Recipe recommendation method, recipe recommendation apparatus, and readable storage medium

Abstract

The present application provides a recipe recommendation method, a recipe recommendation apparatus, and a readable storage medium. The recipe recommendation method comprises: acquiring preparation condition information for a cooking process; acquiring first demand information of a user; and generating a recommendation list on the basis of the preparation condition information and the first demand information, wherein the preparation condition information comprises at least one of cooking device type information, food ingredient type information and food ingredient remaining amount information which are used for the cooking process.

Description

Recipe recommendation method, recipe recommendation device and readable storage medium

[0001] This application claims priority to the Chinese patent application filed with the State Intellectual Property Office of China on June 30, 2023, with application number "202310788360.1" and invention name "Recipe recommendation method, recipe recommendation device and readable storage medium", the entire contents of which are incorporated by reference into this application. Technical Field

[0002] The present application relates to the technical field of household appliances, and in particular to a recipe recommendation method, a recipe recommendation device, and a readable storage medium. Background Art

[0003] In the related art, during the recipe recommendation process, recipes can be recommended based on the demand information input by the user. However, while the recommended recipes meet the user's needs, the user is often unable to implement the selected recipes, which affects the user experience.

[0004] Summary of the Invention

[0005] This application aims to solve at least one of the technical problems existing in the prior art.

[0006] To this end, the first aspect of the present application provides a recipe recommendation method.

[0007] A second aspect of the present application provides a recipe recommendation device.

[0008] A third aspect of the present application provides a recipe recommendation device.

[0009] A fourth aspect of the present application provides a readable storage medium.

[0010] The first aspect of the present application provides a recipe recommendation method, comprising: obtaining production condition information used for a cooking process; obtaining a user's first demand information; generating a recommendation list based on the production condition information and the first demand information; wherein the production condition information includes at least one of cooking equipment type information, ingredient type information, and ingredient remaining amount information used for the cooking process.

[0011] The recipe recommendation method provided in this application can be used to recommend recipes corresponding to user requirements to meet the user's cooking needs. Specifically, the user can input cooking requirements through a smart device, and then the smart device can recommend a recipe list to the user based on the user's requirements, so that the user can select the required recipe from the recipe list.

[0012] Specifically, first, the production condition information used in the cooking process is obtained. The production condition information may include information on the types of cooking equipment used in the cooking process. For example, the types of cooking equipment included in the user's kitchen include rice cookers, ovens, microwave ovens, or electric ovens. Furthermore, the production condition information may also include information on the types of ingredients, that is, the different ingredients available to the user. For example, the different types of ingredients in the user's kitchen refrigerator, such as chicken, pork, fish, or different types of vegetables. Furthermore, the production condition information may also include information on the remaining amount of ingredients, that is, the remaining amount of ingredients available to the user. For example, the remaining amount of chicken or various vegetables in the user's kitchen refrigerator.

[0013] By obtaining the production condition information, it is possible to select recipes from the recipe list based on the user's production condition information during the recipe recommendation process, thereby ensuring that the recipe selected by the user can be achieved under the current production conditions, thereby improving the accuracy of recipe recommendations.

[0014] Among them, the production condition information can be obtained in the smart ecological environment established by the user's home electrical appliances. Specifically, the smart ecological environment refers to the smart ecological environment that will be formed on the basis of the user's use of a set of smart connected devices. All smart device information can be obtained from the unified control center. Each smart device can transmit device information, device operating status information and various other information in the user's home to the control terminal through the local area network, such as temperature, humidity, air quality, and information such as the ingredients contained in the refrigerator and the shelf life, so as to facilitate unified management of the user's home environment. In this way, before recommending a recipe, the production condition information during the user's cooking process can be determined from the smart ecological environment.

[0015] Furthermore, the user's first requirement information is obtained. This information refers to the user's preference for the dish to be prepared. Specifically, it may include the type of dish, such as cold dishes, fried foods, or baked foods. It may also include the flavor of the dish, such as sweet or spicy. The first requirement information may also include basic user information, such as the user's age and gender.

[0016] Furthermore, after obtaining the production condition information for the cooking process and the first requirement information input by the user, a recommendation list can be generated based on the production condition information and the first requirement information. In this way, the user can select the food he wants to make from the generated recommendation list. By generating a recommendation list based on the production condition information and the first requirement information input by the user, on the one hand, the user does not need to perform manual screening, and the automatic generation of the recommendation list of recipes is realized, thereby improving the efficiency of recipe recommendations. On the other hand, while meeting the user's needs, it can be combined with the user's kitchen production condition information to ensure that all recipes in the recommendation list obtained by the user can be realized, thereby ensuring the accuracy of the recipe recommendation and improving the user experience.

[0017] The recipe recommendation method provided by this application first obtains the production condition information used in the cooking process during the recipe recommendation process, thereby ensuring that the recipes recommended to the user can be implemented in the user's kitchen environment. The production condition information is further combined with the first requirement information input by the user to generate a recommendation list. This can not only automatically generate the recommendation list, improving the efficiency of recipe recommendations, but also ensure that the recommendation list can meet the user's needs while combining the production condition information of the user's kitchen to ensure that all recipes in the recommendation list obtained by the user can be implemented, thereby ensuring the accuracy of the recipe recommendations and improving the user experience.

