CN113139120A - Electronic equipment and recipe recommendation method and apparatus - Google Patents
Electronic equipment and recipe recommendation method and apparatus Download PDFInfo
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- CN113139120A CN113139120A CN202010065279.7A CN202010065279A CN113139120A CN 113139120 A CN113139120 A CN 113139120A CN 202010065279 A CN202010065279 A CN 202010065279A CN 113139120 A CN113139120 A CN 113139120A
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Abstract
The application provides an electronic device and a recipe recommendation method and device, wherein the method comprises the following steps: acquiring historical cooking data of a target user, and acquiring first preference information of the target user according to the historical cooking data; acquiring attribute data of a recipe, and acquiring a first recipe preference degree according to the first preference information and the attribute data of the recipe; the method comprises the steps of obtaining interactive data of recipes, obtaining a target recipe for a target user according to the interactive data of the recipes and the preference degree of the first recipe, recommending the target recipe to the target user, determining the target recipe for recommendation according to the preference degree of the user to the recipes and the interactive data of the recipes, effectively enriching a recipe recommendation list, ensuring that the recommended recipes accord with the preference of the user, promoting the cooking desire of the user and achieving the recommendation purpose.
Description
Technical Field
The invention relates to the technical field of cooking, in particular to an electronic device and a recipe recommendation method and device.
Background
In the related art, when a recipe is recommended to a user, the recipe is generally recommended according to historical cooking data of the user, so that the user has limitation in obtaining the recommended data, the cooking desire of the user is difficult to cause, and the recommendation purpose cannot be achieved.
Disclosure of Invention
The present invention is directed to solving, at least to some extent, one of the technical problems in the related art.
Therefore, a first objective of the present invention is to provide a recipe recommendation method, which determines a target recipe for recommendation according to a preference of a user for a recipe and interaction data of the recipe, so as to effectively enrich a recipe recommendation list, ensure that a recommended recipe meets the preference of the user, promote a cooking desire of the user, and achieve a recommendation goal.
A second object of the present invention is to provide a recipe recommendation apparatus.
A third object of the invention is to propose an electronic device.
A fourth object of the invention is to propose a computer-readable storage medium.
In order to achieve the above object, a first aspect of the present invention provides a recipe recommendation method, including: acquiring historical cooking data of a target user, and acquiring first preference information of the target user according to the historical cooking data; acquiring attribute data of a recipe, and acquiring a first recipe preference degree according to the first preference information and the attribute data of the recipe; and acquiring interactive data of a recipe, acquiring a target recipe for the target user according to the interactive data of the recipe and the preference degree of the first recipe, and recommending the target recipe to the target user.
According to an embodiment of the present invention, the obtaining a first recipe preference according to the first preference information and the attribute data of the recipe, includes: acquiring the similarity between the recipe and the first preference information according to the attribute data of the recipe; and sequencing the recipes according to the similarity to obtain a first recipe preference.
According to an embodiment of the present invention, the obtaining a target recipe for the target user according to the interaction data of the recipe and the first recipe preference includes: acquiring the heat degree of the recipe according to the interactive data of the recipe; sorting the recipes according to the heat degree to obtain a first recipe popularity; weighting the preference degree and the popularity degree of the first recipe to obtain a recommendation index of the recipe; and sorting the recommendation indexes of the recipes, and selecting the recipe with the recommendation index larger than a preset threshold value as a target recipe.
According to an embodiment of the present invention, the obtaining a target recipe for the target user according to the interaction data of the recipe and the first recipe preference includes: acquiring users with similar behaviors according to the interaction data of the recipes; acquiring second preference information of the users with similar behaviors, and acquiring second recipe preference according to the second preference information; weighting the first recipe preference degree and the second recipe preference degree to obtain a recommendation index of the recipe; and sorting the recommendation indexes of the recipes, and selecting the recipe with the recommendation index larger than a preset threshold value as a target recipe.
According to the embodiment of the invention, according to the interaction data of the recipe, a first recipe popularity based on the recipe popularity and a second preference based on similar behavior users are obtained; weighting the first recipe preference degree, the first recipe popularity degree and the second recipe preference degree to obtain a recommendation index of the recipe; and sorting the recommendation indexes of the recipes, and selecting the recipe with the recommendation index larger than a preset threshold value as a target recipe.
