Method and apparatus for recipe recommendation based on predetermined recipe

Abstract

The application discloses a kind of method and device for recipe recommendation based on predetermined recipe, the method includes: according to the recipe data of all recipes determined in advance and the first user information of target user with recipe recommendation demand, determine all pending recipes matched with target user, and determine the uploader corresponding to each pending recipe;For each pending recipe, it is judged whether the intimacy between the uploader of pending recipe and target user is greater than or equal to the preset intimacy threshold value, if yes, then determine the target recipe that pending recipe needs to be recommended to target user.It can be seen that the target recipe that needs to be recommended can be determined based on the intimacy between the uploader of each pending recipe and the target user by implementing the present application, which is beneficial to reducing the required recipe data by intelligent recipe screening, and further beneficial to improving the reliability and accuracy of the required recipe data, thereby improving the cooking experience of the user.

Description

Technical Field

[0001] This invention relates to the field of intelligent cooking technology, and in particular to a method and apparatus for recommending recipes based on pre-determined recipes. Background Technology

[0002] With the advancement of technology, smart cooking equipment is becoming increasingly popular. The emergence of smart cooking equipment has provided many conveniences for people's cooking life, allowing them to cook ingredients with simple button touch controls, greatly saving manpower and resources.

[0003] Currently, smart cooking devices also have recipe recommendation functions. They can filter recipes matching user-input keywords from a database and recommend them to the user. However, practical experience has shown that using keywords to search for recipes based on the massive amount of recipe data in the database can easily lead to fuzzy queries, resulting in an excessively large amount of filtered results. This makes it difficult to accurately recommend recipes to users, thus reducing their cooking experience. Therefore, providing an intelligent method for recommending recipes is particularly important. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a method and apparatus for recommending recipes based on pre-determined recipes. This method and apparatus can reduce the amount of recipe data to be recommended through intelligent recipe screening, thereby improving the reliability and accuracy of the recommended recipe data and thus enhancing the user's cooking experience.

[0005] To address the aforementioned technical problems, the first aspect of this invention discloses a method for recommending recipes based on pre-determined recipes, the method comprising:

[0006] The recipe data for each recipe in a pre-determined set of recipes is obtained, along with the first user information of the target user who has a need for recipe recommendations. The recipe data for each recipe includes at least one of the following: the type of ingredients required for the recipe, the type of cooking equipment, the cooking method of the ingredients, and the cooking process of the ingredients. The first user information of the target user includes at least one of the following: the target user's health status information, exercise information, emotional status information, sleep status information, historical dietary information, and dietary needs information.

[0007] Based on the recipe data of all the recipes and the first user information of the target user, determine all the pending recipes that match the target user from all the recipes, and determine the uploader corresponding to each pending recipe;

[0008] For each pending recipe, determine whether the intimacy level between the uploader of the pending recipe and the target user is greater than or equal to a preset intimacy threshold. If so, the pending recipe is determined as a target recipe that needs to be recommended to the target user.

[0009] As an optional implementation, in the first aspect of the present invention, determining whether the intimacy level between the uploader of the pending recipe and the target user is greater than or equal to a preset intimacy threshold for each pending recipe includes:

[0010] Determine whether the target user has corresponding dining companionship needs; the dining companionship needs of the target user include at least one of the following: dining companionship identity information, dining companionship location needs information, and dining companionship time needs information.

[0011] When it is determined that the target user has the dining companionship requirement, for each pending recipe, the second user information of the uploader of the pending recipe is determined; the second user information of the uploader includes at least one of the uploader's user identity information, dining location information, and dining time information;

[0012] Based on the uploader's second user information and the target user's corresponding dining companionship needs information, calculate the dining companionship matching degree between the uploader and the target user, and determine whether the dining companionship matching degree is greater than or equal to a preset first matching degree threshold.

[0013] When it is determined that the matching degree of the dining companion is greater than or equal to the first matching degree threshold, the intimacy between the uploader and the target user is determined to be greater than or equal to the preset intimacy threshold.

[0014] As an optional implementation, in the first aspect of the present invention, calculating the dining companion matching degree between the uploader and the target user based on the uploader's second user information and the target user's corresponding dining companion needs information includes:

[0015] Obtain historical interaction data between the target user and the uploader; the historical interaction data includes at least one of historical entertainment interaction data, historical learning interaction data, historical work interaction data, and historical life interaction data.

[0016] Based on the historical interaction information, the uploader's second user information, and the target user's corresponding dining companionship needs information, predict the dining interaction between the target user and the uploader;

[0017] Based on the dining interaction, calculate the dining companionship matching degree between the uploader and the target user.

[0018] As an optional implementation, in the first aspect of the present invention, the method further includes:

[0019] When it is determined that the matching degree of the dining companion is less than the first matching degree threshold, at least one dining companion corresponding to the target user is determined according to the dining companion need information of the target user, and the third user information of each dining companion is obtained; the third user information of each dining companion includes at least one of the following: health status information, exercise information, emotional status information, sleep status information, historical diet information, and dietary needs information.

[0020] Based on the recipe data of the pending recipe and the third-party user information of all the dining companions, calculate the dining matching degree between the pending recipe and all the dining companions;

[0021] Determine whether the dining matching degree is greater than or equal to a preset second matching degree threshold;

[0022] When it is determined that the dining matching degree is greater than or equal to the second matching degree threshold, the intimacy between the uploader and the target user is determined to be greater than or equal to the intimacy threshold.

[0023] As an optional implementation, in the first aspect of the present invention, the food requirement information of each dining companion includes at least one of the dining companion's food texture requirement information, food temperature requirement information, and food taste requirement information;

[0024] The step of calculating the dining match degree between the pending recipe and all the dining companions based on the recipe data of the pending recipe and the third-party user information of all the dining companions includes:

[0025] When the third user information of each dining companion includes the dining companion's food needs information, the post-cooking taste information of the pending recipe is determined based on the recipe data of the pending recipe; the post-cooking taste information includes at least one of post-cooking softness / hardness information, post-cooking temperature information, and post-cooking flavor information.

[0026] Based on the taste information of the cooked dishes from the pending recipes and the eating needs of each dining companion, the impact of cooking on each dining companion's consumption of the dishes from the pending recipes is predicted. The impact of cooking on each dining companion's consumption of the dishes from the pending recipes includes at least one of the following: chewing impact, oral temperature impact, and taste impact.

[0027] Based on the impact of each dining companion's consumption on the pending recipe after cooking, calculate the dining match degree between the pending recipe and all the dining companions.

[0028] As an optional implementation, in the first aspect of the present invention, the method further includes:

[0029] When it is determined that the target user does not have the dining companion requirement information, for each uploader of the pending recipe, it is determined whether there is corresponding cooking interaction information between the target user and the uploader; the cooking interaction information between the target user and the uploader includes at least one of cooking instruction interaction information, cooking tasting interaction information, and cooking interaction frequency information.