[0018] In addition, the recipe recommendation method according to the above technical solution provided by this application may also have the following additional technical features:

[0019] In the above technical solution, optionally, the recommendation list includes a first recommendation list, and the recommendation list is generated according to the production condition information and the first requirement information, including: generating a recipe list according to the production condition information; determining the first keyword according to the first requirement information; and generating the first recommendation list according to the first keyword and the recipe list.

[0020] In this technical solution, when generating a recommendation list based on the preparation condition information and the user's first requirement information, a recipe list is first generated based on the preparation condition information. That is, recipes that meet the user's current preparation conditions are first screened out, and the recipes displayed in the recipe list are all achievable.

[0021] Then, a first keyword is determined based on the first demand information input by the user, that is, a keyword that can represent the actual demand of the user is extracted from the first demand information input by the user.

[0022] Furthermore, based on the first keyword, the previously generated recipe list is filtered to obtain a first recommendation list. The first recommendation list is a recipe list recommended to the user based on the production condition information and the user's first demand information, so that the user can select the required recipe in the recommendation list.

[0023] In some technical solutions, optionally, a first recommendation list is generated based on the first keyword and the recipe list, including: determining a second keyword in the first keyword that conflicts with the production condition information; when the second keyword is obtained, obtaining confirmation information input by the user; when the confirmation information is obtained, generating a first recommendation list based on the first keyword and the recipe list; when the confirmation information is not obtained, deleting the second keyword in the first keyword; and generating a first recommendation list based on the remaining first keywords and the recipe list.

[0024] In this technical solution, after determining the corresponding first keyword according to the first demand information input by the user, it is first determined whether there is a second keyword in the first keyword that conflicts with the production condition information.

[0025] Furthermore, if the second keyword is obtained, the user's confirmation information is required. This confirmation information is used to inquire the user whether to continue generating a recommendation list based on the keyword as a user requirement. Specifically, if the user's confirmation information is obtained, it indicates that the user still needs to use the keyword as a user requirement for recipe recommendations. In this case, no further operations are required, and the first recommendation list is directly generated based on the first keyword and recipe list.

[0026] If no user confirmation is received, it indicates that the user no longer needs to use the keyword as requirement information to generate a recommendation list. In this case, the second keyword in the first keyword is deleted, that is, the second keyword in the first keyword that conflicts with the preparation condition information is deleted. Then, the first recommendation list is generated based on the remaining first keyword and the recipe list. This ensures that all recipes in the generated first recommendation list are achievable, improving the user experience.

[0027] In some technical solutions, optionally, determining the first keyword according to the first demand information includes: determining, according to the demand information, a first keyword corresponding to the first demand information from a preset keyword list.

[0028] In this technical solution, in the process of determining the first keyword according to the first demand information input by the user, the first keyword corresponding to the first demand information input by the user can be determined from a preset keyword list.

[0029] By determining the first keyword corresponding to the first demand information input by the user in the preset keyword list, the first keyword can be determined intuitively, thereby improving the efficiency of determining the first keyword.

[0030] In some technical solutions, optionally, determining the first keyword based on the first demand information includes: inputting the first demand information into a pre-trained deep learning algorithm to generate a first keyword corresponding to the first demand information.

[0031] In this technical solution, in the process of determining the first keyword based on the first demand information input by the user, the first demand information input by the user can be directly input into a pre-trained deep learning algorithm, and then the first keyword corresponding to the first demand information is obtained through calculation of the deep learning algorithm.

[0032] Specifically, the deep learning algorithm can be a keyword extraction algorithm or an intent recognition model.

[0033] By inputting the first demand information into a pre-trained deep learning algorithm and determining the first keyword corresponding to the first demand information, the accuracy of determining the first keyword can be improved, thereby ensuring the accuracy of obtaining user needs, and then ensuring that the recipes in the generated first recommendation list are more in line with user needs, thereby improving user experience.

[0034] In some technical solutions, optionally, the recommendation list also includes a second recommendation list. After generating the first recommendation list based on the first keyword and the recipe list, the recipe recommendation method also includes: obtaining the user's second demand information; upon receiving the second demand information input by the user, determining the third keyword based on the second demand information; generating the second recommendation list based on the third keyword and the recipe list; and stopping obtaining the second demand information when the user determines the target recipe from the first recommendation list.

[0035] In this technical solution, after the first recommendation list is generated according to the first keyword and production condition information corresponding to the user's first demand information, the second demand information input by the user may be further obtained.

[0036] That is, if the user's desired target recipe does not exist in the first recommendation list, that is, if the user does not select the desired target recipe from the first recommendation list, the user can continue to enter second requirement information, thereby determining a third keyword corresponding to the second requirement information based on the user's second requirement information, and then generating a second recommendation list based on the third keyword and the generated recipe list. At this time, the user can select the target recipe of their own choice from the second recommendation list.

[0037] By obtaining the user's second demand information, if the recommendation list generated based on the user's first demand information and production condition information does not meet the user's needs, a recommendation list can be further generated based on the user's second demand information and production condition information to ensure that the generated recommendation list contains recipes that meet the user's needs, thereby improving the user experience.

[0038] Accordingly, when the user determines the target recipe from the first recommendation list, there is no need to further obtain the user's second requirement information, and the recipe recommendation is completed.

[0039] In some technical solutions, optionally, after generating a recommendation list based on the production condition information and the first requirement information, the recommendation method further includes: obtaining the user's historical cooking information; determining the priority of the recipes in the recommendation list based on the historical cooking information; and sorting the recipes in the recommendation list according to the priority.

