Package recommendation method and device, equipment and storage medium

Abstract

The invention discloses a package recommendation method and device, equipment and a storage medium, and the method comprises the steps: obtaining a reference package set when a package replacement demand of a target user is received; based on any reference package, inputting user attribute information corresponding to the target user, historical package attribute information, historical usage information and first product attribute information of the reference package into a first prediction model to obtain a first probability, inputting the user attribute information, the historical package attribute information, the historical usage information and second product attribute information of the reference package into a second prediction model to obtain a second probability, and calculating a target probability based on the first probability and the second probability; and selecting and recommending a target package from the reference package set based on the target probability of each reference package. According to the package recommendation method and device, when package recommendation is carried out, the whole package is disassembled into a plurality of basic products, and the order probability of the whole package is calculated based on the order probability of the basic products, so that the package recommendation result can better meet user requirements.

Description

Technical Field

[0001] The present application relates to, but is not limited to, the field of data processing technology, and in particular to a package recommendation method, apparatus, device, and storage medium. Background Art

[0002] Telecom operators regularly release new telecom packages to meet the diverse needs of users. When a user needs to replace their current package, the telecom operator will prioritize recommending a new telecom package to the user. The existing package recommendation algorithm determines which packages in the new package set are more in line with the user's preferences based on the user's historical package history, and then recommends packages that meet the user's preferences to the user. In other words, the existing recommendation method considers the package as a whole and makes recommendations. A package is usually composed of multiple different basic products (including a main product PO and multiple additional products SO). However, the attractiveness of a package usually comes from a core product (main product or by-product) in the package, and the remaining services are passively accepted by the user. Recommending packages to users according to the existing recommendation method can easily mask the user's preference for specific services, resulting in recommendation results that cannot meet user needs and a poor user experience. Summary of the Invention

[0003] The embodiments of the present application provide a package recommendation method, apparatus, device, and storage medium, so that the package recommendation results can better meet user needs and improve user experience.

[0004] In a first aspect, an embodiment of the present application provides a package recommendation method, comprising:

[0005] When receiving a package change request from a target user, obtaining a reference package set, wherein the reference package set includes a plurality of different reference packages, each of which consists of a main product and at least one additional product;

[0006] Based on any of the reference packages, user attribute information, historical package attribute information, historical usage information, and first product attribute information of the reference package corresponding to the target user are input into a first prediction model to obtain a first probability; the user attribute information, the historical package attribute information, the historical usage information, and second product attribute information of the reference package are input into a second prediction model to obtain a second probability; and a target probability is calculated based on the first and second probabilities, wherein the first product attribute information is product attribute information of the main product of the reference package, the second product attribute information is product attribute information of the main product and additional products of the reference package, the first probability is the probability that the target user will subscribe to the main product of the reference package, and the second probability is the probability that the target user will subscribe to the additional product if they have already subscribed to the main product of the reference package;

[0007] A target package is selected from the reference package set based on the target probability of each reference package, and the target package is recommended to the target user.

[0008] In some embodiments, obtaining a reference package set includes:

[0009] Obtaining an initial package set, wherein each candidate package in the initial package set corresponds to a package type label;

[0010] Selecting a candidate label from all the package type labels based on the reference type label of the target user's current package and the historical package attribute information;

[0011] The reference package set is formed based on the candidate packages corresponding to the candidate tags.

[0012] In some embodiments, selecting a target package from the reference package set based on the target probability of each of the reference packages includes:

[0013] determining a preset probability threshold;

[0014] The reference package in the reference package set whose target probability exceeds the preset probability threshold is determined as the target package.

[0015] In some embodiments, there are multiple target packages, and recommending the target packages to the target user includes:

[0016] Based on the target probabilities corresponding to the target packages, the target packages are reordered in descending order of numerical values;

[0017] Selecting a preset number of top-ranked packages from the sorted target packages to form a recommendation list;

[0018] The recommendation list is sent to the target user, or the order of the target packages in the recommendation list is adjusted based on preset popular package elements, and the recommendation list after the order adjustment is sent to the target user.

[0019] In some embodiments, adjusting the order of the target package in the recommendation list based on preset popular package elements, and sending the adjusted recommendation list to the target user, includes:

[0020] Determine the attribute content of each product in each target package, and calculate the similarity between the popular package elements and the attribute content;

[0021] The order of the target packages in the recommendation list is adjusted in descending order of similarity, and the adjusted recommendation list is sent to the target user.