[0030] When it is determined that there is cooking interaction information between the target user and the uploader, the cooking interaction demand value between the target user and the uploader is calculated based on the cooking interaction information; the larger the cooking interaction demand value, the higher the cooking interaction demand between the target user and the uploader.

[0031] Determine whether the cooking interaction demand value is greater than or equal to a preset interaction demand threshold;

[0032] When it is determined that the cooking interaction demand value is greater than or equal to the interaction demand threshold, the intimacy between the uploader and the target user is determined to be greater than or equal to the intimacy threshold.

[0033] As an optional implementation, in the first aspect of the present invention, the method further includes:

[0034] When it is determined that there is no cooking interaction information between the target user and the uploader, the fourth user information of the uploader is obtained; the fourth user information of the uploader includes at least one of the uploader's health status information, exercise information, emotional status information, sleep status information, historical diet information, and dietary needs information.

[0035] Based on the uploader's fourth user information and the target user's first user information, determine whether the user similarity between the uploader and the target user is greater than or equal to a preset similarity threshold;

[0036] When it is determined that the user similarity is greater than or equal to the similarity threshold, the target changes corresponding to the dishes cooked by the uploader using the pending recipe are obtained; the target changes include at least one of the following: changes in health status, changes in motor function, changes in emotional state, changes in sleep status, and changes in dietary structure;

[0037] Based on the target change, determine the target change degree corresponding to the dish cooked by the uploader using the pending recipe, and determine whether the target change degree is greater than or equal to a preset change degree threshold;

[0038] When it is determined that the degree of change of the target is greater than or equal to the degree of change threshold, the intimacy between the uploader and the target user is determined to be greater than or equal to the intimacy threshold.

[0039] A second aspect of the present invention discloses an apparatus for recommending recipes based on pre-determined recipes, the apparatus comprising:

[0040] The acquisition module is used to acquire the recipe data of each recipe in a pre-determined recipe set and the first user information of the target user with recipe recommendation needs; the recipe data of each recipe includes at least one of the following: the type of ingredients required for the recipe, the type of cooking equipment, the cooking method of the ingredients, and the cooking process of the ingredients; the first user information of the target user includes at least one of the following: the target user's health status information, exercise information, emotional status information, sleep status information, historical diet information, and dietary needs information.

[0041] The determination module is used to determine all pending recipes that match the target user from all the recipes based on the recipe data of all the recipes and the first user information of the target user, and to determine the uploader corresponding to each pending recipe;

[0042] The judgment module is used to determine, for each of the pending recipes, whether the intimacy between the uploader of the pending recipe and the target user is greater than or equal to a preset intimacy threshold.

[0043] The determining module is further configured to determine the undetermined recipe as a target recipe that needs to be recommended to the target user when the judgment result of the judgment module is yes.

[0044] As an optional implementation, in a second aspect of the present invention, the determining module includes:

[0045] The determination submodule is used to determine whether the target user has corresponding dining companionship needs; the dining companionship needs of the target user include at least one of the following: dining companionship identity information, dining companionship location needs information, and dining companionship time needs information.

[0046] The determination submodule is used to determine the second user information of the uploader of each pending recipe when the judgment submodule determines that the target user has the dining companion requirement information; the second user information of the uploader includes at least one of the uploader's user identity information, dining location information, and dining time information.

[0047] The calculation submodule is used to calculate the matching degree of dining companionship between the uploader and the target user based on the uploader's second user information and the target user's corresponding dining companionship needs information;

[0048] The judgment submodule is also used to determine whether the dining companion matching degree is greater than or equal to a preset first matching degree threshold.

[0049] The determining submodule is further configured to determine that the intimacy between the uploader and the target user is greater than or equal to a preset intimacy threshold when the judging submodule determines that the dining companion matching degree is greater than or equal to the first matching degree threshold.

[0050] As an optional implementation, in the second aspect of the present invention, the calculation submodule calculates the matching degree of dining companionship between the uploader and the target user based on the uploader's second user information and the target user's corresponding dining companionship needs information as follows:

[0051] Obtain historical interaction data between the target user and the uploader; the historical interaction data includes at least one of historical entertainment interaction data, historical learning interaction data, historical work interaction data, and historical life interaction data.

[0052] Based on the historical interaction information, the uploader's second user information, and the target user's corresponding dining companionship needs information, predict the dining interaction between the target user and the uploader;

[0053] Based on the dining interaction, calculate the dining companionship matching degree between the uploader and the target user.

[0054] As an optional implementation, in a second aspect of the invention, the determining submodule is further configured to:

[0055] When the judgment submodule determines that the matching degree of the dining companion is less than the first matching degree threshold, it determines at least one dining companion corresponding to the target user based on the dining companion need information corresponding to the target user, and obtains the third user information of each dining companion; the third user information of each dining companion includes at least one of the following: health status information, exercise information, emotional status information, sleep status information, historical diet information, and dietary needs information.

[0056] The calculation submodule is also used to calculate the dining matching degree between the undetermined recipe and all the dining companions based on the recipe data of the undetermined recipe and the third user information of all the dining companions.

[0057] The judgment submodule is also used to determine whether the dining matching degree is greater than or equal to a preset second matching degree threshold;

[0058] The determining submodule is further configured to determine that the intimacy between the uploader and the target user is greater than or equal to the intimacy threshold when the judging submodule determines that the dining matching degree is greater than or equal to the second matching degree threshold.

[0059] As an optional implementation, in the second aspect of the present invention, the food requirement information of each dining companion includes at least one of the dining companion's food texture requirement information, food temperature requirement information, and food taste requirement information;

[0060] Specifically, the calculation submodule calculates the matching degree between the undetermined recipe and all the dining companions based on the recipe data and the third-party user information of all the dining companions as follows:

[0061] When the third user information of each dining companion includes the dining companion's food needs information, the post-cooking taste information of the pending recipe is determined based on the recipe data of the pending recipe; the post-cooking taste information includes at least one of post-cooking softness / hardness information, post-cooking temperature information, and post-cooking flavor information.

[0062] Based on the taste information of the cooked dishes from the pending recipes and the eating needs of each dining companion, the impact of cooking on each dining companion's consumption of the dishes from the pending recipes is predicted. The impact of cooking on each dining companion's consumption of the dishes from the pending recipes includes at least one of the following: chewing impact, oral temperature impact, and taste impact.

[0063] Based on the impact of each dining companion's consumption on the pending recipe after cooking, calculate the dining match degree between the pending recipe and all the dining companions.

[0064] As an optional implementation, in a second aspect of the invention, the determining submodule is further configured to:

[0065] When it is determined that the target user does not have the dining companion requirement information, for each uploader of the pending recipe, it is determined whether there is corresponding cooking interaction information between the target user and the uploader; the cooking interaction information between the target user and the uploader includes at least one of cooking instruction interaction information, cooking tasting interaction information, and cooking interaction frequency information.

[0066] The calculation submodule is further configured to, when the judgment submodule determines that there is cooking interaction information between the target user and the uploader, calculate the cooking interaction demand value between the target user and the uploader based on the cooking interaction information; the larger the cooking interaction demand value, the higher the cooking interaction demand between the target user and the uploader.

