also analyze the characteristics of the eating habits of other users in the same manner as when analyzing the user based on the event data including the identification information of the dishes eaten by other users, the dish category information, and the eating time information, and store the obtained analysis data in advance in the storage unit.

[0070] For example, Fig. 9 As shown, the recommendation unit 7 pre-acquires event data 18a, 18b, ... of multiple other users P1, P2, ..., and analyzes the characteristics of the eating habits of each of the other users based on the event data 18a, 18b, .... At this time, the recommendation unit 7 can calculate the characteristic index 19 representing the eating habits of the other users by performing a time series correlation analysis on the event data 18a, 18b, .... Then, the recommendation unit 7 can generate analysis data 21a, 21b, ... including event data 20z1 of event A, event data 20z2 of event B, and characteristic index 19.

[0071] In addition, when the variable names of other users P1, P2, ... are inconsistent (the variable name of a specific user is missing), the recommendation unit 7 may also interpolate the missing variable name of the specific user based on the average value of the variable values ​​of other users P1, P2, ... where the variable name exists. In addition, the recommendation unit 7 may obtain the preference information, avoidance food information, attribute information, or cooking recipe browsing history of the user P from the extraction processing unit 9, and use this information to extract the characteristic index 19 in the same way as the extraction processing unit 9. For example, between step S6 and step S7, the recommendation unit 7 may filter the analysis data 21a, 21b, ... based on the preference information, avoidance food information, attribute information, or cooking recipe browsing history of the user P.

[0072] Here, analysis data 21a may be generated separately according to the category of the dish category information of event B. For example, regarding the analysis data 21a of other user P1, analysis data 21a1 indicating that the taste sensation information of event B is "light", analysis data 21a2 indicating that the taste sensation information of event B is "tender", analysis data 21a3 indicating that the taste sensation information of event B is "chewy", etc. may be generated separately according to the category of taste sensation information. The recommendation unit 7 stores the analysis data 21a, 21b, ... of other users P1, P2, ... in the storage unit in advance.

[0073] Next, when the analysis data 16a of the user whose taste information “light” indicates event B is input from the extraction processing unit 9, the recommendation unit 7 extracts the analysis data 21a1, 21b1, ... corresponding to the taste information “light” from the analysis data 21a, 21b, ... of other users P1, P2, ... stored in the storage unit. Then, the recommendation unit 7 compares the analysis data 16a of the user with the analysis data 21a1, 21b1, ... corresponding to the taste information “light”.

[0074] At this time, the recommendation unit 7 can search for dish candidates to be proposed to the user from the analysis data 21a1, 21b1, ... containing the eating habits of other users corresponding to the taste information "light" based on the characteristic index indicating the eating habits corresponding to the specific taste information "light" input by the user. Fig.10 As shown, collaborative filtering is used to compare the variable value 23 (characteristic index) of user P with the variable values ​​24 (characteristic index) of other users P1, P2, ... in the comparison data 22, thereby searching for dish candidates proposed to user P. At this time, the recommendation unit 7 can calculate the similarity of the variable values ​​24 of other users P1, P2, ... relative to the variable value 23 of user P for all variables X1, X2, ..., Y1, ..., Z1, .... As an example, the recommendation unit 7 can also calculate the cosine similarity based on the vector of the variable value 23 of user P and the vector of the variable value 24 of other users P1, P2, .... In addition, the recommendation unit 7 selects other users whose similarity is greater than a specified value (for example, other users P1 and P2 whose similarity is greater than 0.8) as users with high similarity in eating habits with user P. Next, the recommendation unit 7 counts, for each variable, the number of variable values ​​24a that present values ​​within a specified value (e.g., the same value) relative to the variable value 23 of the user P among the variable values ​​24 of other users P1 and P2 with similar eating habits. Then, the recommendation unit 7 selects the variables X2, Y1, and Z1 having a large number of variable values ​​24a, and in step S7, outputs the dish candidates based on the selected variables X2, Y1, and Z1. At this time, the recommendation unit 7 may also preferentially select the variable having the A event that is consistent with the name or taste sensation information of the dish that the user P has eaten 1 day or 2 days ago from the selected variables X2, Y1, and Z1.

[0075] In addition, the method by which the recommendation unit 7 searches for dish candidates proposed to the user is not limited to collaborative filtering. For example, based on a characteristic index representing an eating habit corresponding to the specific taste sensation information "light" input by the user, statistical analysis, machine learning, or deep learning may be used to search for dish candidates proposed to the user from analysis data 21a1, 21b1, ... containing other users' eating habits corresponding to the taste sensation information "light".