[0022] In some embodiments, calculating the target probability based on the first probability and the second probability includes:

[0023] When the number of the additional product is 1, determining a first product of the first probability and the second probability as the target probability;

[0024] In the case that there are multiple additional products, second products of all the second probabilities are calculated, and the product of the second products and the first probability is determined as the target probability.

[0025] In some embodiments, before inputting the user attribute information, historical package attribute information, historical usage information, and first product attribute information of the candidate package corresponding to the target user into the first prediction model to obtain the first probability, the method further includes:

[0026] Get the preset data preprocessing algorithm;

[0027] The user attribute information, the historical package attribute information, the historical usage information and the first product attribute information of the candidate package are preprocessed based on the data preprocessing algorithm.

[0028] In a second aspect, an embodiment of the present application provides a control device comprising at least one control processor and a memory for communicating with the at least one control processor; the memory stores instructions that can be executed by the at least one control processor, and the instructions are executed by the at least one control processor to enable the at least one control processor to execute the package recommendation method as described in the first aspect.

[0029] In a third aspect, an embodiment of the present application further provides an electronic device comprising the control device of the second aspect.

[0030] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium storing computer-executable instructions, wherein the computer-executable instructions are used to execute the package recommendation method as described in the first aspect.

[0031] Embodiments of the present application provide a package recommendation method, apparatus, device, and storage medium. The method includes: upon receiving a package change request from a target user, obtaining a reference package set, wherein the reference package set includes multiple different reference packages, each of the reference packages consisting of a main product and at least one additional product; based on any of the reference packages, inputting user attribute information, historical package attribute information, historical usage information, and first product attribute information of the reference package corresponding to the target user into a first prediction model to obtain a first probability; inputting the user attribute information, the historical package attribute information, the historical usage information, and second product attribute information of the reference package into a second prediction model to obtain a second probability; and calculating a target probability based on the first and second probabilities, wherein the first product attribute information is product attribute information of the main product of the reference package, the second product attribute information is product attribute information of the main product and additional products of the reference package, the first probability is the probability that the target user will subscribe to the main product of the reference package, and the second probability is the probability that the target user will subscribe to the additional product if they have already subscribed to the main product of the reference package; and selecting a target package from the reference package set based on the target probabilities of each reference package, and recommending the target package to the target user. According to the solution provided in the embodiment of the present application, when making package recommendations, the overall package is broken down into multiple basic products, and the ordering probability of the overall package is calculated based on the ordering probability of the basic products, which effectively avoids masking the user's preference for a certain product, so that the package recommendation results can be more in line with user needs and improve user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 This is a flowchart of the steps of a package recommendation method provided by an embodiment of the present application;

[0033] Figure 2 This is a structural diagram of a control device provided in another embodiment of the present application. DETAILED DESCRIPTION

[0034] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0035] It is understood that although the device schematics illustrate functional module divisions and the flowcharts illustrate logical sequences, in certain circumstances, the steps shown or described may be performed in a sequence that differs from the module divisions in the device or the sequence in the flowcharts. The terms "first," "second," and the like in the specification, claims, or accompanying drawings are used to distinguish similar items and are not necessarily used to describe a specific sequence or precedence.

[0036] Telecom operators regularly release new telecom packages to meet the diverse needs of users. When a user needs to replace their current package, the telecom operator will prioritize recommending a new telecom package to the user. The existing package recommendation algorithm determines which packages in the new package set are more in line with the user's preferences based on the user's historical package history, and then recommends packages that meet the user's preferences to the user. In other words, the existing recommendation method considers the package as a whole and makes recommendations. A package is usually composed of multiple different basic products (including a main product PO and multiple additional products SO). However, the attractiveness of a package usually comes from a core product (main product or by-product) in the package, and the remaining services are passively accepted by the user. Recommending packages to users according to the existing recommendation method can easily mask the user's preference for specific services, resulting in recommendation results that cannot meet user needs and a poor user experience.