[0067] The judgment submodule is also used to determine whether the cooking interaction demand value is greater than or equal to a preset interaction demand threshold.

[0068] The determining submodule is further configured to determine that the intimacy between the uploader and the target user is greater than or equal to the intimacy threshold when the judging submodule determines that the cooking interaction demand value is greater than or equal to the interaction demand threshold.

[0069] As an optional implementation, in a second aspect of the present invention, the determining module further includes:

[0070] The acquisition submodule is used to acquire the uploader's fourth user information when the judgment submodule determines that there is no cooking interaction information between the target user and the uploader; the uploader's fourth user information includes at least one of the uploader's health status information, exercise information, emotional status information, sleep status information, historical diet information, and dietary needs information.

[0071] The judgment submodule is further configured to determine whether the user similarity between the uploader and the target user is greater than or equal to a preset similarity threshold based on the uploader's fourth user information and the target user's first user information;

[0072] The acquisition submodule is further configured to acquire, when the judgment submodule determines that the user similarity is greater than or equal to the similarity threshold, the target change situation corresponding to the dishes cooked by the uploader using the pending recipe; the target change situation includes at least one of the following: changes in health status, changes in motor function, changes in emotional state, changes in sleep status, and changes in dietary structure.

[0073] The determining submodule is further configured to determine the degree of target change corresponding to the dish cooked by the uploader using the pending recipe, based on the target change situation;

[0074] The judgment submodule is also used to determine whether the degree of change of the target is greater than or equal to a preset degree of change threshold;

[0075] The determining submodule is further configured to determine that the intimacy between the uploader and the target user is greater than or equal to the intimacy threshold when the judging submodule determines that the target change degree is greater than or equal to the change degree threshold.

[0076] A third aspect of the present invention discloses another apparatus for recommending recipes based on pre-determined recipes, the apparatus comprising:

[0077] Memory containing executable program code;

[0078] A processor coupled to the memory;

[0079] The processor calls the executable program code stored in the memory to execute the recipe recommendation method based on a predetermined recipe disclosed in the first aspect of the present invention.

[0080] The fourth aspect of the present invention discloses a computer storage medium storing computer instructions, which, when invoked, are used to execute the method for recommending recipes based on predetermined recipes disclosed in the first aspect of the present invention.

[0081] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0082] In this embodiment of the invention, based on the recipe data of all predetermined recipes and the first user information of the target user with recipe recommendation needs, all pending recipes matching the target user are determined, and the uploader corresponding to each pending recipe is determined. For each pending recipe, it is determined whether the intimacy between the uploader of the pending recipe and the target user is greater than or equal to a preset intimacy threshold. If so, the pending recipe is determined as a target recipe to be recommended to the target user. It is evident that implementing this invention can determine the target recipes to be recommended based on the intimacy between the uploader of each pending recipe and the target user. This intelligent recipe filtering not only helps reduce the amount of recipe data to be recommended, thus improving the reliability and accuracy of the recommended recipe data, but also improves the cooking matching degree between the recommended recipe data and the target user, thereby improving the cooking effect of the dishes cooked by the target user using the recipe data, and ultimately enhancing the user's cooking experience. Attached Figure Description

[0083] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0084] Figure 1 This is a flowchart illustrating a method for recommending recipes based on pre-determined recipes, as disclosed in an embodiment of the present invention.

[0085] Figure 2 This is a flowchart illustrating another method for recommending recipes based on pre-determined recipes, as disclosed in an embodiment of the present invention.

[0086] Figure 3 This is a schematic diagram of the structure of a device for recommending recipes based on a pre-determined recipe, as disclosed in an embodiment of the present invention;

[0087] Figure 4 This is a schematic diagram of another device for recommending recipes based on a pre-determined recipe, as disclosed in an embodiment of the present invention.

[0088] Figure 5 This is a schematic diagram of the structure of another device for recommending recipes based on a pre-determined recipe, as disclosed in an embodiment of the present invention. Detailed Implementation

[0089] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0090] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, apparatus, product, or end that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or ends.

[0091] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0092] This invention discloses a method and apparatus for recommending recipes based on pre-determined recipes. This method helps to reduce the amount of recipe data to be recommended through intelligent recipe filtering, thereby improving the reliability and accuracy of the recommended recipe data and ultimately enhancing the user's cooking experience.

[0093] Example 1

[0094] Please see Figure 1 , Figure 1 This is a flowchart illustrating a method for recommending recipes based on pre-determined recipes, as disclosed in an embodiment of the present invention. Optionally, this method can be implemented by an intelligent recipe recommendation system. This intelligent recipe recommendation system can be integrated into an intelligent cooking device, or it can exist independently of the intelligent cooking device. It can also be a local server or cloud server used to process the recipe recommendation process, etc. This embodiment of the present invention does not impose any limitations. Figure 1 As shown, this method for recommending recipes based on pre-determined recipes may include the following operations:

[0095] 101. Obtain the recipe data for each recipe in the pre-determined recipe set, as well as the first user information of the target users who have recipe recommendation needs.

[0096] In this embodiment of the invention, optionally, the pre-determined recipe set may be a set of recipes pre-uploaded by relevant uploaders, or a set of recipes pre-stored in a relevant database. Further optionally, the recipe data for each recipe includes at least one of the following: the type of ingredients required for the recipe, the type of cooking equipment, the cooking method of the ingredients, and the cooking process of the ingredients. The cooking process of the ingredients includes multiple sub-processes, and each sub-process includes corresponding multiple cooking steps. Further optionally, the first user information of the target user includes at least one of the following: the target user's health status information, exercise information, emotional status information, sleep status information, historical dietary information, and dietary requirements information. Further optionally, the dietary requirements information of the target user includes at least one of the following: dietary firmness requirements, dietary temperature requirements, dietary taste requirements, and dietary doneness requirements.

[0097] 102. Based on the recipe data of all recipes and the first user information of the target user, identify all pending recipes that match the target user from all recipes, and determine the uploader corresponding to each pending recipe.

[0098] In this embodiment of the invention, based on the recipe data of all recipes and the target user's first user information, potential recipes that are beneficial to the target user's physical / psychological health or meet the target user's dietary needs are determined from all recipes. For example, if the target user is in a low mood, recipes that promote physical and mental well-being can be identified and recommended to the target user; or, if the target user has just woken up and has had poor sleep quality, recipes that have an energizing effect can be identified and recommended to the target user; or, if the target user has just consumed ginseng chicken soup, recipes containing radish can be excluded from the recommendations.

[0099] Furthermore, before determining the uploader for each pending recipe, the method includes:

[0100] Determine the number of recipes corresponding to all pending recipes, and determine whether the number of recipes is greater than or equal to a preset quantity threshold;

[0101] When the judgment result is yes, the operation of determining the uploader corresponding to each pending recipe is triggered;

[0102] If the judgment result is negative, all pending recipes are identified as target recipes that need to be recommended to the target users.