[0076] For example, Fig.11 As shown, when the dishes of event B of variables X2, Y1 and Z1 are "curry", "chicken stew" and "beef rice", the recommendation unit 7 can output these dishes as dish candidates 25a~25c. In addition, the recommendation unit 7 can also output the dish "potato stew" of event A of variables X2, Y1 and Z1 as reference dishes 26a~26c. At this time, the recommendation unit 7 can give priority to variables X2, Y1 and Z1 whose dish record of event A is "potato stew" when the dish that user P ate 1 day ago is "potato stew". In addition, the recommendation unit 7 can also output condition-related information 27 related to the selection conditions when variables X2, Y1 and Z1 are selected. Thus, the proposed data 28 including dish candidates 25a~25c, reference dishes 26a~26c and condition-related information 27 is prepared.

[0077] In this way, the recommendation unit 7 outputs the dish candidates 25a to 25c proposed to the user P based on the characteristic indexes 17 such as support, confidence, and lift. Here, the characteristic index 17 is a characteristic index extracted in the following manner, that is, in the extraction unit 6, the characteristics of the eating habits of the user P are analyzed based on the event data 15 including the identification information of the dishes that the user P has eaten, the dish category information, and the eating time information, and the characteristic index is extracted based on the characteristics of the eating habits of the user P, corresponding to the specific dish category information input by the user P. In this way, the recommendation unit 7 can propose a suitable dish to the user P.

[0078] In addition, the recommendation unit 7 analyzes the characteristics of the eating habits of other users P1, P2, ..., and searches for dish candidates 25a to 25c based on the characteristic index 17 of the user P and the characteristic index 19 of other users P1, P2, .... In this way, the recommendation unit 7 outputs dish candidates 25a to 25c based on the eating habits of other users P1, P2, .... Therefore, the recommendation unit 7 can output various dish candidates 25a to 25c other than the eating history of the user P, and can prevent the situation where dishes that the user P has eaten before are proposed multiple times.

[0079] Here, the recommendation unit 7 may also output other information besides the dish candidates 25a to 25c. For example, the recommendation unit 7 may also output the eating habit data representing the temporal relationship between the event data 15z1 and the event data 15z2 based on the analysis data 16a of the user P outputted from the extraction processing unit 9, wherein the event data 15z1 and the event data 15z2 are event data including the identification information and the dish category information. In addition, the recommendation unit 7 may also output the eating habit data representing the temporal relationship between the event data 20z1 and the event data 20z2 based on the analysis data 21a1 and 21b1 of other users P1 and P2, wherein the event data 20z1 and the event data 20z2 are event data including the identification information and the dish category information. For example, Fig.12 As shown, the recommendation unit 7 can also output the eating habit data 32 based on the analysis data 16a of the user P, which represents the temporal relationship between the event data 15z1 and the event data 15z2 with nodes 30 and edges 31. The event data 15z1 and the event data 15z2 are event data containing taste sensory information (dish category information), the name of the dish (identification information) and the food information. Here, for example, the higher the frequency or support of the event data 15z2 of the B event is selected, the larger the node 30 is formed. In addition, the greater the lift or confidence, the thicker the edge 31 is formed. At this time, the recommendation unit 7 can also limit the specified number of edges 31 to produce the eating habit data 32, and the specified number of edges 31 is the specified number of edges that are ranked in front when sorted from large to small by lift or confidence (for example, limited to the 26 edges 31 that are ranked in front). Furthermore, the recommendation unit 7 may select a path 33 that is a path that connects the nodes 30 in order of size in a time series for a predetermined time period (eg, four days), and display the path 33 in a changed display format.

[0080] In this way, the recommendation unit 7 outputs the eating habit data 32 indicating the temporal relationship between the event data 15z1 and the event data 15z2 based on the characteristic index 17 indicating the characteristics of the eating habit of the user P. In this way, since the unconscious eating habit of the user P is visualized, the user P can easily understand the intention of the dish candidates 25a to 25c in the suggestion data 28 being suggested.

[0081] Next, the recommendation unit 7 sends the created suggestion data 28 to the terminal 1. At this time, the recommendation unit 7 may also send the eating habit data 32 to the terminal 1. Upon receiving the suggestion data 28 and the eating habit data 32, the terminal 1 displays the suggestion data 28 and the eating habit data 32 on the display unit.

[0082] According to the present embodiment, the extraction unit 6 pre-acquires event data 15 including identification information of dishes that the user P has eaten, dish category information, and eating time information, analyzes the characteristics of the eating habits of the user P based on the event data 15, and extracts a characteristic index 17 of the eating habits corresponding to the specific dish category information input by the user P based on the characteristics of the eating habits of the user P. Then, the recommendation unit 7 outputs dish candidates 25a to 25c proposed to the user P based on the characteristic index 17. Thus, the recommendation unit 7 can propose a suitable dish to the user P.