[0040] In this technical solution, after generating a recommendation list based on the production condition information and the user's first demand information, the multiple recipes in the recommendation list can also be sorted according to the user's historical cooking information. The user's historical cooking information can reflect the user's cooking preferences, and the multiple recipes in the recommendation list can be further sorted according to the user's preferences, making it easier for the user to view and select.

[0041] Specifically, first, the user's historical cooking information is obtained, wherein the historical cooking information may include the user's historical recipe interaction behavior, such as which recipes the user has cooked and the number of times the user has cooked the recipe.

[0042] Then, based on the user's historical cooking information, the priorities of all recipes in the generated recommendation list are determined, and then all recipes in the recommendation list are sorted according to the priorities for the user to view.

[0043] Furthermore, all recipes in the recommendation list can be sorted based on other information. For example, the priority of all recipes in the recommendation list can be determined based on the user's taste preferences. Alternatively, the recipe sorting method can be determined based on the similarity of the main ingredients of all recipes in the recommendation list. For example, recipes with the same or similar main ingredients can be grouped together to facilitate user selection based on the main ingredients. Alternatively, the recipe sorting method can be determined based on the similarity of the taste of all recipes in the recommendation list. For example, recipes with the same or similar taste can be grouped together to facilitate user selection based on the main ingredients.

[0044] In some technical solutions, optionally, the priority of the recipes in the recommendation list is determined based on the historical cooking information, including: constructing a historical cooking information matrix based on the historical cooking information; and inputting the historical cooking information matrix into a preset algorithm to generate the priority of the recipes in the recommendation list.

[0045] In this technical solution, a historical cooking information matrix can be constructed based on the user's historical cooking information. Specifically, the user's historical recipe interaction behavior, such as which recipes the user has cooked and how many times the user has cooked a recipe, can be used to construct the historical cooking information matrix. This historical cooking information matrix is ​​then input into a preset algorithm, which then determines the priority of the recipes in the recommendation list.

[0046] Specifically, the preset algorithm may be a collaborative filtering algorithm.

[0047] In some technical solutions, optionally, obtaining the user's first demand information includes: obtaining voice information input by the user; converting the voice information into text information; and determining the first demand information based on the text information.

[0048] In this technical solution, the user can interact with the smart device through voice, so that the smart device can determine the user's first demand information based on the voice information input by the user.

[0049] Specifically, the system first obtains the user's voice input, then converts the user's voice input into text information, and finally determines the user's primary need information based on the text information. Through voice interaction, users can easily express their real-time recipe search needs, realizing convenient user recipe search need interaction.

[0050] According to the second aspect of the present application, a recipe recommendation device is proposed, including: an acquisition unit for acquiring production condition information used for a cooking process; and acquiring a user's first demand information; a generation unit for generating a recommendation list based on the production condition information and the first demand information; wherein the production condition information includes at least one of cooking equipment type information, ingredient type information and ingredient remaining amount information used for the cooking process.

[0051] The recipe recommendation device provided by the present application first obtains the production condition information used for the cooking process during the recipe recommendation process, thereby ensuring that the recipes recommended to the user can be implemented in the user's kitchen environment. The production condition information is further combined with the first requirement information input by the user to generate a recommendation list. This can not only realize the automatic generation of the recommendation list and improve the efficiency of recipe recommendation, but also enable the recommendation list to meet the user's needs while combining the production condition information of the user's kitchen to ensure that all recipes in the recommendation list obtained by the user can be implemented, thereby ensuring the accuracy of the recipe recommendation and improving the user experience.

[0052] Furthermore, the generating unit is specifically configured to: generate a recipe list according to the production condition information; determine a first keyword according to the first demand information; and generate a first recommendation list according to the first keyword and the recipe list.

[0053] Furthermore, the generation unit is specifically used to: determine the second keyword that conflicts with the production condition information in the first keyword; the acquisition unit is also used to: obtain confirmation information input by the user when the second keyword is obtained; the generation unit is specifically used to: generate a first recommendation list based on the first keyword and the recipe list when the confirmation information is obtained; delete the second keyword in the first keyword when the confirmation information is not obtained; and generate a first recommendation list based on the remaining first keywords and the recipe list.

[0054] Furthermore, the generating unit is further configured to determine, according to the demand information, a first keyword corresponding to the first demand information from a preset keyword list.

[0055] Furthermore, the generating unit is specifically configured to input the demand information into a pre-trained deep learning algorithm to generate a first keyword corresponding to the first demand information.

[0056] Furthermore, the acquisition unit is further configured to acquire second demand information from the user; the generation unit is further configured to, upon receiving the second demand information input by the user, determine a third keyword based on the second demand information; and generate a second recommendation list based on the third keyword and the first recommendation list. The acquisition unit stops acquiring the second demand information when the user determines the target recipe from the first recommendation list.

[0057] Furthermore, after generating the recommendation list according to the preparation condition information and the first requirement information, the acquisition unit is further configured to acquire the user's historical cooking information.

[0058] The recipe recommendation device further includes a sorting unit, which is used to determine the priority of the recipes in the recommendation list based on the historical cooking information; and sort the recipes in the recommendation list according to the priority.

[0059] Furthermore, the sorting unit is specifically configured to construct a historical cooking information matrix based on the historical cooking information; and input the historical cooking information matrix into a preset algorithm to generate a priority of recipes in the recommendation list.