[0103] In this alternative embodiment, the quantity threshold can be determined based on the cooking needs of the target user.

[0104] 103. For each pending recipe, determine whether the intimacy between the uploader of the pending recipe and the target user is greater than or equal to the preset intimacy threshold. If so, the pending recipe is determined as the target recipe that needs to be recommended to the target user.

[0105] In this embodiment of the invention, determining whether the intimacy between the uploader of the pending recipe and the target user is greater than or equal to a preset intimacy threshold can be understood as determining whether the dining companionship matching degree between the uploader and the target user is greater than or equal to a preset matching degree threshold, or determining whether the pending recipe uploaded by the uploader meets the physiological / psychological health needs or dietary needs of the target user and their dining companion, or determining whether the cooking interaction need value between the uploader and the target user is greater than or equal to a preset need threshold, or determining whether the relevant changes in the uploader's consumption of the dishes cooked using the pending recipe meet the target user's preset conditions, etc. Furthermore, when it is determined that the intimacy between the uploader of the pending recipe and the target user is less than the intimacy threshold, it is not necessary to determine the pending recipe as a target recipe to be recommended to the target user.

[0106] As can be seen, implementing the embodiments of the present invention can determine the target recipes to be recommended based on the closeness between the uploader of each pending recipe and the target user. In this way, through intelligent recipe filtering, it is not only beneficial to reduce the amount of recipe data to be recommended, thereby improving the reliability and accuracy of the recommended recipe data, but also beneficial to improve the cooking matching degree between the recommended recipe data and the target user, thereby improving the cooking effect of the dishes cooked by the target user using the recipe data, and thus improving the user's cooking experience.

[0107] Example 2

[0108] Please see Figure 2 , Figure 2 This is a flowchart illustrating a method for recommending recipes based on pre-determined recipes, as disclosed in an embodiment of the present invention. Optionally, this method can be implemented by an intelligent recipe recommendation system. This intelligent recipe recommendation system can be integrated into an intelligent cooking device, or it can exist independently of the intelligent cooking device. It can also be a local server or cloud server used to process the recipe recommendation process, etc. This embodiment of the present invention does not impose any limitations. Figure 2 As shown, this method for recommending recipes based on pre-determined recipes may include the following operations:

[0109] 201. Obtain the recipe data for each recipe in the pre-determined recipe set, as well as the first user information of the target users who have recipe recommendation needs.

[0110] 202. Based on the recipe data of all recipes and the first user information of the target user, identify all pending recipes that match the target user from all recipes, and determine the uploader corresponding to each pending recipe.

[0111] 203. Determine whether the target user has corresponding information regarding their need for companionship during meals.

[0112] In this embodiment of the invention, it is determined whether the target user needs a companion to dine with. Optionally, the dining companionship requirement information for the target user includes at least one of the following: companion identity information, dining location requirement information, and dining time requirement information.

[0113] 204. When it is determined that the target user has a need for companionship during meals, for each pending recipe, determine the second user information of the uploader of the pending recipe.

[0114] In this embodiment of the invention, optionally, the uploader's second user information includes at least one of the uploader's user identity information, dining location information, and dining time information.

[0115] 205. Based on the uploader's second user information and the target user's corresponding dining companionship needs information, calculate the dining companionship matching degree between the uploader and the target user, and determine whether the dining companionship matching degree is greater than or equal to the preset first matching degree threshold.

[0116] In this embodiment of the invention, based on the uploader's second user information and the target user's corresponding dining companion needs information, it is determined whether the uploader is the dining companion expected by the target user, or whether the dining location and dining time period of the uploader and the target user are matched, etc. When the above results are yes, it can be determined that the dining companion matching degree is greater than or equal to the preset first matching degree threshold.

[0117] 206. When it is determined that the matching degree of dining companionship is greater than or equal to the first matching degree threshold, the intimacy between the uploader and the target user is determined to be greater than or equal to the preset intimacy threshold.

[0118] 207. When it is determined that the intimacy between the uploader and the target user is greater than or equal to the preset intimacy threshold, the pending recipe is determined as the target recipe that needs to be recommended to the target user.

[0119] In this embodiment of the invention, for other descriptions of steps 201, 202, 206 and 207, please refer to the detailed description of steps 101-103 in Embodiment 1. This embodiment of the invention will not repeat them.

[0120] As can be seen, implementing the embodiments of the present invention can determine the degree of matching between the uploader of the recipe and the target user based on relevant user information, thereby judging the intimacy between the uploader and the target user. This helps to improve the reliability and accuracy of the determined degree of matching between the uploader and the target user, and further improves the reliability, accuracy and effectiveness of the subsequent operation of judging the intimacy between the uploader and the target user, thus helping to accurately recommend recipes to the target user.

[0121] In an optional embodiment, step 205 above, which calculates the matching degree of dining companionship between the uploader and the target user based on the uploader's second user information and the target user's corresponding dining companionship needs information, includes:

[0122] Obtain historical interaction data between the target user and the uploader;

[0123] Based on historical interaction data, the uploader's secondary user information, and the target user's corresponding dining companionship needs, predict the dining interaction between the target user and the uploader.

[0124] Based on the dining interaction, the matching degree of dining companionship between the uploader and the target user is calculated.

[0125] In this optional embodiment, the historical interaction data may optionally include at least one of historical entertainment interactions, historical learning interactions, historical work interactions, and historical life interactions. For example, if the target user and the uploader of the proposed recipe frequently learn and exchange ideas about the project together, and the uploader is the target user's expected dining companion, and the dining location and time between the uploader and the target user match, then based on the above circumstances, it can be predicted that the dining interactions between the target user and the uploader will facilitate further communication between them. In this case, the dining companionship matching degree between the uploader and the target user can be calculated to be 90% (high).

[0126] As can be seen, this optional embodiment can further calculate the dining companionship matching degree between the target user and the uploader based on the historical interaction between them. This helps to improve the reliability and accuracy of the calculation of the dining companionship matching degree between the target user and the uploader, thereby improving the reliability, accuracy and effectiveness of the operation to determine the dining companionship matching degree between the target user and the uploader, and thus improving the reliability and accuracy of the subsequent operation to determine the intimacy between the target user and the uploader.

[0127] In another alternative embodiment, the method further includes:

[0128] When it is determined that the matching degree of dining companion is less than the first matching degree threshold, at least one dining companion corresponding to the target user is determined based on the dining companion need information of the target user, and the third user information of each dining companion is obtained.

[0129] Based on the recipe data of the pending recipes and the third-party user information of all dining companions, calculate the dining matching degree between the pending recipes and all dining companions.

[0130] Determine whether the meal matching degree is greater than or equal to the preset second matching degree threshold;

[0131] When the dining matching degree is determined to be greater than or equal to the second matching degree threshold, the intimacy between the uploader and the target user is determined to be greater than or equal to the intimacy threshold.