According to one embodiment of the present invention, the weighting process includes: and acquiring weight data set by a user, and weighting the first recipe preference degree, the first recipe popularity degree and/or the second recipe preference degree according to the weight data.
According to an embodiment of the present invention, after the recommending the target recipe to the target user, the method further includes: identifying the cooking times of the user on the target recipe; adjusting the weight of the first recipe preference, the first recipe popularity, and/or the second recipe preference according to the number of cookings.
According to an embodiment of the present invention, after the recommending the target recipe to the target user, the method further includes: acquiring feedback data of the target recipe; and adjusting the weight of the preference of the first recipe, the popularity of the first recipe and/or the preference of the second recipe according to the feedback data of the recipe.
According to the recipe recommendation method provided by the embodiment of the invention, the preference degree of the first recipe can be determined according to the first preference information of the target user and the attribute data of the recipe, and the target recipe recommended to the user is determined according to the preference degree of the first recipe and the interaction data of the recipe, so that the preference degree of the target user on the recommended recipe is effectively ensured, the problem of repeated recommendation of the recipe cooked by the user is avoided, the recommended recipe list is enriched, the user is promoted to cook the recommended recipe, and the recommendation conversion rate is improved.
In order to achieve the above object, a second aspect of the present invention provides a recipe recommendation apparatus, including: the first acquisition module is used for acquiring historical cooking data of a target user and acquiring first preference information of the target user according to the historical cooking data; the second acquisition module is used for acquiring attribute data of the recipe and acquiring preference degree of the first recipe according to the first preference information and the attribute data of the recipe; and the recommending module is used for acquiring interactive data of the recipe, acquiring a target recipe for the target user according to the interactive data of the recipe and the preference degree of the first recipe, and recommending the target recipe to the target user.
In order to achieve the above object, a third embodiment of the present invention provides an electronic device, which includes the recipe recommendation apparatus.
In order to achieve the above object, a fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, where the computer program is executed by a processor to perform the recipe recommendation method.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
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The foregoing and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
fig. 1 is a flowchart of a recipe recommendation method according to an embodiment of the present invention;
FIG. 2 is a flow chart of a recipe recommendation method according to an embodiment of the invention;
FIG. 3 is a flow chart of a recipe recommendation method according to another embodiment of the invention;
FIG. 4 is a flow chart of a recipe recommendation method according to yet another embodiment of the invention;
FIG. 5 is a block diagram of a recipe recommendation device according to an embodiment of the present invention;
FIG. 6 is a block diagram of an electronic device according to an embodiment of the invention;
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are illustrative and intended to be illustrative of the invention and are not to be construed as limiting the invention.
An electronic device and a recipe recommendation method and apparatus according to embodiments of the present invention are described below with reference to the drawings.
Fig. 1 is a flowchart of a recipe recommendation method according to an embodiment of the present invention. As shown in fig. 1, a recipe recommendation method according to an embodiment of the present invention includes the following steps:
s101: historical cooking data of a target user is obtained, and first preference information of the target user is obtained according to the historical cooking data.
It should be noted that the first preference information of the target user may include information of multiple dimensions, such as eating season, cuisine, applicable population, efficacy, food material, cuisine, taste, taboo population, cooking manner, difficulty level of recipe making, and the like, wherein the recipe may further include, for example, western style and chinese style, the chinese recipe may further include, for example, chuanxiong dish, beijing dish, cantonese dish, lucuisine, northeast dish, and the like, and the taste may include, for example, salty, sweet, fresh, spicy, and the like. Thus, the first preference information may include preferences of multiple dimensions of the target user and/or a recipe preferred by the user.
S102: and acquiring attribute data of the recipe, and acquiring a first recipe preference degree according to the first preference information and the attribute data of the recipe.
Specifically, the attribute data of the recipe may include information of multiple dimensions, such as eating season, cuisine, applicable population, efficacy, food material, cuisine, taste, taboo population, cooking manner, difficulty level of recipe making, and the like.