[0060] Furthermore, the acquisition unit is specifically configured to acquire voice information input by the user; convert the voice information into text information; and determine the first demand information based on the text information.

[0061] According to the third aspect of the present application, a recipe recommendation device is proposed, comprising: a processor and a memory, wherein the memory stores programs or instructions that can be run on the processor, and when the programs or instructions are executed by the processor, the steps of the recipe recommendation method provided in the first aspect are implemented.

[0062] The recipe recommendation device provided in the present application includes a memory and a processor, and also includes a program or instruction stored in the memory. When the program or instruction is executed by the processor, it can implement the steps of the recipe recommendation method of the first aspect mentioned above. Therefore, the recipe recommendation device has all the beneficial effects of the above-mentioned recipe recommendation method, which will not be repeated here.

[0063] According to a fourth aspect of the present application, a readable storage medium is proposed, on which a program or instruction is stored. When the program or instruction is executed by a processor, a recipe recommendation method as described in any one of the above technical solutions is implemented.

[0064] The readable storage medium provided in this application stores a program or instruction thereon. When the program or instruction is executed by the processor, it can implement a recipe recommendation method such as any one of the above technical solutions. Therefore, the storage medium has all the beneficial effects of the above recipe recommendation method, which will not be repeated here.

[0065] Additional aspects and advantages of the present application will become apparent in the following description or may be learned through practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which:

[0067] FIG1 is a schematic diagram showing a process of a recipe recommendation method according to an embodiment of the present application;

[0068] FIG2 is a schematic diagram showing a process flow of a recipe recommendation method according to another embodiment of the present application;

[0069] FIG3 shows a schematic flow chart of a recipe recommendation method according to yet another embodiment of the present application;

[0070] FIG4 shows a schematic flow chart of a recipe recommendation method according to yet another embodiment of the present application;

[0071] FIG5 is a schematic diagram showing a flow chart of a recipe recommendation method according to yet another embodiment of the present application;

[0072] FIG6 shows a flowchart of a recipe recommendation method according to yet another embodiment of the present application;

[0073] FIG7 shows a schematic flow chart of a recipe recommendation method according to yet another embodiment of the present application;

[0074] FIG8 is a schematic diagram showing a flow chart of a recipe recommendation method according to yet another embodiment of the present application;

[0075] FIG9 shows a flowchart of a recipe recommendation method according to an embodiment of the present application. DETAILED DESCRIPTION

[0076] In order to more clearly understand the above-mentioned objects, features and advantages of the present application, the present application is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features therein can be combined with each other in the absence of conflict.

[0077] In the following description, many specific details are set forth to facilitate a full understanding of the present application. However, the present application may also be implemented in other ways different from those described herein. Therefore, the scope of protection of the present application is not limited to the specific embodiments disclosed below.

[0078] The following describes a recipe recommendation method, a recipe recommendation device, and a readable storage medium provided according to some embodiments of the present application with reference to Figures 1 to 9.

[0079] As shown in FIG1 , according to one embodiment of the present application, a recipe recommendation method is proposed, including:

[0080] S102, obtaining preparation condition information for the cooking process;

[0081] S104, obtaining the user's first demand information;

[0082] S106: Generate a recommendation list based on the production condition information and the first requirement information.

[0083] The preparation condition information includes at least one of the type information of cooking equipment used in the cooking process, the type information of ingredients, and the remaining amount information of ingredients.

[0084] The recipe recommendation method provided in this application can be used to recommend recipes corresponding to user requirements to meet the user's cooking needs. Specifically, the user can input cooking requirements through a smart device, and then the smart device can recommend a recipe list to the user based on the user's requirements, so that the user can select the required recipe from the recipe list.

[0085] Specifically, first, the production condition information used in the cooking process is obtained. The production condition information may include information on the types of cooking equipment used in the cooking process. For example, the types of cooking equipment included in the user's kitchen include rice cookers, ovens, microwave ovens, or electric ovens. Furthermore, the production condition information may also include information on the types of ingredients, that is, the different ingredients available to the user. For example, the different types of ingredients in the user's kitchen refrigerator, such as chicken, pork, fish, or different types of vegetables. Furthermore, the production condition information may also include information on the remaining amount of ingredients, that is, the remaining amount of ingredients available to the user. For example, the remaining amount of chicken or various vegetables in the user's kitchen refrigerator.

[0086] By obtaining the production condition information, it is possible to select recipes from the recipe list based on the user's production condition information during the recipe recommendation process, thereby ensuring that the recipe selected by the user can be achieved under the current production conditions, thereby improving the accuracy of recipe recommendations.

[0087] Among them, the production condition information can be obtained in the smart ecological environment established by the user's home electrical appliances. Specifically, the smart ecological environment refers to the smart ecological environment that will be formed on the basis of the user's use of a set of smart connected devices. All smart device information can be obtained from the unified control center. Each smart device can transmit device information, device operating status information and various other information in the user's home to the control terminal through the local area network, such as temperature, humidity, air quality, and information such as the ingredients contained in the refrigerator and the shelf life, so as to facilitate unified management of the user's home environment. In this way, before recommending a recipe, the production condition information during the user's cooking process can be determined from the smart ecological environment.

[0088] Alternatively, the cooking condition information can also be obtained by receiving active input from the user about existing cooking equipment conditions and regularly updating the food management table to ensure that the system can obtain the cooking condition information.