[0132] In this optional embodiment, it is determined whether the proposed recipe is also beneficial to the physical / psychological health of the dining companion of the target user or whether it meets the dietary needs of the dining companion. Optionally, the third user information for each dining companion includes at least one of the following: health status information, exercise information, emotional status information, sleep status information, historical dietary information, and dietary needs information.

[0133] Furthermore, based on the recipe data of the pending recipe and the third-party user information of all dining companions, the matching degree between the pending recipe and all dining companions is calculated. This includes: predicting the relevant changes that each dining companion will experience when consuming dishes cooked using the pending recipe, based on the recipe data of the pending recipe and the third-party user information of all dining companions; calculating the matching degree between the pending recipe and each dining companion based on the relevant changes that each dining companion will experience when consuming dishes cooked using the pending recipe; and finally calculating the matching degree between the pending recipe and each dining companion based on the matching degree between the pending recipe and each dining companion. The relevant changes may include at least one of the following: changes in health status, changes in motor function, changes in emotional state, changes in sleep status, and changes in dietary structure.

[0134] As can be seen, this optional embodiment can calculate the matching degree between the proposed recipe and the dining companion based on the user information of the target user's dining companion, thereby determining the intimacy between the uploader of the proposed recipe and the target user. This can improve the reliability and accuracy of the calculated matching degree between the proposed recipe and the dining companion, and further improve the reliability, accuracy and effectiveness of the intimacy determination operation between the uploader and the target user. As a result, relevant recipes can be accurately recommended to the target user to enhance their cooking experience and the dining experience of their dining companion.

[0135] In another optional embodiment, the step of calculating the dining match degree between the pending recipe and all dining companions based on the recipe data of the pending recipe and the third-party user information of all dining companions includes:

[0136] When the third-party user information of each dining companion includes the dining companion's food needs information, the taste information after cooking of the pending recipe is determined based on the recipe data of the pending recipe.

[0137] Based on the taste information of the dishes after cooking and the eating needs of each dining companion, predict how much each dining companion will be affected by the dishes after cooking.

[0138] Calculate the dining match degree between the pending recipe and all dining companions based on how well each person's consumption of the food is affected after cooking.

[0139] In this optional embodiment, the dietary needs information for each dining companion may optionally include at least one of the following: dietary firmness requirements, dietary temperature requirements, dietary taste requirements, and dietary doneness requirements. Further optionally, the post-cooking texture information may include at least one of the following: post-cooking firmness information, post-cooking temperature information, and post-cooking taste information. Still optionally, the post-cooking impact on each dining companion regarding the proposed recipe may include at least one of the following: impact on chewing, impact on oral temperature, and impact on taste experienced by the dining companion when consuming dishes cooked according to the proposed recipe.

[0140] For example, if a dining companion's taste preference is for a non-spicy dish, but the proposed recipe uses a lot of chili peppers, it can be predicted that the dining companion will likely be stimulated by the spiciness and have difficulty eating. Therefore, the dining match between the proposed recipe and the dining companion can be calculated to be 10% (low).

[0141] As can be seen, this optional embodiment can further predict the impact of cooking the proposed recipe on the dining companion's consumption based on the companion's dietary needs information, thereby calculating the dining match degree between the proposed recipe and the dining companion. This can improve the reliability and accuracy of the calculation of the dining match degree between the proposed recipe and the dining companion, and thus ensure that the recipes subsequently recommended to the target user are also suitable for the dining companion, thereby improving the recommendation accuracy of the target recipe and enhancing the recommendation effect of the recipe.

[0142] In yet another optional embodiment, the method further includes:

[0143] When it is determined that the target user does not have any information about needing someone to accompany them during meals, for each uploader of a pending recipe, it is determined whether there is any corresponding cooking interaction information between the target user and the uploader.

[0144] When it is determined that there is cooking interaction information between the target user and the uploader, the cooking interaction demand value between the target user and the uploader is calculated based on the cooking interaction information; the larger the cooking interaction demand value, the higher the cooking interaction demand between the target user and the uploader.

[0145] Determine whether the cooking interaction demand value is greater than or equal to the preset interaction demand threshold;

[0146] When it is determined that the cooking interaction demand value is greater than or equal to the interaction demand threshold, the intimacy between the uploader and the target user is determined to be greater than or equal to the intimacy threshold.

[0147] In this optional embodiment, the cooking interaction information between the target user and the uploader may include at least one of cooking instruction interaction information, cooking tasting interaction information, and cooking interaction frequency information. For example, if the uploader of the recipe to be determined is the target user's cooking instructor, the cooking instruction situation between the two can be determined, such as the cooking instruction progress, cooking instruction effect, cooking instruction frequency, etc., and the cooking interaction demand value between the two can be calculated based on the cooking instruction situation between the two. For example, the higher the cooking instruction frequency between the two, the greater the cooking interaction demand value, etc.

[0148] As can be seen, this optional embodiment can also calculate the cooking interaction demand value between the target user and the uploader based on the cooking interaction information between the two, thereby determining the intimacy between them. This realizes the intelligent determination of the intimacy between the target user and the uploader, which helps to improve the reliability and accuracy of the calculated cooking interaction demand value between the target user and the uploader. In turn, it helps to improve the reliability, accuracy and effectiveness of the subsequent intimacy determination operation between the target user and the uploader, thereby improving the intelligence and accuracy of the recipe recommendation method.

[0149] In yet another optional embodiment, the method further includes:

[0150] When it is determined that there is no cooking interaction information between the target user and the uploader, obtain the uploader's fourth user information;

[0151] Based on the uploader's fourth user information and the target user's first user information, determine whether the user similarity between the uploader and the target user is greater than or equal to a preset similarity threshold.

[0152] When it is determined that the user similarity is greater than or equal to the similarity threshold, the target changes corresponding to the dishes cooked by the uploader using the pending recipe are obtained;

[0153] Based on the changes in the target, determine the degree of target change corresponding to the dishes cooked by the uploader using the pending recipes, and determine whether the degree of target change is greater than or equal to the preset degree of change threshold.

[0154] When it is determined that the degree of change of the target is greater than or equal to the degree of change threshold, the intimacy between the uploader and the target user is determined to be greater than or equal to the intimacy threshold.

[0155] In this optional embodiment, the uploader's fourth user information may optionally include at least one of the uploader's health status information, exercise status information, emotional status information, sleep status information, historical dietary information, and dietary needs information. Further optionally, the target change status may include at least one of changes in health status, changes in motor function, changes in emotional status, changes in sleep status, and changes in dietary structure. For example, if the uploader of the pending recipe A and the target user are both diabetic patients and should not eat foods high in sugar, then the user similarity between the two can be determined to be greater than or equal to a preset similarity threshold. In this case, the health status changes corresponding to the uploader consuming the dishes cooked using the pending recipe A can be obtained, such as whether the uploader's condition improved through the pending recipe A, etc., and it can be determined whether the degree of improvement is greater than or equal to a preset degree threshold. If so, it can be determined that the intimacy between the uploader and the target user is greater than or equal to a preset intimacy threshold, that is, the uploader's health status changes have met the target user's expectations, and the target user can improve their condition through the pending recipe A.