Wherein, according to the first preference information and the attribute data of the recipe, acquiring the preference of the first recipe comprises: and acquiring the similarity between the recipe and the first preference information according to the attribute data of the recipe, and taking the similarity as the preference of the first recipe.
Specifically, when the first preference information is a preference condition of multiple dimensions, the attribute data of each recipe in the recipe database is extracted, then the similarity between the recipe and the first preference information is calculated according to the similarity between the attribute data of the recipe and the preference condition and the weight of each preference dimension, the recipes are sorted according to the similarity, and the preference based on the first preference information of the target user is obtained.
Or when the first preference information is a recipe liked by the user, extracting each item of attribute data of the recipe, and simultaneously extracting attribute data of each recipe in the recipe database, then performing weight calculation on the attribute data of each recipe in the recipe database and the attribute data of the recipe liked by the user to obtain the similarity between each recipe in the recipe database and the recipe liked by the user, further sequencing the recipes according to the similarity, and obtaining the preference based on the first preference information of the target user.
Or, the similarity between every two recipes in the recipe database can be calculated according to the weight occupied by each preset dimension, and when the first preference information of the user is a favorite recipe, the similarity between the favorite recipe and other recipes can be directly sorted, so that the first recipe preference based on the first preference information of the target user is obtained.
S103: and acquiring interactive data of the recipes, acquiring a target recipe for the target user according to the interactive data of the recipes and the preference degree of the first recipe, and recommending the target recipe to the target user.
The interactive data of the recipe can be attribute data with human colors generated by interactive behaviors of browsing, cooking, collecting, paying attention to the recipe and the like of the full-network user.
Specifically, interactive data of the recipes are matched with the preference degree of the first recipe, the target recipe to be recommended for the target user is obtained, and then the target recipe is recommended to the terminal device of the target user, so that the user selects the recipes to cook according to the recommendation information, the recommended recipes are effectively enriched, meanwhile, the recommendation recipes are prevented from being repeated, the user is promoted to cook the recommended recipes, and the recommendation conversion rate is improved.
The target recipe can be recommended through the mobile terminal bound with the cooking equipment or the cooking equipment, for example, when the terminal equipment is a mobile terminal such as a smart phone, recommended recipe content can be sent to a user through the mobile terminal when the user browses the cooking recipe; when the terminal is cooking equipment, the recommended recipe content can be directly displayed through the terminal arranged on the cooking equipment, so that the user can conveniently select the recipe.
Therefore, the recipe recommendation method provided by the embodiment of the invention can determine the preference degree of the first recipe according to the first preference information of the target user and the attribute data of the recipe, and determine the target recipe recommended to the user according to the preference degree of the first recipe and the interaction data of the recipe, so that the preference degree of the target user on the recommended recipe is effectively ensured, the problem of repeated recommendation of the recipe cooked by the user is avoided, the recommended recipe list is enriched, the user is promoted to cook the recommended recipe, and the recommendation conversion rate is improved.
According to an embodiment of the present invention, as shown in fig. 2, acquiring a target recipe for a target user according to interaction data of the recipe and a first recipe preference includes:
s201: and acquiring the heat of the recipe according to the interactive data of the recipe.
Wherein, according to the interactive data of the recipe, obtaining the heat of the recipe may include: extracting operation data such as the click times, the browsing times, the collection times and the starting times of the recipes from the interactive data, weighting the operation data such as the click times, the browsing times, the collection times and the starting times of the recipes, and acquiring the heat degree of the recipes.
That is, the interactive data of the entire web of recipes may be acquired and analyzed, and for each acquired recipe, the number of times that the recipe is clicked, the number of times that the recipe is browsed, the number of times that the recipe is collected, and the number of times that the recipe is started (cooked) are further acquired, and then the number of clicks, the number of times that the recipe is browsed, the number of times that the recipe is collected, and the number of times that the recipe is started are calculated in a weighted manner to acquire the heat of the recipe, in other words, the heat of the recipe is the weight of the number of clicks + the number of times that the recipe is browsed + the weight of the number of times that the recipe is started + the number of times that the recipe is started.
S202: and sorting the recipes according to the heat degree to obtain the popularity of the first recipe.
S203: and weighting the first preference and the first recipe popularity to obtain the recommendation index of the recipe.