[0089] Furthermore, the user's first requirement information is obtained. This information refers to the user's preference for the dish to be prepared. Specifically, it may include the type of dish, such as cold dishes, fried foods, or baked foods. It may also include the flavor of the dish, such as sweet or spicy. The first requirement information may also include basic user information, such as the user's age and gender.

[0090] Furthermore, after obtaining the production condition information for the cooking process and the first requirement information input by the user, a recommendation list can be generated based on the production condition information and the first requirement information. In this way, the user can select the food he wants to make from the generated recommendation list. By generating a recommendation list based on the production condition information and the first requirement information input by the user, on the one hand, the user does not need to perform manual screening, and the automatic generation of the recommendation list of recipes is realized, thereby improving the efficiency of recipe recommendations. On the other hand, while meeting the user's needs, it can be combined with the user's kitchen production condition information to ensure that all recipes in the recommendation list obtained by the user can be realized, thereby ensuring the accuracy of the recipe recommendation and improving the user experience.

[0091] The recipe recommendation method provided by this application first obtains the production condition information used in the cooking process during the recipe recommendation process, thereby ensuring that the recipes recommended to the user can be implemented in the user's kitchen environment. The production condition information is further combined with the first requirement information input by the user to generate a recommendation list. This can not only automatically generate the recommendation list, improving the efficiency of recipe recommendations, but also ensure that the recommendation list can meet the user's needs while combining the production condition information of the user's kitchen to ensure that all recipes in the recommendation list obtained by the user can be implemented, thereby ensuring the accuracy of the recipe recommendations and improving the user experience.

[0092] According to one embodiment of the present application, as shown in FIG2 , a recipe recommendation method is proposed, including:

[0093] S202, obtaining preparation condition information for the cooking process;

[0094] S204, obtaining the user's first demand information;

[0095] S206, generating a recipe list based on the preparation condition information;

[0096] S208, determining a first keyword based on the first demand information;

[0097] S210: Generate a first recommendation list based on the first keyword and the recipe list.

[0098] In this embodiment, when generating a recommendation list based on the preparation condition information and the user's first requirement information, a recipe list is first generated based on the preparation condition information. That is, recipes that meet the user's current preparation conditions are first screened out, and the recipes displayed in the recipe list are all achievable.

[0099] Then, a first keyword is determined based on the first demand information input by the user, that is, a keyword that can represent the actual demand of the user is extracted from the first demand information input by the user.

[0100] For example, the user's first demand information is "What should I eat today because I have a cold? I am also in a bad mood and want to eat something sweet, such as a dessert made from strawberries." At this time, the types of keywords can be determined as "common diseases," "mood," "taste," "dishes," and "ingredients" based on the first demand information, and the keywords of the first demand information can be further determined as "cold," "sadness," "sweet," "dessert," and "strawberry."

[0101] Furthermore, based on the first keyword, the previously generated recipe list is filtered to obtain a first recommendation list. The first recommendation list is a recipe list recommended to the user based on the production condition information and the user's first demand information, so that the user can select the required recipe in the recommendation list.

[0102] According to one embodiment of the present application, as shown in FIG3 , a recipe recommendation method is proposed, including:

[0103] S302, obtaining preparation condition information for the cooking process;

[0104] S304, obtaining the user's first demand information;

[0105] S306, generating a recipe list based on the preparation condition information;

[0106] S308, determining a first keyword based on the first demand information;

[0107] S310, determining a second keyword that conflicts with the production condition information among the first keywords;

[0108] S312, when the second keyword is obtained, obtaining confirmation information input by the user;

[0109] S314, when the confirmation information is obtained, generating a first recommendation list based on the first keyword and the recipe list;

[0110] S316, if no confirmation information is obtained, deleting the second keyword from the first keyword;

[0111] S318: Generate a first recommendation list based on the remaining first keywords and the recipe list.

[0112] In this embodiment, after determining the corresponding first keyword according to the first requirement information input by the user, it is first determined whether there is a second keyword in the first keyword that conflicts with the production condition information.

[0113] For example, a user's first requirement is "What should I eat today? I have a cold, and I'm feeling down, so I want something sweet, especially a dessert made with strawberries." Based on this first requirement, the keyword types can be determined to be "common symptoms," "mood," "taste," "dish," and "ingredients." Furthermore, the keywords for this first requirement are determined to be "cold," "sadness," "sweet," "dessert," and "strawberry." However, the ingredient "strawberry" is not found in the production condition information. Therefore, it can be determined that "strawberry" in the first keyword is the second keyword that conflicts with the production condition information.

[0114] Furthermore, if the second keyword is obtained, the user's confirmation information is required. This confirmation information is used to inquire the user whether to continue generating a recommendation list based on the keyword as a user requirement. Specifically, if the user's confirmation information is obtained, it indicates that the user still needs to use the keyword as a user requirement for recipe recommendations. In this case, no further operations are required, and the first recommendation list is directly generated based on the first keyword and recipe list.

[0115] If no user confirmation is received, it indicates that the user no longer needs to use the keyword as requirement information to generate a recommendation list. In this case, the second keyword in the first keyword is deleted, that is, the second keyword in the first keyword that conflicts with the preparation condition information is deleted. Then, the first recommendation list is generated based on the remaining first keyword and the recipe list. This ensures that all recipes in the generated first recommendation list are achievable, improving the user experience.