[0037] To solve the above-mentioned problems, the embodiments of the present application provide a package recommendation method, apparatus, device and storage medium, the method comprising: upon receiving a package change request from a target user, obtaining a reference package set, wherein the reference package set includes a plurality of different reference packages, each of which consists of a main product and at least one additional product; based on any of the reference packages, inputting user attribute information, historical package attribute information, historical usage information and first product attribute information of the reference package corresponding to the target user into a first prediction model to obtain a first probability; inputting the user attribute information, the historical package attribute information, the historical usage information and second product attribute information of the reference package into a first prediction model to obtain a first probability; The product attribute information is input into the second prediction model to obtain a second probability, and the target probability is calculated based on the first probability and the second probability, wherein the first product attribute information is the product attribute information of the main product of the reference package, and the second product attribute information is the product attribute information of the main product and the additional product of the reference package, the first probability is the probability that the target user will order the main product of the reference package, and the second probability is the probability that the target user will order the additional product if he has already ordered the main product of the reference package; based on the target probability of each of the reference packages, a target package is selected from the reference package set, and the target package is recommended to the target user. According to the solution provided in the embodiment of the present application, when making a package recommendation, the overall package is disassembled into multiple basic products, and the ordering probability of the overall package is calculated based on the ordering probability of the basic products, which effectively avoids masking the user's preference for a certain product, so that the package recommendation results can better meet user needs and improve user experience.

[0038] The embodiments of the present application are further described below with reference to the accompanying drawings.

[0039] refer to Figure 1 , Figure 1 This is a flowchart of the steps of a package recommendation method provided by an embodiment of the present application. The embodiment of the present application provides a package recommendation method, which includes but is not limited to the following steps:

[0040] Step S10: When a package change request from a target user is received, a reference package set is obtained, wherein the reference package set includes a plurality of different reference packages, and each reference package consists of a main product and at least one additional product.

[0041] Specifically, the package recommendation method of this embodiment is applied to a telecommunications user management system, and the main product and the additional product are both package product services preset in the telecommunications user management system, such as traffic products or call fee products.

[0042] In some embodiments, Figure 1 The acquisition of the reference package set in step S10 includes but is not limited to the following steps:

[0043] Step S11, obtaining an initial package set, wherein each candidate package in the initial package set corresponds to a package type label;

[0044] Step S12, selecting a candidate tag from all package type tags based on the reference type tag of the target user's current package and historical package attribute information;

[0045] Step S13: forming a reference package set based on the candidate packages corresponding to the candidate tags.

[0046] It is understandable that since hundreds or even thousands of telecom packages are pre-set in the telecom user management system, if each target user who wishes to change their package were to traverse and screen each pre-set telecom package based on the characteristics of the target user, a large amount of system resources would be consumed, the screening method would be non-targeted, and the efficiency of package recommendations would be affected, thereby affecting the user experience. Based on this, this embodiment uses the reference type tag of the target user's current package and historical package attribute information. Based on this, after receiving the target user's package change request, this embodiment first determines the target user's user profile in the system based on the reference type tag of the target user's current package and historical package attribute information. For example, based on user group classification, whether it is an individual user or a family user, and consumption behavior, whether it is a low-demand user or a medium-to-high-volume user, etc. Since each candidate package in the system has a package type tag, which contains the corresponding candidate package content keywords, keyword matching is performed based on the package type tag with the reference type tag of the target user's current package and historical package attribute information. The tags that successfully match are determined as candidate tags, and a reference package set is formed based on the candidate packages corresponding to the candidate tags, achieving rapid preliminary screening and providing effective support for subsequently improving the efficiency of package recommendations.

[0047] Step S20: Based on any reference package, the user attribute information, historical package attribute information, historical usage information and the first product attribute information of the reference package corresponding to the target user are input into the first prediction model to obtain a first probability; the user attribute information, historical package attribute information, historical usage information and the second product attribute information of the reference package are input into the second prediction model to obtain a second probability; the target probability is calculated based on the first probability and the second probability, wherein the first product attribute information is the product attribute information of the main product of the reference package, the second product attribute information is the product attribute information of the main product and the additional product of the reference package, the first probability is the probability that the target user orders the main product of the reference package, and the second probability is the probability that the target user orders the additional product if he has already ordered the main product of the reference package.