S204: and sorting the recommendation indexes of the recipes, and selecting the recipe with the recommendation index larger than a preset threshold value as a target recipe.
Specifically, after the heat degree of each recipe is obtained, the recipes are sorted in a descending order according to the heat degree condition to obtain the popularity of the first recipe, at this time, the preference value and the popularity value of each recipe in the recipe database are obtained, weighting processing is performed according to the weight occupied by the preference value and the popularity value to obtain the recommendation index of the recipe, the recommendation index is further sorted, and the recipe with the recommendation index larger than a preset threshold is selected and used as the target recipe.
It should be understood that the recipe with the largest heat value is the recipe with the highest current degree of fire explosion, and then in the process of further acquiring the recipe with the recommendation index larger than the preset threshold, the preset threshold may be acquired according to the sorting condition of the recommendation index, for example, the recommendation index of the 10 th recipe in the recommendation index sorting may be acquired first, and the recommendation index of the 10 th recipe may be used as the preset threshold, or a fixed recommendation index may be set as the preset threshold, that is, as long as the recommendation index is larger than the preset threshold, the recommendation index may be used as the target recipe, and then the quantity control may be performed according to the number of the target recipes that can be recommended by the system. The setting manner of the preset threshold in this embodiment is also applicable to other embodiments of the present application.
For example, take the following 10 recipes as an example: pork rib braised rice, spiced chicken, home-made bean curd, universal sauce, spiced salt pleurotus eryngii, fried instant noodles, braised meat, delicious bean curd soup, green pepper fried bean curd and purple sweet potato flaky pastries. For 10 recipes in the recipe list, first preference between each recipe and first preference information of a user is firstly obtained, then interactive data of each recipe is obtained, and heat of each recipe is obtained according to the interactive data of each recipe, for example, the preference of the chop braising is 7, the heat is 5, then the recommendation index of the chop braising is 6 according to the preference, the heat and the weights of the preference and the heat (assuming that the weights are 0.5 respectively), and at this time, the recommendation index of each recipe in the recipe list is determined to be 6, and so on, the recommendation index of each recipe in the recipe list is obtained, and the recipes are sorted to obtain a target recipe.
It should be noted that the heat degree of the recipe is the expression of the likeness and the popularity of the whole online user to the recipe, the current cyber red recipe can be obtained according to the heat degree of the recipe, the cyber red recipe suitable for the target user can be obtained by matching the cyber red recipe with the first preference degree, and the recipe is recommended as the target recipe, so that the psychology that the current people like to feel hot and catch up with cyber red can be effectively met.
According to another embodiment of the present invention, as shown in fig. 3, acquiring a target recipe for a target user according to interaction data of the recipe and a first recipe preference further includes:
s301: and acquiring users with similar behaviors according to the interactive data of the recipes.
The users with similar behaviors can be users with the same or similar preference conditions, or users with similar requirements due to regions, climates and physiology (diseases, etc.), and even users who carry out recipe cooking through the network recipes over the whole network can be used as the users with similar behaviors (the similar behaviors are the users who carry out the recipe cooking by adopting the network recipes).
S302: and acquiring second preference information of the users with similar behaviors, and acquiring second recipe preference according to the second preference information.
Specifically, the interaction data of the recipe may be identified, the interaction data originating from the same similar behavior user may be identified, the second preference information of the similar behavior user and the second preference degree based on the second preference information and the recipe attribute data may be obtained based on the interaction data of the same similar behavior user. The second preference information may be acquired by the same method as the first preference information.
S303: and weighting the first recipe preference and the second recipe preference to obtain the recommendation index of the recipe.
S304: and sorting the recommendation indexes of the recipes, and selecting the recipe with the recommendation index larger than a preset threshold value as a target recipe.
Specifically, registered users in the network can all be used as similar behavior users, preference information of each similar behavior user is analyzed, attribute data of each recipe in the recipe database is matched with second preference information, and second recipe preference of the recipe based on the second preference information is obtained, that is, the recipes in the recipe database are sorted according to preference conditions of the similar behavior users, and then weighting processing is performed according to the first recipe preference and the second recipe preference, so that recommendation indexes of the recipes are obtained.