[0116] According to one embodiment of the present application, as shown in FIG4 , a recipe recommendation method is proposed, comprising:

[0117] S402, obtaining preparation condition information for the cooking process;

[0118] S404, obtaining the user's first demand information;

[0119] S406, generating a recipe list based on the preparation condition information;

[0120] S408, determining a first keyword corresponding to the first demand information from a preset keyword list according to the demand information;

[0121] S410: Generate a first recommendation list based on the first keyword and the recipe list.

[0122] In this embodiment, in the process of determining the first keyword according to the first demand information input by the user, the first keyword corresponding to the first demand information input by the user may be determined from a preset keyword list.

[0123] Specifically, in the first keyword list, multiple first keywords can be categorized according to keyword type. For example, keyword types can include "common symptoms," "mood," "taste," "dish," and "ingredients." Furthermore, each keyword type can include multiple specific first keywords. For example, in the "taste" type, first keywords such as "sour," "sweet," "spicy," and "salty" can be included.

[0124] By determining the first keyword corresponding to the first demand information input by the user in the preset keyword list, the first keyword can be determined intuitively, thereby improving the efficiency of determining the first keyword.

[0125] According to one embodiment of the present application, as shown in FIG5 , a recipe recommendation method is proposed, including:

[0126] S502, obtaining preparation condition information for the cooking process;

[0127] S504, obtaining the user's first demand information;

[0128] S506, generating a recipe list based on the preparation condition information;

[0129] S508 , inputting the first demand information into a pre-trained deep learning algorithm to generate a first keyword corresponding to the first demand information;

[0130] S510: Generate a first recommendation list based on the first keyword and the recipe list.

[0131] In this embodiment, in the process of determining the first keyword based on the first demand information input by the user, the first demand information input by the user can be directly input into a pre-trained deep learning algorithm, and then the first keyword corresponding to the first demand information is obtained through calculation of the deep learning algorithm.

[0132] Specifically, the deep learning algorithm can be a keyword extraction algorithm or an intent recognition model.

[0133] By inputting the first demand information into a pre-trained deep learning algorithm and determining the first keyword corresponding to the first demand information, the accuracy of determining the first keyword can be improved, thereby ensuring the accuracy of obtaining user needs, and then ensuring that the recipes in the generated first recommendation list are more in line with user needs, thereby improving user experience.

[0134] According to one embodiment of the present application, as shown in FIG6 , a recipe recommendation method is proposed, including:

[0135] S602, obtaining preparation condition information for the cooking process;

[0136] S604, obtaining the user's first demand information;

[0137] S606, generating a recipe list based on the preparation condition information;

[0138] S608, determining a first keyword based on the first demand information;

[0139] S610, generating a first recommendation list based on the first keyword and the recipe list;

[0140] S612, obtaining the user's second demand information;

[0141] S614, upon receiving the second demand information input by the user, determining a third keyword according to the second demand information;

[0142] S616: Generate a second recommendation list based on the third keyword and the recipe list.

[0143] In this embodiment, after the first recommendation list is generated according to the first keyword and the production condition information corresponding to the user's first demand information, the second demand information input by the user may be further obtained.

[0144] That is, if the user's desired target recipe does not exist in the first recommendation list, that is, if the user does not select the desired target recipe from the first recommendation list, the user can continue to enter second requirement information, thereby determining a third keyword corresponding to the second requirement information based on the user's second requirement information, and then generating a second recommendation list based on the third keyword and the generated recipe list. At this time, the user can select the target recipe of their own choice from the second recommendation list.

[0145] By obtaining the user's second demand information, if the recommendation list generated based on the user's first demand information and production condition information does not meet the user's needs, a recommendation list can be further generated based on the user's second demand information and production condition information to ensure that the generated recommendation list contains recipes that meet the user's needs, thereby improving the user experience.

[0146] Accordingly, when the user determines the target recipe from the first recommendation list, there is no need to further obtain the user's second requirement information, and the recipe recommendation is completed.

[0147] Furthermore, as shown in FIG9 , if the second recommendation list still does not contain the recipe required by the user, the user's third demand information can be obtained, and a fourth recommendation list can be generated based on the third demand information and the recipe list. In this way, the user's demand information can be obtained multiple times until the generated recommendation list contains the recipe required by the user, and there is no need to continue to obtain the user's demand information.

[0148] According to one embodiment of the present application, as shown in FIG7 , a recipe recommendation method is proposed, including:

[0149] S702, obtaining preparation condition information for the cooking process;

[0150] S704, obtaining the user's first demand information;

[0151] S706, generating a recommendation list based on the production condition information and the first requirement information;

[0152] S708, obtaining the user's historical cooking information;

[0153] S710, determining the priority of recipes in the recommendation list based on historical cooking information;

[0154] S712: Sort the recipes in the recommendation list according to priority.

[0155] In this embodiment, after generating a recommendation list based on the production condition information and the user's first demand information, the multiple recipes in the recommendation list can also be sorted according to the user's historical cooking information. The user's historical cooking information can reflect the user's cooking preferences, and the multiple recipes in the recommendation list can be further sorted according to the user's preferences, making it easier for the user to view and select.

[0156] Specifically, first, the user's historical cooking information is obtained, wherein the historical cooking information may include the user's historical recipe interaction behavior, such as which recipes the user has cooked and the number of times the user has cooked the recipe.

[0157] Then, based on the user's historical cooking information, the priorities of all recipes in the generated recommendation list are determined, and then all recipes in the recommendation list are sorted according to the priorities for the user to view.