[0156] As can be seen, this optional embodiment can determine the intimacy between the uploader and the target user based on the target changes corresponding to the dishes cooked by the uploader using the pending recipe. This improves the comprehensiveness of the intimacy determination operation between the uploader and the target user, thereby improving the reliability, accuracy and effectiveness of the intimacy determination operation, and thus improving the accuracy of recipe recommendations to enhance the user's cooking experience.

[0157] Example 3

[0158] Please see Figure 3 , Figure 3 This is a schematic diagram of a device for recommending recipes based on pre-determined recipes, as disclosed in an embodiment of the present invention. Figure 3 As shown, the device for recommending recipes based on pre-determined recipes may include:

[0159] The acquisition module 301 is used to acquire the recipe data of each recipe in the pre-determined recipe set and the first user information of the target user with recipe recommendation needs;

[0160] The determination module 302 is used to determine all pending recipes that match the target user from all recipes based on the recipe data of all recipes and the first user information of the target user, and to determine the uploader corresponding to each pending recipe.

[0161] The judgment module 303 is used to determine, for each pending recipe, whether the intimacy between the uploader of the pending recipe and the target user is greater than or equal to a preset intimacy threshold.

[0162] The determining module 302 is also used to determine the pending recipe as the target recipe that needs to be recommended to the target user when the judgment result of the judging module 303 is yes.

[0163] In this embodiment of the invention, the recipe data for each recipe includes at least one of the following: the type of ingredients required for the recipe, the type of cooking equipment, the cooking method of the ingredients, and the cooking process of the ingredients. The first user information of the target user includes at least one of the following: the target user's health status information, exercise information, emotional status information, sleep status information, historical diet information, and dietary needs information.

[0164] It is evident that implementation Figure 3 The described recipe recommendation device, based on pre-determined recipes, can determine the target recipes to be recommended based on the closeness between the uploader of each pending recipe and the target user. In this way, through intelligent recipe filtering, it not only helps to reduce the amount of recipe data to be recommended, thereby improving the reliability and accuracy of the recommended recipe data, but also helps to improve the cooking matching degree between the recommended recipe data and the target user, thereby improving the cooking effect of the dishes cooked by the target user using the recipe data, and thus improving the user's cooking experience.

[0165] In an optional embodiment, the determination module 303 includes:

[0166] The judgment submodule 3031 is used to determine whether the target user has corresponding dining companionship needs.

[0167] The determination submodule 3032 is used to determine the second user information of the uploader of each pending recipe when the judgment submodule determines that the target user has a need for dining companionship.

[0168] The calculation submodule 3033 is used to calculate the matching degree of dining companionship between the uploader and the target user based on the uploader's second user information and the target user's corresponding dining companionship needs information.

[0169] The judgment submodule 3031 is also used to determine whether the dining companion matching degree is greater than or equal to the preset first matching degree threshold;

[0170] The determining submodule 3032 is also used to determine that the intimacy between the uploader and the target user is greater than or equal to the preset intimacy threshold when the judging submodule 3031 determines that the dining companion matching degree is greater than or equal to the first matching degree threshold.

[0171] In this optional embodiment, the dining companionship requirement information corresponding to the target user includes at least one of the following: dining companion identity information, dining companionship location requirement information, and dining companionship time requirement information; the uploader's second user information includes at least one of the uploader's user identity information, dining location information, and dining time information.

[0172] It is evident that implementation Figure 4 The described device for recommending recipes based on pre-determined recipes can determine the degree of matching between the uploader of the recipe and the target user based on relevant user information. This allows for the assessment of the intimacy between the uploader and the target user, thereby improving the reliability and accuracy of the determined degree of matching. Consequently, it enhances the reliability, accuracy, and effectiveness of subsequent intimacy assessments, ultimately leading to more accurate recipe recommendations for the target user.

[0173] In another optional embodiment, the calculation submodule 3033 calculates the matching degree of dining companionship between the uploader and the target user based on the uploader's second user information and the target user's corresponding dining companionship needs information as follows:

[0174] Obtain historical interaction data between the target user and the uploader;

[0175] Based on historical interaction data, the uploader's secondary user information, and the target user's corresponding dining companionship needs, predict the dining interaction between the target user and the uploader.

[0176] Based on the dining interaction, the matching degree of dining companionship between the uploader and the target user is calculated.

[0177] In this optional embodiment, historical interaction scenarios include at least one of historical entertainment interaction scenarios, historical learning interaction scenarios, historical work interaction scenarios, and historical life interaction scenarios.

[0178] It is evident that implementation Figure 4The described device for recommending recipes based on pre-determined recipes can further calculate the dining companionship matching degree between the target user and the uploader based on the historical interaction between them. This helps to improve the reliability and accuracy of the calculation of the dining companionship matching degree between the target user and the uploader, thereby improving the reliability, accuracy and effectiveness of the operation of determining the dining companionship matching degree between the target user and the uploader, and thus improving the reliability and accuracy of the subsequent operation of determining the intimacy between the target user and the uploader.

[0179] In yet another optional embodiment, the determining submodule 3032 is further configured to:

[0180] When the judgment submodule 3031 determines that the matching degree of dining companion is less than the first matching degree threshold, it determines at least one dining companion corresponding to the target user based on the dining companion demand information of the target user, and obtains the third user information of each dining companion.

[0181] The calculation submodule 3033 is also used to calculate the dining matching degree between the pending recipe and all dining companions based on the recipe data of the pending recipe and the third-party user information of all dining companions.

[0182] The judgment submodule 3031 is also used to determine whether the meal matching degree is greater than or equal to the preset second matching degree threshold;

[0183] The determining submodule 3032 is also used to determine that the intimacy between the uploader and the target user is greater than or equal to the intimacy threshold when the judging submodule 3031 determines that the dining matching degree is greater than or equal to the second matching degree threshold.

[0184] In this optional embodiment, the third user information for each dining companion includes at least one of the following: health status information, exercise information, emotional status information, sleep status information, historical dietary information, and dietary needs information.

[0185] It is evident that implementation Figure 4 The described device for recommending recipes based on pre-determined recipes can calculate the matching degree between a proposed recipe and its dining companion based on the user information of the target user's dining companion. This allows for the determination of the intimacy between the uploader of the proposed recipe and the target user, thereby improving the reliability and accuracy of the calculated matching degree. Consequently, it enhances the reliability, accuracy, and effectiveness of the intimacy determination operation between the uploader and the target user, enabling precise recommendation of relevant recipes to the target user to improve their cooking experience and the dining experience of their dining companion.

[0186] In another optional embodiment, the calculation submodule 3033 calculates the dining match degree between the pending recipe and all dining companions based on the recipe data of the pending recipe and the third-party user information of all dining companions in the following specific way:

[0187] When the third-party user information of each dining companion includes the dining companion's food needs information, the taste information after cooking of the pending recipe is determined based on the recipe data of the pending recipe.