It should be understood that the recipe recommended to the target user in memorability at this time may be a recipe that does not conform to the first favorite information of the target user, that is, the similar behavior user has more similar labels (region, age, occupation, etc.) with the target user, and therefore, there may be a greater coincidence between the preferences of the two people, and at this time, the target user may be recommended using other types of recipes involved in hunting by the similar user as the target recipe, that is, the recipe recommended to the target user may be not only a type of recipe that the similar behavior user likes but also the target user likes, but also only a type of recipe that the similar behavior user likes, so that the target user can be promoted to find a new favorite type, and the recipe recommendation range is further widened.
Moreover, with the development of the country, the living and learning environments of people gradually break regional restrictions, people in the same native region may gradually disperse to different countries and regions to live, learn and work, but people usually like to cook food materials in a familiar cooking mode, so that the recipe is innovated to a certain extent, and at the moment, the innovated recipe can be used as a target recipe to recommend a target user.
For example, northeast people like to eat dumplings, the traditional northeast dumplings only have fillings mixed with pork, beef and some vegetables, and the dumplings develop into the northeast China area, so that donkey meat stuffing exists, at the moment, if a target user is the northeast user and a similar behavior user is the northeast people living in the northeast China, the donkey meat stuffed dumplings can be recommended to the target user as a recommended recipe, and similarly, the users in the Shandong coastal area can also be used as the similar behavior user to recommend the dumplings with aquatic stuffing to the target user.
According to still another embodiment of the method, as shown in fig. 4, the method further comprises:
s401: according to the interaction data of the recipes, a first recipe popularity based on the recipe popularity and a second preference based on the similar behavior user are obtained.
The method for acquiring the popularity of the first recipe and the second preference has been described in the foregoing embodiments, and is not described herein again.
S402: and weighting the first recipe preference degree, the first recipe popularity degree and the second recipe preference degree to obtain the recommendation index of the recipe.
S403: and sorting the recommendation indexes of the recipes, and selecting the recipe with the recommendation index larger than a preset threshold value as a target recipe.
That is to say, in order to comprehensively recommend recipes to a target user and avoid the situation that the recommended recipes are single in type and lack of novelty, weighting calculation can be performed according to the preference of the first recipe, the popularity of the first recipe and the preference of the second recipe to obtain a recommendation index with higher comprehensiveness, then the recipe with the largest recommendation index is used as the target recipe, and the recipe which is high in network popularity and liked by similar users and liked by the target user with higher probability is selected as the target recipe.
Wherein the weighting process includes: and acquiring weight data set by a user, and weighting the first recipe preference, the first recipe popularity and/or the second recipe preference according to the weight data.
That is, when recommending recipes according to the first recipe preference, the first recipe popularity, and/or the second recipe preference, the user may set the weight of each parameter by himself/herself, for example, if the user prefers to pursue a trend of popularity, the weight of the recipe popularity may be set relatively high, and if the user pays attention to his/her taste needs, the weight of the first recipe preference may be set relatively high.
Of course, in the process of pursuing intelligent recipe recommendation, the method and the device can also adjust the weight data set by the user and/or the weight data set initially according to the operation after the target recipe recommendation is carried out.
For example, the cooking times of the user on the target recipe are identified, and the weight of the first recipe preference degree, the first recipe popularity degree and/or the second recipe preference degree is adjusted according to the cooking times; for another example, feedback data of the target recipe is obtained, and the weight of the preference of the first recipe, the popularity of the first recipe and/or the preference of the second recipe is adjusted according to the feedback data of the recipe.
The feedback data of the target recipe can include operation data of browsing, collecting and the like of the target recipe by the user.
In other words, after the target recipe is recommended to the user, the interactive data of the target recipe by the target user is analyzed, and the original first recipe preference, the first recipe popularity and/or the second recipe preference condition reflected by the interactive data are extracted, so that the weight of the first recipe preference, the first recipe popularity and/or the second recipe preference is adjusted according to the condition reflected by the interactive data.
For example, after the recipe recommendation is performed on the target user, if the user performs less operations such as browsing, cooking, and collecting on the recipe with a higher preference degree of the second recipe, the weight of the preference degree of the second recipe may be appropriately reduced, or if the user performs operations such as browsing, cooking, and collecting on the recipe with a higher preference degree for multiple times, the weight of the preference degree of the second recipe may be appropriately increased.