[0158] Furthermore, all recipes in the recommendation list can be sorted based on other information. For example, the priority of all recipes in the recommendation list can be determined based on the user's taste preferences. Alternatively, the recipe sorting method can be determined based on the similarity of the main ingredients of all recipes in the recommendation list. For example, recipes with the same or similar main ingredients can be grouped together to facilitate user selection based on the main ingredients. Alternatively, the recipe sorting method can be determined based on the similarity of the taste of all recipes in the recommendation list. For example, recipes with the same or similar taste can be grouped together to facilitate user selection based on the main ingredients.

[0159] In some embodiments, optionally, the priority of the recipes in the recommendation list is determined based on the historical cooking information, including: constructing a historical cooking information matrix based on the historical cooking information; and inputting the historical cooking information matrix into a preset algorithm to generate the priority of the recipes in the recommendation list.

[0160] In this embodiment, a historical cooking information matrix can be constructed based on the user's historical cooking information. Specifically, the user's historical recipe interaction behavior, such as which recipes the user has cooked and how many times the user has cooked a recipe, can be used to construct the historical cooking information matrix. This historical cooking information matrix is ​​then input into a preset algorithm, which then determines the priority of the recipes in the recommendation list.

[0161] Specifically, the preset algorithm may be a collaborative filtering algorithm.

[0162] According to one embodiment of the present application, as shown in FIG8 , a recipe recommendation method is proposed, including:

[0163] S802, obtaining preparation condition information for the cooking process;

[0164] S804, obtaining voice information input by the user;

[0165] S806, converting the voice information into text information;

[0166] S808, determining first demand information based on the text information;

[0167] S810: Generate a recommendation list based on the production condition information and the first requirement information.

[0168] In this embodiment, the user can interact with the smart device through voice, so that the smart device can determine the user's first demand information based on the voice information input by the user.

[0169] Specifically, the system first obtains the user's voice input, then converts the user's voice input into text information, and finally determines the user's primary need information based on the text information. Through voice interaction, users can easily express their real-time recipe search needs, realizing convenient user recipe search need interaction.

[0170] According to the second aspect of the present application, a recipe recommendation device is proposed, including: an acquisition unit for acquiring production condition information used for a cooking process; and acquiring a user's first demand information; a generation unit for generating a recommendation list based on the production condition information and the first demand information; wherein the production condition information includes at least one of cooking equipment type information, ingredient type information and ingredient remaining amount information used for the cooking process.

[0171] The recipe recommendation device provided by the present application first obtains the production condition information used for the cooking process during the recipe recommendation process, thereby ensuring that the recipes recommended to the user can be implemented in the user's kitchen environment. The production condition information is further combined with the first requirement information input by the user to generate a recommendation list. This can not only realize the automatic generation of the recommendation list and improve the efficiency of recipe recommendation, but also enable the recommendation list to meet the user's needs while combining the production condition information of the user's kitchen to ensure that all recipes in the recommendation list obtained by the user can be implemented, thereby ensuring the accuracy of the recipe recommendation and improving the user experience.

[0172] Furthermore, the generating unit is specifically configured to: generate a recipe list according to the production condition information; determine a first keyword according to the first demand information; and generate a first recommendation list according to the first keyword and the recipe list.

[0173] Furthermore, the generation unit is specifically used to: determine the second keyword that conflicts with the production condition information in the first keyword; the acquisition unit is also used to: obtain confirmation information input by the user when the second keyword is obtained; the generation unit is specifically used to: generate a first recommendation list based on the first keyword and the recipe list when the confirmation information is obtained; delete the second keyword in the first keyword when the confirmation information is not obtained; and generate a first recommendation list based on the remaining first keywords and the recipe list.

[0174] Furthermore, the generating unit is further configured to determine, according to the demand information, a first keyword corresponding to the first demand information from a preset keyword list.

[0175] Furthermore, the generating unit is specifically configured to input the demand information into a pre-trained deep learning algorithm to generate a first keyword corresponding to the first demand information.

[0176] Furthermore, the acquisition unit is further configured to acquire second demand information from the user; the generation unit is further configured to, upon receiving the second demand information input by the user, determine a third keyword based on the second demand information; and generate a second recommendation list based on the third keyword and the first recommendation list. The acquisition unit stops acquiring the second demand information when the user determines the target recipe from the first recommendation list.

[0177] Furthermore, after generating the recommendation list according to the preparation condition information and the first requirement information, the acquisition unit is further configured to acquire the user's historical cooking information.

[0178] The recipe recommendation device further includes a sorting unit, which is used to determine the priority of the recipes in the recommendation list based on the historical cooking information; and sort the recipes in the recommendation list according to the priority.

[0179] Furthermore, the sorting unit is specifically configured to construct a historical cooking information matrix based on the historical cooking information; and input the historical cooking information matrix into a preset algorithm to generate a priority of recipes in the recommendation list.

[0180] Furthermore, the acquisition unit is specifically configured to acquire voice information input by the user; convert the voice information into text information; and determine the first demand information based on the text information.

[0181] According to the third aspect of the present application, a recipe recommendation device is proposed, comprising: a processor and a memory, wherein the memory stores programs or instructions that can be run on the processor, and when the programs or instructions are executed by the processor, the steps of the recipe recommendation method provided in the first aspect are implemented.