[0083] In addition, in the present embodiment, the recommendation unit 7 outputs the dish candidates 25a to 25c based on the eating habits of other users P1, P2, ..., but is not limited thereto, as long as the dish candidates 25a to 25c can be output. For example, the recommendation unit 7 may select the dish candidates 25a to 25c from the analysis data 16a representing the eating habits of the user P based on the characteristic index 17. For example, the recommendation unit 7 may select the dish candidates 25a to 25c in the analysis data 16a in descending order of the value of the characteristic index 17.

[0084] In addition, in the present embodiment, the dish recommendation system 3 is configured on the server 2, but the present invention is not limited thereto, as long as it can output dish candidates. For example, the dish recommendation system 3 can also be configured on the terminal 1.

[0085] In addition, in the present embodiment, the present disclosure is described using the names of dishes or ingredients of Japanese food, but is not limited thereto. For example, the names of dishes or ingredients may be changed in accordance with the food culture of the country in which the dish recommendation system, dish recommendation method, and program are used.

[0086] Although specific examples of the present disclosure have been described in detail above, these specific examples are merely illustrative and do not limit the claims. The technology described in the claims includes various modifications and changes made to the specific examples described above.

[0087] This application is based on the Japanese patent application (Japanese Patent Application No. 2023-051611) filed on March 28, 2023, the contents of which are incorporated herein by reference.

[0088] Industrial Applicability

[0089] The dish recommendation system disclosed in the present invention can be used in a system that proposes dish candidates to users.

[0090] Description of Reference Numerals

[0091] 1 Terminal

[0092] 2 Server

[0093] 3. Dish Recommendation System

[0094] 4. Acquisition

[0095] 5 Storage

[0096] 6 Extraction Department

[0097] 7 Recommended

[0098] 8 Analysis and processing department

[0099] 9 Extraction Processing Unit

[0100] 11 Storage Devices

[0101] 12 Processor

[0102] 13 UI Devices

[0103] 14 communication devices

[0104] 15 Event Data

[0105] 16, 16a Analyzing data

[0106] 17 Characteristics

[0107] 18a, 18b Event data

[0108] 21a, 21b Analyze data

[0109] 22 Comparative Data

[0110] 23 Variable Values

[0111] 24 Variable Value

[0112] 25a~25c Dish Candidates

[0113] 26a~26c Reference dishes

[0114] 27 Conditions related information

[0115] 28 Proposed data

[0116] 29 Specific taste sensation information

[0117] 30 nodes

[0118] 31 Edge

[0119] 32 Eating habits data

[0120] 33 Path

[0121] P User

[0122] P1, P2 Other users

Claims

1. A dish recommendation system, It is characterized in that include: an acquisition unit, which pre-acquires event data including identification information of dishes eaten by the user, dish category information, and eating time information, wherein the dish category information includes taste sensation information for classifying the dishes according to taste sensation; an extraction unit, which calculates a characteristic index including at least one of support, confidence, and lift related to the user's eating habits based on the event data through time series association analysis, and extracts a specific characteristic index corresponding to the specific dish category information input by the user; and The recommendation unit outputs menu candidates to be proposed to the user based on the specific characteristic index.

2. The dish recommendation system according to claim 1, in, The taste sensation information includes information indicating onomatopoeia and mimetic words, which are onomatopoeia and mimetic words that express the sensation of taste.

3. The dish recommendation system according to claim 1, in, The event data includes the ingredient information of the dish that the user has eaten.

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

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

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

7. The dish recommendation system according to claim 1, in, The recommendation unit outputs the eating habit data in which the temporal relationship of the event data is expressed by nodes and edges based on the specific characteristic index.

8. A method for recommending dishes. It is characterized in that The following steps are involved: Acquire in advance event data including identification information of dishes eaten by the user, dish category information, and eating time information, wherein the dish category information includes taste sensation information for classifying the dishes according to taste sensation; Based on the event data, calculating a characteristic index including at least one of support, confidence, and lift related to the user's eating habits through time series association analysis, and extracting a specific characteristic index corresponding to the specific dish category information input by the user; and Based on the specific characteristic index, menu candidates proposed to the user are output.

9. A procedure, It is characterized in that Causes the computer to perform the following processing: Acquire in advance event data including identification information of dishes eaten by the user, dish category information, and eating time information, wherein the dish category information includes taste sensation information for classifying the dishes according to taste sensation; Based on the event data, calculating a characteristic index including at least one of support, confidence, and lift related to the user's eating habits through time series association analysis, and extracting a specific characteristic index corresponding to the specific dish category information input by the user; and Based on the specific characteristic index, menu candidates proposed to the user are output.

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