[0048] It can be understood that, based on the product and service composition of the reference package (for example, consisting of a PO and an SO, both of which are basic product services), the first probability of the target user subscribing to the main product PO of the reference package and the second probability of the target user subscribing to the additional product SO when the target user has already subscribed to the main product PO of the reference package are calculated respectively. The first probability and the second probability are combined to calculate the target user's subscription probability for the entire package. On the one hand, it can effectively avoid masking the user's preference for a certain product. On the other hand, it can avoid treating the combination of PO and SO as a whole, and failing to collect training data for the subscription probability prediction model of the entire package, thereby affecting the accuracy of the target probability. In this way, this embodiment decomposes the entire package into multiple basic products, and calculates the subscription probability of the entire package based on the subscription probability of the basic products. This method can make the package recommendation results more in line with user needs and improve user experience.

[0049] It should be noted that this embodiment transforms the overall package recommendation problem into two sub-problems, each solved using two models (a first prediction model and a second prediction model). The training data required to train these two prediction models is satisfied by the target user's current package credit. The prediction models of this embodiment can utilize supervised algorithms such as machine learning or deep learning, without further limitation. For example, the method for constructing training data for the first prediction model P (user.po) is to use the target user's user attribute information (e.g., age, gender, occupation, etc.), usage information (e.g., call duration, data usage), PO specifications, and other user- and PO product-related information as model inputs, and to output the probability of the user ordering the PO. The method for constructing training data for the second prediction model P (user.po.so) is to use the target user's user attribute information (e.g., age, gender, occupation, etc.), usage information (e.g., call duration, data usage), PO and SO specifications, and other user- and PO / SO product-related information as model inputs, and to output the probability of the target user ordering the additional product SO if they have already ordered the reference package's main product PO.

[0050] It should be noted that this embodiment does not limit the specific number of additional products SO. Figure 1 Calculating the target probability based on the first probability and the second probability in step S20 includes but is not limited to the following steps:

[0051] Step S21, when the number of additional products is 1, determining a first product of the first probability and the second probability as a target probability;

[0052] Step S22: When there are multiple additional products, calculate the second products of all the second probabilities, and determine the product of the second products and the first probability as the target probability.

[0053] Specifically, when the number of additional products is 1, the first product of the first probability and the second probability is determined as the target probability, which is calculated according to the following formula: P(user.po)*P(so|user.po); when the number of additional products is multiple, the second product of all the second probabilities is calculated, and the product between the second product and the first probability is determined as the target probability, which is calculated according to the following formula: P(user.po)*P(so1|user.po)*...*P(so n |user.po), where P(user.po) is the first probability, P(so|user.po) is the second probability when the number of additional products is 1; P(so1|user.po), ..., P(so n |user.po) is the second probability when the number of additional products is multiple, and user is the target user.

[0054] Additionally, in some embodiments, executing Figure 1 Before step S20, the package recommendation method of the embodiment of the present application further includes but is not limited to the following steps:

[0055] Step S41, obtaining a preset data preprocessing algorithm;

[0056] Step S42: pre-process the user attribute information, historical package attribute information, historical usage information and the first product attribute information of the candidate package based on a data pre-processing algorithm.

[0057] It is understandable that the initially acquired user attribute information, historical package attribute information, historical usage information and the first product attribute information of the candidate package contain irrelevant information such as null values ​​or error values. Therefore, this embodiment performs data preprocessing on the user attribute information, historical package attribute information, historical usage information and the first product attribute information of the candidate package through a preset data preprocessing algorithm, which can restore useful real information, enhance the detectability of relevant information and simplify the data to the maximum extent, thereby improving the reliability of subsequent applications after model processing and reducing the possibility of overfitting of the network model.

[0058] It should be noted that the data preprocessing algorithm of this embodiment may include: data cleaning, text data information extraction and recognition, formatting, etc., and no further restrictions are imposed here.

[0059] Step S30 : selecting a target package from the reference package set based on the target probability of each reference package, and recommending the target package to the target user.

[0060] Specifically, in some embodiments, Figure 1The step S30 of selecting a target package from the reference package set based on the target probability of each reference package includes but is not limited to the following steps:

[0061] Step S31, determining a preset probability threshold;

[0062] Step S32: Determine the reference package in the reference package set whose target probability exceeds a preset probability threshold as the target package.