[0188] Based on the taste information of the dishes after cooking and the eating needs of each dining companion, predict how much each dining companion will be affected by the dishes after cooking.

[0189] Calculate the dining match degree between the pending recipe and all dining companions based on how well each person's consumption of the food is affected after cooking.

[0190] In this optional embodiment, the eating needs information of each dining companion includes at least one of the dining companion's eating texture requirements, eating temperature requirements, and eating taste requirements; the post-cooking eating texture information includes at least one of the post-cooking eating texture requirements, post-cooking eating temperature information, and post-cooking eating taste information; the post-cooking eating conditions of each dining companion in relation to the pending recipe include at least one of the chewing effects, oral temperature effects, and taste effects experienced by the dining companion when eating dishes cooked according to the pending recipe.

[0191] It is evident that implementation Figure 4 The described device for recommending recipes based on pre-determined recipes can further predict the impact of cooking the recipes on the dining companion's consumption based on the companion's dietary needs. This allows for the calculation of the matching degree between the recipes and the companion, improving the reliability and accuracy of the matching degree calculation. Consequently, it ensures that recipes recommended to the target user are also suitable for the companion, thus increasing the accuracy of recipe recommendations and enhancing the overall recommendation effect.

[0192] In yet another optional embodiment, the determination submodule 3031 is further configured to:

[0193] When it is determined that the target user does not have any information about needing someone to accompany them during meals, for each uploader of a pending recipe, it is determined whether there is any corresponding cooking interaction information between the target user and the uploader.

[0194] The calculation submodule 3033 is also used to calculate the cooking interaction demand value between the target user and the uploader based on the cooking interaction information when the judgment submodule 3031 determines that there is cooking interaction information between the target user and the uploader.

[0195] The judgment submodule 3031 is also used to determine whether the cooking interaction demand value is greater than or equal to the preset interaction demand threshold.

[0196] The determining submodule 3032 is also used to determine that the intimacy between the uploader and the target user is greater than or equal to the intimacy threshold when the judging submodule 3031 determines that the cooking interaction demand value is greater than or equal to the interaction demand threshold.

[0197] In this optional embodiment, the cooking interaction information between the target user and the uploader includes at least one of cooking instruction interaction information, cooking tasting interaction information, and cooking interaction frequency information; the higher the cooking interaction demand value, the higher the cooking interaction demand between the target user and the uploader.

[0198] It is evident that implementation Figure 4 The described recipe recommendation device, based on pre-determined recipes, can also calculate the cooking interaction demand value between the target user and the uploader based on the cooking interaction information between the two, thereby determining the intimacy between them. This realizes the intelligent determination of the intimacy between the target user and the uploader, which helps to improve the reliability and accuracy of the calculated cooking interaction demand value between the target user and the uploader. In turn, it helps to improve the reliability, accuracy and effectiveness of subsequent intimacy determination operations between the target user and the uploader, thus improving the intelligence and accuracy of the recipe recommendation method.

[0199] In yet another optional embodiment, the determination module 303 further includes:

[0200] The acquisition submodule 3034 is used to acquire the fourth user information of the uploader when the judgment submodule 3031 determines that there is no cooking interaction information between the target user and the uploader.

[0201] The judgment submodule 3031 is also used to determine whether the user similarity between the uploader and the target user is greater than or equal to a preset similarity threshold based on the uploader's fourth user information and the target user's first user information.

[0202] The acquisition submodule 3034 is also used to acquire the target change corresponding to the dishes cooked by the uploader using the pending recipe when the judgment submodule 3031 determines that the user similarity is greater than or equal to the similarity threshold.

[0203] The determination submodule 3032 is also used to determine the degree of target change corresponding to the dish cooked by the uploader using the pending recipe, based on the target change situation;

[0204] The judgment submodule 3031 is also used to determine whether the degree of change of the target is greater than or equal to a preset degree of change threshold;

[0205] The determination submodule 3032 is also used to determine that the intimacy between the uploader and the target user is greater than or equal to the intimacy threshold when the judgment submodule 3031 determines that the target change degree is greater than or equal to the change degree threshold.

[0206] In this optional embodiment, the uploader's fourth user information includes at least one of the uploader's health status information, exercise status information, emotional status information, sleep status information, historical dietary information, and dietary needs information; the target change information includes at least one of the following: changes in health status, changes in motor function, changes in emotional status, changes in sleep status, and changes in dietary structure.

[0207] It is evident that implementation Figure 4 The described device for recommending recipes based on pre-determined recipes can determine the intimacy between the uploader and the target user based on the target changes corresponding to the dishes cooked by the uploader using the proposed recipes. This improves the comprehensiveness of the intimacy determination operation between the uploader and the target user, thereby improving the reliability, accuracy, and effectiveness of the intimacy determination operation, and thus improving the accuracy of recipe recommendations to enhance the user's cooking experience.

[0208] Example 4

[0209] Please see Figure 5 , Figure 5 This is a schematic diagram of another device for recommending recipes based on pre-determined recipes, as disclosed in an embodiment of the present invention. Figure 5 As shown, the device for recommending recipes based on pre-determined recipes may include:

[0210] Memory 401 storing executable program code;

[0211] Processor 402 coupled to memory 401;

[0212] The processor 402 calls the executable program code stored in the memory 401 to execute the steps in the method for recommending recipes based on a predetermined recipe as described in Embodiment 1 or Embodiment 2 of the present invention.

[0213] Example 5

[0214] This invention discloses a computer storage medium storing computer instructions. When these computer instructions are invoked, they are used to execute the steps in the method for recommending recipes based on a predetermined recipe, as described in Embodiment 1 or Embodiment 2 of this invention.

[0215] Example 6

[0216] This invention discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to perform the steps in the method for recommending recipes based on predetermined recipes described in Embodiment 1 or Embodiment 2.