In summary, the recipe recommendation method according to the embodiment of the present invention can determine the preference of the first recipe according to the first preference information of the target user and the attribute data of the recipe, and determine the target recipe recommended to the user according to the preference of the first recipe and the interaction data of the recipe, thereby effectively ensuring the preference of the target user to the recommended recipe, avoiding the problem of repeatedly recommending the recipe cooked by the user, enriching the recommended recipe list, promoting the user to cook the recommended recipe, and improving the recommendation conversion rate.
In order to realize the embodiment, the invention further provides a recipe recommending device.
Fig. 5 is a block diagram of a recipe recommendation apparatus according to an embodiment of the present invention. As shown in fig. 5, the recipe recommendation apparatus 100 includes: a first obtaining module 10, a second obtaining module 20 and a recommending module 30.
The first obtaining module 10 is configured to obtain historical cooking data of a target user, and obtain first preference information of the target user according to the historical cooking data; the second obtaining module 20 is configured to obtain attribute data of a recipe, and obtain a preference degree of the first recipe according to the first preference information and the attribute data of the recipe; the recommending module 30 is configured to obtain interaction data of a recipe, obtain a target recipe for the target user according to the interaction data of the recipe and the preference of the first recipe, and recommend the target recipe to the target user.
Further, the recommending module 30 is further configured to: acquiring the similarity between the recipe and the first preference information according to the attribute data of the recipe; and sequencing the recipes according to the similarity to obtain a first recipe preference.
Further, the recommending module 30 is further configured to: acquiring the heat degree of the recipe according to the interactive data of the recipe; sorting the recipes according to the heat degree to obtain a first recipe popularity; weighting the preference degree and the popularity degree of the first recipe to obtain a recommendation index of the recipe; and sorting the recommendation indexes of the recipes, and selecting the recipe with the recommendation index larger than a preset threshold value as a target recipe.
Further, the recommending module 30 is further configured to: acquiring users with similar behaviors according to the interaction data of the recipes; acquiring second preference information of the users with similar behaviors, and acquiring second recipe preference according to the second preference information; weighting the first recipe preference degree and the second recipe preference degree to obtain a recommendation index of the recipe; and sorting the recommendation indexes of the recipes, and selecting the recipe with the recommendation index larger than a preset threshold value as a target recipe.
Further, the recommending module 30 is further configured to: acquiring a first recipe popularity based on the recipe popularity and a second preference based on similar behavior users according to the interaction data of the recipes; weighting the first recipe preference degree, the first recipe popularity degree and the second recipe preference degree to obtain a recommendation index of the recipe; and sorting the recommendation indexes of the recipes, and selecting the recipe with the recommendation index larger than a preset threshold value as a target recipe.
Further, the recommending module 30 is further configured to: after the target recipe is recommended to the target user, identifying the cooking times of the target recipe by the user; adjusting the weight of the first recipe preference, the first recipe popularity, and/or the second recipe preference according to the number of cookings.
Further, the recommending module 30 is further configured to: after the recommending the target recipe to the target user, further comprising: acquiring feedback data of the target recipe; and adjusting the weight of the preference of the first recipe, the popularity of the first recipe and/or the preference of the second recipe according to the feedback data of the recipe.
It should be noted that the foregoing explanation of the embodiment of the recipe recommendation method is also applicable to the apparatus for recommending a recipe in this embodiment, and is not repeated herein.
In order to implement the above embodiment, the present invention further provides an electronic device, as shown in fig. 6, the electronic device 200 includes a recipe recommendation apparatus 100.
The electronic device 200 may be a server, or may also be a mobile terminal such as a mobile phone or a Pad, or an electric cooking device with an intelligent function.
In order to implement the above embodiments, the present invention also proposes a computer-readable storage medium on which a computer program is stored, which when executed by a processor implements the aforementioned recipe recommendation method.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "a plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing steps of a custom logic function or process, and alternate implementations are included within the scope of the preferred embodiment of the present invention in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present invention.