[0182] The recipe recommendation device provided in the present application includes a memory and a processor, and also includes a program or instruction stored in the memory. When the program or instruction is executed by the processor, it can implement the steps of the recipe recommendation method of the first aspect mentioned above. Therefore, the recipe recommendation device has all the beneficial effects of the above-mentioned recipe recommendation method, which will not be repeated here.

[0183] According to a fourth aspect of the present application, a readable storage medium is proposed, on which a program or instruction is stored. When the program or instruction is executed by a processor, a recipe recommendation method as described in any one of the above technical solutions is implemented.

[0184] The readable storage medium provided in this application stores a program or instruction thereon. When the program or instruction is executed by the processor, it can implement a recipe recommendation method such as any one of the above technical solutions. Therefore, the storage medium has all the beneficial effects of the above recipe recommendation method, which will not be repeated here.

[0185] In the description of this application, the term "plurality" refers to two or more than two. Unless otherwise expressly defined, the terms "upper" and "lower" indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings. They are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation. Therefore, they cannot be understood as limitations on this application. The terms "connect," "install," and "fix" should be understood in a broad sense. For example, "connection" can be a fixed connection, a detachable connection, or an integral connection; it can be a direct connection or an indirect connection through an intermediate medium. For those of ordinary skill in the art, the specific meanings of the above terms in this application can be understood according to the specific circumstances.

[0186] Throughout this specification, terms such as "one embodiment," "some embodiments," and "specific embodiments" mean that the specific features, structures, materials, or characteristics described in conjunction with that embodiment or example are included in at least one embodiment or example of the present application. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0187] The above are merely preferred embodiments of the present application and are not intended to limit the present application. Those skilled in the art will readily appreciate that various modifications and variations are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present application shall be included within the scope of protection of the present application.

Claims

1. A recipe recommendation method, wherein: include: Obtaining information about preparation conditions used in the cooking process; Obtain the user's first demand information; generating a recommendation list according to the production condition information and the first requirement information; The preparation condition information includes at least one of information on the type of cooking equipment used in the cooking process, information on the type of ingredients, and information on the remaining amount of ingredients.

2. The recipe recommendation method according to claim 1, wherein: The recommendation list includes a first recommendation list, and generating the recommendation list according to the production condition information and the first requirement information includes: Generate a recipe list according to the production condition information; Determining a first keyword according to the first demand information; The first recommendation list is generated according to the first keyword and the recipe list.

3. The recipe recommendation method according to claim 2, wherein: The step of generating the first recommendation list according to the first keyword and the recipe list includes: Determining, among the first keywords, a second keyword that conflicts with the production condition information; When the second keyword is obtained, obtaining confirmation information input by the user; When the confirmation information is obtained, generating the first recommendation list according to the first keyword and the recipe list; If the confirmation information is not obtained, deleting the second keyword from the first keyword; The first recommendation list is generated according to the remaining first keywords and the recipe list.

4. The recipe recommendation method according to claim 2, wherein: The determining the first keyword according to the first demand information includes: According to the demand information, the first keyword corresponding to the first demand information is determined from a preset keyword list.

5. The recipe recommendation method according to claim 2, wherein: The determining the first keyword according to the first demand information includes: The first demand information is input into a pre-trained deep learning algorithm to generate the first keyword corresponding to the first demand information.

6. The recipe recommendation method according to claim 2, wherein: The recommendation list further includes a second recommendation list. After the first recommendation list is generated according to the first keyword and the recipe list, the recipe recommendation method further includes: Acquiring second demand information of the user; In case of receiving the second demand information input by the user, determining a third keyword according to the second demand information; generating the second recommendation list according to the third keyword and the recipe list; When the user determines the target recipe from the first recommendation list, the acquisition of the second demand information is stopped.

7. The recipe recommendation method according to any one of claims 1 to 6, wherein: The generating a recommendation list according to the production condition information and the first requirement information, the recommendation method further comprises: Acquire the user's historical cooking information; Determining the priority of the recipes in the recommendation list according to the historical cooking information; The recipes in the recommendation list are sorted according to the priority.

8. The recipe recommendation method according to claim 7, wherein: Determining the priority of the recipes in the recommendation list according to the historical cooking information includes: Constructing a historical cooking information matrix according to the historical cooking information; The historical cooking information matrix is ​​input into a preset algorithm to generate the priority of the recipes in the recommendation list.

9. The recipe recommendation method according to any one of claims 1 to 6, wherein: The obtaining of the user's first demand information includes: Acquiring voice information input by the user; Converting the voice information into text information; The first requirement information is determined according to the text information.

10. A recipe recommendation device, wherein: include: An acquisition unit, used for acquiring preparation condition information for a cooking process; as well as Obtain the user's first demand information; A generating unit, configured to generate a recommendation list according to the production condition information and the first requirement information; The preparation condition information includes at least one of information on the type of cooking equipment used in the cooking process, information on the type of ingredients, and information on the remaining amount of ingredients.

11. A recipe recommendation device, wherein: include: A processor and a memory, wherein the memory stores a program or instruction that can be run on the processor, and when the program or instruction is executed by the processor, the steps of the recipe recommendation method as described in any one of claims 1 to 9 are implemented.

12. A readable storage medium, wherein: The readable storage medium stores a program or instruction, and when the program or instruction is executed by a processor, the steps of the recipe recommendation method according to any one of claims 1 to 9 are implemented.

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