[0217] The device embodiments described above are merely illustrative. The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules; that is, they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0218] Through the detailed description of the above embodiments, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, including read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electrically-Erasable Programmable Read-Only Memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0219] Finally, it should be noted that the method and apparatus for recommending recipes based on pre-determined recipes disclosed in the embodiments of the present invention are merely preferred embodiments of the present invention and are only used to illustrate the technical solutions of the present invention, not to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for recommending recipes based on pre-determined recipes, characterized in that, The method includes: The recipe data for each recipe in a pre-determined set of recipes is obtained, along with the first user information of the target user who has a need for recipe recommendations. The recipe data for each recipe includes at least one of the following: the type of ingredients required for the recipe, the type of cooking equipment, the cooking method of the ingredients, and the cooking process of the ingredients. The first user information of the target user includes at least one of the following: the target user's health status information, exercise information, emotional status information, sleep status information, historical dietary information, and dietary needs information. Based on the recipe data of all the recipes and the first user information of the target user, determine all the pending recipes that match the target user from all the recipes, and determine the uploader corresponding to each pending recipe; For each pending recipe, determine whether the intimacy between the uploader of the pending recipe and the target user is greater than or equal to a preset intimacy threshold. If so, the pending recipe is determined as a target recipe that needs to be recommended to the target user. Wherein, for each of the pending recipes, determining whether the intimacy level between the uploader of the pending recipe and the target user is greater than or equal to a preset intimacy threshold includes: Determine whether the target user has information indicating a need for companionship during meals; When it is determined that the target user does not have the dining companion requirement information, for each uploader of the pending recipe, it is determined whether there is corresponding cooking interaction information between the target user and the uploader; When it is determined that there is no cooking interaction information between the target user and the uploader, the fourth user information of the uploader is obtained; the fourth user information of the uploader includes at least one of the uploader's health status information, exercise information, emotional status information, sleep status information, historical diet information, and dietary needs information. Based on the uploader's fourth user information and the target user's first user information, determine whether the user similarity between the uploader and the target user is greater than or equal to a preset similarity threshold; When it is determined that the user similarity is greater than or equal to the similarity threshold, the target changes corresponding to the dishes cooked by the uploader using the pending recipe are obtained; the target changes include at least one of the following: changes in health status, changes in motor function, changes in emotional state, changes in sleep status, and changes in dietary structure; Based on the target change, determine the target change degree corresponding to the dish cooked by the uploader using the pending recipe, and determine whether the target change degree is greater than or equal to a preset change degree threshold; When it is determined that the degree of change of the target is greater than or equal to the degree of change threshold, the intimacy between the uploader and the target user is determined to be greater than or equal to the intimacy threshold.

2. The method for recommending recipes based on pre-determined recipes according to claim 1, characterized in that, The dining companionship needs information corresponding to the target user includes at least one of the following: dining companionship identity information, dining companionship location needs information, and dining companionship time needs information; The method further includes: When it is determined that the target user has the dining companionship requirement, for each pending recipe, the second user information of the uploader of the pending recipe is determined; the second user information of the uploader includes at least one of the uploader's user identity information, dining location information, and dining time information; Based on the uploader's second user information and the target user's corresponding dining companionship needs information, calculate the dining companionship matching degree between the uploader and the target user, and determine whether the dining companionship matching degree is greater than or equal to a preset first matching degree threshold. When it is determined that the matching degree of the dining companion is greater than or equal to the first matching degree threshold, the intimacy between the uploader and the target user is determined to be greater than or equal to the preset intimacy threshold.

3. The method for recommending recipes based on pre-determined recipes according to claim 2, characterized in that, The step of calculating the dining companion matching degree between the uploader and the target user based on the uploader's second user information and the target user's corresponding dining companion needs information includes: Obtain historical interaction data between the target user and the uploader; the historical interaction data includes at least one of historical entertainment interaction data, historical learning interaction data, historical work interaction data, and historical life interaction data. Based on the historical interaction information, the uploader's second user information, and the target user's corresponding dining companionship needs information, predict the dining interaction between the target user and the uploader; Based on the dining interaction, calculate the dining companionship matching degree between the uploader and the target user.

4. The method for recommending recipes based on pre-determined recipes according to claim 3, characterized in that, The method further includes: When it is determined that the matching degree of the dining companion is less than the first matching degree threshold, at least one dining companion corresponding to the target user is determined according to the dining companion need information of the target user, and the third user information of each dining companion is obtained; the third user information of each dining companion includes at least one of the following: health status information, exercise information, emotional status information, sleep status information, historical diet information, and dietary needs information. Based on the recipe data of the pending recipe and the third-party user information of all the dining companions, calculate the dining matching degree between the pending recipe and all the dining companions; Determine whether the dining matching degree is greater than or equal to a preset second matching degree threshold; When it is determined that the dining matching degree is greater than or equal to the second matching degree threshold, the intimacy between the uploader and the target user is determined to be greater than or equal to the intimacy threshold.

5. The method for recommending recipes based on pre-determined recipes according to claim 4, characterized in that, The dietary needs information for each dining companion includes at least one of the following: dietary firmness requirements, dietary temperature requirements, and dietary taste requirements. The step of calculating the dining match degree between the pending recipe and all the dining companions based on the recipe data of the pending recipe and the third-party user information of all the dining companions includes: When the third user information of each dining companion includes the dining companion's food needs information, the post-cooking taste information of the pending recipe is determined based on the recipe data of the pending recipe; the post-cooking taste information includes at least one of post-cooking softness / hardness information, post-cooking temperature information, and post-cooking flavor information. Based on the taste information of the cooked dishes from the pending recipes and the eating needs of each dining companion, the impact of cooking on each dining companion's consumption of the dishes from the pending recipes is predicted. The impact of cooking on each dining companion's consumption of the dishes from the pending recipes includes at least one of the following: chewing impact, oral temperature impact, and taste impact. Based on the impact of each dining companion's consumption on the pending recipe after cooking, calculate the dining match degree between the pending recipe and all the dining companions.

6. The method for recommending recipes based on a predetermined recipe according to any one of claims 2-5, characterized in that, The cooking interaction information between the target user and the uploader includes at least one of cooking instruction interaction information, cooking tasting interaction information, and cooking interaction frequency information; The method further includes: When it is determined that there is cooking interaction information between the target user and the uploader, the cooking interaction demand value between the target user and the uploader is calculated based on the cooking interaction information; the larger the cooking interaction demand value, the higher the cooking interaction demand between the target user and the uploader. Determine whether the cooking interaction demand value is greater than or equal to a preset interaction demand threshold; When it is determined that the cooking interaction demand value is greater than or equal to the interaction demand threshold, the intimacy between the uploader and the target user is determined to be greater than or equal to the intimacy threshold.

7. A device for recommending recipes based on pre-determined recipes, characterized in that, The apparatus is used to perform the method for recommending recipes based on a pre-determined recipe as described in any one of claims 1-6, and the apparatus comprises: The acquisition module is used to acquire the recipe data of each recipe in a pre-determined recipe set and the first user information of the target user with recipe recommendation needs; the recipe data of each recipe includes at least one of the following: the type of ingredients required for the recipe, the type of cooking equipment, the cooking method of the ingredients, and the cooking process of the ingredients; the first user information of the target user includes at least one of the following: the target user's health status information, exercise information, emotional status information, sleep status information, historical diet information, and dietary needs information. The determination module is used to determine all pending recipes that match the target user from all the recipes based on the recipe data of all the recipes and the first user information of the target user, and to determine the uploader corresponding to each pending recipe; The judgment module is used to determine, for each of the pending recipes, whether the intimacy between the uploader of the pending recipe and the target user is greater than or equal to a preset intimacy threshold. The determining module is further configured to determine the undetermined recipe as a target recipe that needs to be recommended to the target user when the judgment result of the judgment module is yes.

8. A device for recommending recipes based on pre-determined recipes, characterized in that, The device includes: Memory containing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the recipe recommendation method based on a predetermined recipe as described in any one of claims 1-6.

9. A computer storage medium, characterized in that, The computer storage medium stores computer instructions, which, when invoked, are used to execute the method for recommending recipes based on a predetermined recipe as described in any one of claims 1-6.

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