The logic and/or steps represented in the flowcharts or otherwise described herein, e.g., an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
It should be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware related to instructions of a program, which may be stored in a computer readable storage medium, and when the program is executed, the program includes one or a combination of the steps of the method embodiments.
In addition, functional units in the embodiments of the present invention may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a stand-alone product, may also be stored in a computer readable storage medium.
The storage medium mentioned above may be a read-only memory, a magnetic or optical disk, etc. Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made to the above embodiments by those of ordinary skill in the art within the scope of the present invention.
Claims (11)
1. A recipe recommendation method, comprising the steps of:
acquiring historical cooking data of a target user, and acquiring first preference information of the target user according to the historical cooking data;
acquiring attribute data of a recipe, and acquiring a first recipe preference degree according to the first preference information and the attribute data of the recipe;
and acquiring interactive data of a recipe, acquiring a target recipe for the target user according to the interactive data of the recipe and the preference degree of the first recipe, and recommending the target recipe to the target user.
2. The recipe recommendation method according to claim 1, wherein the obtaining a first recipe preference based on the first preference information and the attribute data of the recipe comprises:
acquiring the similarity between the recipe and the first preference information according to the attribute data of the recipe;
and taking the similarity as a first recipe preference.
3. The recipe recommendation method according to claim 1, wherein the obtaining a target recipe for the target user according to the interaction data of the recipe and the first recipe preference comprises:
acquiring the heat degree of the recipe according to the interactive data of the recipe;
sorting the recipes according to the heat degree to obtain a first recipe popularity;
weighting the preference degree and the popularity degree of the first recipe to obtain a recommendation index of the recipe;
and sorting the recommendation indexes of the recipes, and selecting the recipe with the recommendation index larger than a preset threshold value as a target recipe.
4. The recipe recommendation method according to claim 1, wherein the obtaining a target recipe for the target user according to the interaction data of the recipe and the first recipe preference comprises:
acquiring users with similar behaviors according to the interaction data of the recipes;
acquiring second preference information of the users with similar behaviors, and acquiring second recipe preference according to the second preference information;
weighting the first recipe preference degree and the second recipe preference degree to obtain a recommendation index of the recipe;
and sorting the recommendation indexes of the recipes, and selecting the recipe with the recommendation index larger than a preset threshold value as a target recipe.
5. The recipe recommendation method according to any one of claims 1 to 4, further comprising:
acquiring a first recipe popularity based on the recipe popularity and a second preference based on similar behavior users according to the interaction data of the recipes;
weighting the first recipe preference degree, the first recipe popularity degree and the second recipe preference degree to obtain a recommendation index of the recipe;
and sorting the recommendation indexes of the recipes, and selecting the recipe with the recommendation index larger than a preset threshold value as a target recipe.
6. The recipe recommendation method according to claim 5, wherein the weighting process comprises:
and acquiring weight data set by a user, and weighting the first recipe preference degree, the first recipe popularity degree and/or the second recipe preference degree according to the weight data.
7. The recipe recommendation method according to claim 6, further comprising, after said recommending the target recipe to the target user:
identifying the cooking times of the user on the target recipe;
adjusting the weight of the first recipe preference, the first recipe popularity, and/or the second recipe preference according to the number of cookings.
8. The recipe recommendation method according to claim 6, further comprising, after said recommending the target recipe to the target user:
acquiring feedback data of the target recipe;
and adjusting the weight of the preference of the first recipe, the popularity of the first recipe and/or the preference of the second recipe according to the feedback data of the recipe.
9. A recipe recommendation apparatus comprising:
the first acquisition module is used for acquiring historical cooking data of a target user and acquiring first preference information of the target user according to the historical cooking data;
the second acquisition module is used for acquiring attribute data of the recipe and acquiring preference degree of the first recipe according to the first preference information and the attribute data of the recipe;
and the recommending module is used for acquiring interactive data of the recipe, acquiring a target recipe for the target user according to the interactive data of the recipe and the preference degree of the first recipe, and recommending the target recipe to the target user.
10. An electronic device, characterized in that it comprises a recommendation means for recipes as claimed in claim 9.
11. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out a method of recommending recipes according to any one of claims 1 to 8.
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