[0063] It can be understood that the reference packages in the reference package set whose target probability exceeds the preset probability threshold have a higher subscription probability for the corresponding target users. Determining these reference packages as target packages can provide effective guarantees for subsequent package recommendation results to meet user needs, effectively improving the user experience of the target users.

[0064] Specifically, in some embodiments, there are multiple target packages. Figure 1 The process of recommending a target package to a target user in step S30 includes but is not limited to the following steps:

[0065] Step S33, based on the target probabilities corresponding to the target packages, reorder the target packages in descending order of numerical values;

[0066] Step S34, selecting a preset number of top-ranked packages from the ranked target packages to form a recommendation list;

[0067] Step S35: Send the recommendation list to the target user, or adjust the order of the target package in the recommendation list based on the preset popular package elements, and send the adjusted recommendation list to the target user.

[0068] It is understood that in this embodiment, based on the target probabilities corresponding to the target packages, the reference packages are reordered in descending numerical order, and a preset number of packages ranked at the top of the order are selected from the ordered target packages to determine them as target packages. In this way, the package subscription probabilities corresponding to the determined target packages are sorted from high to low to form a recommendation list, and the recommendation list is sent to the target user. Alternatively, the order of the target packages in the recommendation list is adjusted based on preset popular package elements, and the adjusted recommendation list is sent to the target user. In this way, the recommendation list sent to the target user can better meet the user's actual needs.

[0069] Specifically, in some embodiments, the method of adjusting the order of the target package in the recommendation list based on the preset popular package elements in step S35 and sending the adjusted recommended list to the target user includes but is not limited to the following steps:

[0070] Step S351, determining the attribute content of each product in each target package, and calculating the similarity between the popular package elements and the attribute content;

[0071] Step S352: Adjust the order of the target packages in the recommendation list in descending order of similarity, and send the adjusted recommendation list to the target user.

[0072] It is understandable that individual user behavior data may lag behind market changes, while popular package elements imply group preferences and operator strategies. The recommendation list before adjustment already contains recommendation results that meet the user's real needs. On this basis, the attribute content of each product in each target package in the recommendation list is determined, and the second similarity between the popular package elements and the attribute content is calculated. The order of the target packages in the recommendation list is adjusted in descending order according to the second similarity, and the adjusted recommendation list is sent to the target user. That is, the recommendation list adjusted based on the current popular package elements can dynamically adapt to market trends and user group preferences, and better optimize the user experience.

[0073] like Figure 2 As shown, Figure 2 : is a structural diagram of a control device provided in one embodiment of the present application. The present invention also provides a control device 200, comprising:

[0074] The processor 210 may be implemented as a general-purpose central processing unit (CPU), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is configured to execute relevant programs to implement the technical solutions provided in the embodiments of the present application.

[0075] The memory 220 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 220 can store an operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 220 and is called by the processor 210 to execute the package recommendation method of the embodiments of this application.

[0076] Input / output interface 230, used to implement information input and output;

[0077] Communication interface 240, used to implement communication interaction between the apparatus and other devices, which can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WiFi, Bluetooth, etc.);

[0078] bus 250 , which transmits information between the various components of the device (e.g., processor 210 , memory 220 , input / output interface 230 , and communication interface 240 );

[0079] The processor 210 , the memory 220 , the input / output interface 230 and the communication interface 240 are connected to each other in communication within the device via the bus 250 .

[0080] In addition, an embodiment of the present application further provides an electronic device, including the control device 200 of the above embodiment.

[0081] In addition, an embodiment of the present application further provides a storage medium, which is a computer-readable storage medium and stores a computer program. When the computer program is executed by a processor, the above-mentioned package recommendation method is implemented.

[0082] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely located relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned networks include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof. The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and may be located in one place, or may be distributed to multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment.

[0083] Those skilled in the art will appreciate that all or some of the steps and systems in the method disclosed above can be implemented as software, firmware, hardware, and appropriate combinations thereof. Some physical components or all physical components can be implemented as software executed by a processor, such as a central processing unit, a digital signal processor, or a microprocessor, or implemented as hardware, or implemented as an integrated circuit, such as an application-specific integrated circuit. Such software can be distributed on a computer-readable medium, and the computer-readable medium can include computer storage media (or non-transitory media) and communication media (or temporary media). As known to those skilled in the art, the term computer storage media is included in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data) and is volatile and non-volatile, removable, and non-removable. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory, or other memory technology, CD-ROM, digital versatile disks (DVD), or other optical disk storage, magnetic cassettes, magnetic tapes, disk storage, or other magnetic storage devices, or any other medium that can be used to store desired information and can be accessed by a computer. Furthermore, as is well known to those skilled in the art, communication media typically includes computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.

