US-20260127654-A1 - FOOD RECOMMENDATION SYSTEM, FOOD 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.

Inventors

• Isao Ueda

Assignees

• PANASONIC INTELLECTUAL PROPERTY MANAGEMENT CO., LTD.

Dates

Publication Date

20260507

Application Date

20240314

Priority Date

20230328

Claims (9)

1. 1 . A food recommendation system comprising: an acquirer that acquires in advance event data including dish identification information on a dish a user has eaten, dish type information including taste sensation information that classifies the dish by taste sensation, and meal time information; an extractor that extracts a specific characteristic index corresponding to specific dish type information input from the user by calculating a characteristic index including at least one of a support level, a confidence level and a lift value related to eating habits of the user through time-series association analysis based on the event data; and a recommender that outputs a dish candidate to be suggested to the user based on the specific characteristic index.
2. 2 . The food recommendation system according to claim 1 , wherein the taste sensation information includes onomatopoeia information representing the taste sensation.
3. 3 . The food recommendation system according to claim 1 , wherein the event data includes ingredient information on the dish the user has eaten.
4. 4 . The food recommendation system according to claim 3 , wherein the event data includes at least one of the dish identification information and the ingredient information, and the dish type information that are associated with each other.
5. 5 . The food recommendation system according to claim 1 , wherein the extractor extracts the specific characteristic index by further using at least one of avoided ingredient information and preference information of the user.
6. 6 . The food recommendation system according to claim 1 , wherein the recommender analyzes characteristics of eating habits of another user, and searches for the dish candidate based on the specific characteristic index of the user and a characteristic index of the another user.
7. 7 . The food recommendation system according to claim 1 , wherein the recommender outputs eating habit data that represents a time relationship of the event data by using a node and an edge based on the specific characteristic index.
8. 8 . A food recommendation method comprising: acquiring in advance event data including dish identification information on a dish a user has eaten, dish type information including taste sensation information that classifies the dish by taste sensation, and meal time information; extracting a specific characteristic index corresponding to specific dish type information input from the user by calculating a characteristic index including at least one of a support level, a confidence level and a lift value related to eating habits of the user through time-series association analysis based on the event data; and outputting a dish candidate to be suggested to the user based on the specific characteristic index.
9. 9 . A program that causes a computer to execute: acquiring in advance event data including dish identification information on a dish a user has eaten, dish type information including taste sensation information that classifies the dish by taste sensation, and meal time information; extracting a specific characteristic index corresponding to specific dish type information input from the user by calculating a characteristic index including at least one of a support level, a confidence level and a lift value related to eating habits of the user through time-series association analysis based on the event data; and outputting a dish candidate to be suggested to the user based on the specific characteristic index.

Description

TECHNICAL FIELD The present disclosure relates to a food recommendation system, a food recommendation method and a program. BACKGROUND ART Conventionally, food recommendation systems that suggest dishes based on the use's meal history have been proposed. For example, PTL 1 discloses a dish proposal device that suggests dishes to be eaten next by using meal patterns, preferences, food amounts and the like based on past meal histories. CITATION LIST Patent Literature PTL 1 Japanese Patent Application Laid-Open No. 2022-124701 SUMMARY OF INVENTION Technical Problem However, since the device in PTL 1 automatically suggests dishes without confirming the user's requests, there is a risk that dishes different from what the user wanted to eat would be suggested. An object of the present disclosure is to provide a food recommendation system that suggests appropriate dishes to the user. Solution to Problem A food recommendation system according to the present disclosure includes: an acquirer that acquires in advance event data including dish identification information on a dish a user has eaten, dish type information including taste sensation information that classifies the dish by taste sensation, and meal time information; an extractor that extracts a specific characteristic index corresponding to specific dish type information input from the user by calculating a characteristic index including at least one of a support level, a confidence level and a lift value related to eating habits of the user through time-series association analysis based on the event data; and a recommender that outputs a dish candidate to be suggested to the user based on the specific characteristic index. A food recommendation method according to the present disclosure includes: acquiring in advance event data including dish identification information on a dish a user has eaten, dish type information including taste sensation information that classifies the dish by taste sensation, and meal time information: extracting a specific characteristic index corresponding to specific dish type information input from the user by calculating a characteristic index including at least one of a support level, a confidence level and a lift value related to eating habits of the user through time-series association analysis based on the event data; and outputting a dish candidate to be suggested to the user based on the specific characteristic index. A program according to the present disclosure causes a computer to execute: acquiring in advance event data including dish identification information on a dish a user has eaten, dish type information including taste sensation information that classifies the dish by taste sensation, and meal time information: extracting a specific characteristic index corresponding to specific dish type information input from the user by calculating a characteristic index including at least one of a support level, a confidence level and a lift value related to eating habits of the user through time-series association analysis based on the event data; and outputting a dish candidate to be suggested to the user based on the specific characteristic index. Advantageous Effects of Invention According to the present disclosure, appropriate dishes can be suggested to the user. BRIEF DESCRIPTION OF DRAWINGS FIG. 1 is a diagram illustrating an overview of an embodiment of the present disclosure; FIG. 2 is a block diagram illustrating a functional configuration of a food recommendation system according to an embodiment of the present disclosure; FIG. 3 is a block diagram illustrating a hardware configuration of a food recommendation system according to an embodiment of the present disclosure; FIG. 4 is a flowchart illustrating an operation of an embodiment of the present disclosure; FIG. 5 is a diagram illustrating dish type information determined on the basis of ingredient information; FIG. 6 is a diagram illustrating event data; FIG. 7 is a diagram illustrating analysis data obtained through time-series association analysis on event data of the user; FIG. 8 is a diagram illustrating analysis data extracted on the basis of specific dish type information. FIG. 9 is a diagram illustrating a state where time-series association analysis is performed on event data of another user: FIG. 10 is a diagram illustrating comparison data obtained by comparing characteristic indices of the user and another user; FIG. 11 is a diagram illustrating suggestion data including dish candidates output from a recommender; and FIG. 12 is a diagram illustrating eating habit data output from the recommender. DESCRIPTION OF EMBODIMENTS Embodiments of the present disclosure are described below with reference to the drawings. An overview of the present disclosure is described below. For example, as illustrated in FIG. 1, when user's meal data (e.g. images of foods, etc.) is input, user terminal 1 sequentially transmits the meal data to server 2. Here, server 2 inc