[0084] The above is a specific description of the preferred implementation of the present invention, but the present invention is not limited to the above implementation. Those skilled in the art can also make various equivalent modifications or substitutions under the shared conditions that do not violate the spirit of the present invention. These equivalent modifications or substitutions are all included in the scope defined by the claims of the present invention.

Claims

1. A package recommendation method, characterized in that: include: When receiving a package change request from a target user, obtaining a reference package set, wherein the reference package set includes a plurality of different reference packages, each of which consists of a main product and at least one additional product; Based on any of the reference packages, user attribute information, historical package attribute information, historical usage information, and first product attribute information of the reference package corresponding to the target user are input into a first prediction model to obtain a first probability; the user attribute information, the historical package attribute information, the historical usage information, and second product attribute information of the reference package are input into a second prediction model to obtain a second probability; and a target probability is calculated based on the first and second probabilities, wherein the first product attribute information is product attribute information of the main product of the reference package, the second product attribute information is product attribute information of the main product and additional products of the reference package, the first probability is the probability that the target user will subscribe to the main product of the reference package, and the second probability is the probability that the target user will subscribe to the additional product if they have already subscribed to the main product of the reference package; A target package is selected from the reference package set based on the target probability of each reference package, and the target package is recommended to the target user.

2. The method for recommending a package according to claim 1, wherein: The obtaining of the reference package set includes: Obtaining an initial package set, wherein each candidate package in the initial package set corresponds to a package type label; Selecting a candidate label from all the package type labels based on the reference type label of the target user's current package and the historical package attribute information; The reference package set is formed based on the candidate packages corresponding to the candidate tags.

3. The method for recommending a meal plan according to claim 1, wherein: Selecting a target package from the reference package set based on the target probability of each of the reference packages includes: determining a preset probability threshold; The reference package in the reference package set whose target probability exceeds the preset probability threshold is determined as the target package.

4. The method for recommending a meal plan according to claim 1 or 3, wherein: There are multiple target packages, and recommending the target packages to the target user includes: Based on the target probabilities corresponding to the target packages, the target packages are reordered in descending order of numerical values; Selecting a preset number of top-ranked packages from the sorted target packages to form a recommendation list; The recommendation list is sent to the target user, or the order of the target packages in the recommendation list is adjusted based on preset popular package elements, and the recommendation list after the order adjustment is sent to the target user.

5. The method for recommending a meal plan according to claim 4, wherein: Adjusting the order of the target package in the recommendation list based on preset popular package elements, and sending the adjusted recommendation list to the target user, includes: Determine the attribute content of each product in each target package, and calculate the similarity between the popular package elements and the attribute content; The order of the target packages in the recommendation list is adjusted in descending order of similarity, and the adjusted recommendation list is sent to the target user.

6. The method for recommending a meal plan according to claim 1, wherein: Calculating a target probability based on the first probability and the second probability includes: When the number of the additional product is 1, determining a first product of the first probability and the second probability as the target probability; In the case that there are multiple additional products, second products of all the second probabilities are calculated, and the product of the second products and the first probability is determined as the target probability.

7. The method for recommending a meal plan according to claim 1, wherein: Before inputting the user attribute information, historical package attribute information, historical usage information, and first product attribute information of the candidate package corresponding to the target user into the first prediction model to obtain the first probability, the method further includes: Get the preset data preprocessing algorithm; The user attribute information, the historical package attribute information, the historical usage information and the first product attribute information of the candidate package are preprocessed based on the data preprocessing algorithm.

8. A control device, characterized in that: It includes at least one control processor and a memory for communicating with the at least one control processor; the memory stores instructions that can be executed by the at least one control processor, and the instructions are executed by the at least one control processor to enable the at least one control processor to execute the package recommendation method as described in any one of claims 1 to 7.

9. An electronic device, characterized in that: Comprising the control device according to claim 8.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are used to enable a computer to execute the package recommendation method according to any one of claims 1 to 7.

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