Landing page generation method and device and related equipment

Abstract

The invention provides a landing page generation method and device and related equipment, and the method comprises the steps: receiving target input; obtaining multiple pieces of to-be-recommended user tag information according to the user set information, wherein users corresponding to the to-be-recommended user tag information are to-be-recommended users in the user set information; the multiple pieces of to-be-recommended user tag information are input into a preset model, material information corresponding to each piece of to-be-recommended user tag information is generated, multiple pieces of material information are obtained, and the preset model is a model obtained through pre-training and used for generating the material information; and generating the target landing page according to the various kinds of material information and the parameter information of the target landing page. Therefore, the generation efficiency of the target landing page is improved.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of electronic technology, and in particular, to a landing page generation method, device and related equipment. Background Art

[0002] With the continuous development of Internet technology, the application scope of advertisements in people's lives is becoming more and more extensive. Landing pages can usually be used as a carrier of advertising content to improve the conversion rate of advertisements. In actual use, landing pages usually need to be manually developed and generated by users, resulting in low efficiency in generating landing pages. Summary of the invention

[0003] The embodiments of the present application provide a landing page generation method, device and related equipment to solve the problem of low efficiency in generating landing pages.

[0004] To solve the above problems, this application is implemented as follows:

[0005] In a first aspect, an embodiment of the present application provides a landing page generation method, comprising:

[0006] Receive a target input, the target input is used to input demand information, the demand information is used to generate a target landing page, the demand information includes user set information and parameter information of the target landing page, the user set information includes user information of users to be recommended to the target landing page, and the parameter information is used to represent display parameter information of the target landing page;

[0007] Acquire multiple pieces of to-be-recommended user tag information according to the user set information, wherein the users corresponding to the to-be-recommended user tag information are the to-be-recommended users in the user set information;

[0008] Inputting the plurality of user tag information to be recommended into a preset model, generating material information corresponding to each user tag information to be recommended, and obtaining a plurality of material information, wherein the preset model is a pre-trained model for generating material information;

[0009] The target landing page is generated according to the multiple material information and the parameter information of the target landing page.

[0010] In a second aspect, an embodiment of the present application provides a landing page generation device, including:

[0011] A first receiving module is used to receive a target input, wherein the target input is used to input demand information, and the demand information is used to generate a target landing page, wherein the demand information includes user set information and parameter information of the target landing page, wherein the user set information includes user information of users to be recommended to the target landing page, and the parameter information is used to represent display parameter information of the target landing page;

[0012] An acquisition module, configured to acquire a plurality of user tag information to be recommended according to the user set information, wherein the users corresponding to the user tag information to be recommended are the users to be recommended in the user set information;

[0013] A first generating module is used to input the plurality of user tag information to be recommended into a preset model, generate material information corresponding to each user tag information to be recommended, and obtain a plurality of material information, wherein the preset model is a pre-trained model for generating material information;

[0014] The second generating module is used to generate the target landing page according to the multiple material information and the parameter information of the target landing page.

[0015] In a third aspect, an embodiment of the present application further provides an electronic device, comprising: a memory, a processor, and a program stored in the memory and executable on the processor; the processor is used to read the program in the memory to implement the steps in the method described in the first aspect above.

[0016] In a fourth aspect, an embodiment of the present application further provides a readable storage medium for storing a program, which, when executed by a processor, implements the steps in the method described in the first aspect above.

[0017] In a fifth aspect, an embodiment of the present application further provides a computer program product, comprising computer instructions, which, when executed by a processor, implement the steps in the method described in the first aspect above.

[0018] In an embodiment of the present application, a target input is received, and multiple user label information to be recommended is obtained based on the user set information, and the users corresponding to the user label information to be recommended are the users to be recommended in the user set information; the multiple user label information to be recommended are input into a preset model, and material information corresponding to each user label information to be recommended is generated to obtain multiple material information, and the preset model is a pre-trained model for generating material information; the target landing page is generated based on the multiple material information and the parameter information of the target landing page. In this way, the target landing page can be generated in a high-quality, personalized and automatic manner, thereby improving the generation efficiency of the target landing page and improving the generation quality of the target landing page. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the description of the embodiments of the present application will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0020] Figure 1 This is one of the flow charts of the landing page generation method provided in the embodiment of the present application;

[0021] Figure 2 This is the second flowchart of the landing page generation method provided in the embodiment of the present application;

[0022] Figure 3 It is a structural schematic diagram of a landing page generating device provided in an embodiment of the present application;

[0023] Figure 4 It is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0024] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0025] The terms "first", "second" etc. in the embodiments of the present application are used to distinguish similar objects, and need not be used to describe a specific order or sequential order. In addition, the terms "include" and "have" and any variation thereof are intended to cover non-exclusive inclusions, for example, the process, method, system, product or equipment comprising a series of steps or units need not be limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or equipment. In addition, "and / or" is used in the present application to represent at least one of connected objects, such as A and / or B and / or C, representing the inclusion of separate A, separate B, separate C, and A and B all exist, B and C all exist, A and C all exist, and 7 situations that A, B and C all exist.

[0026] See also Figure 1 , Figure 1 is a flow chart of a landing page generation method provided by an embodiment of the present application. The present application embodiment can be applied to electronic devices, such as Figure 1 As shown, the landing page generation method may include the following steps:

[0027] Step 101: Receive target input, where the target input is used to input demand information, and the demand information is used to generate a target landing page. The demand information includes user set information and parameter information of the target landing page, and the user set information includes user information of users to be recommended to the target landing page. The parameter information is used to represent display parameter information of the target landing page.

[0028] Among them, the demand information can be information used to generate a target landing page, and the display content of the target landing page can be the content of the target advertisement. In this way, the above-mentioned demand information can also be referred to as the generation demand of the target advertisement.

[0029] At the same time, since the demand information includes user set information, and the user set information includes the user information of the users to be recommended of the target landing page, the target landing page can be pushed to the users to be recommended to enhance the promotion effect of the target advertisement, and the above-mentioned users to be recommended can also be referred to as the recommended group of the target advertisement.

[0030] It should be noted that the user information of the user to be recommended can be used to represent the preference information of the user to be recommended. Optionally, the user information can include at least one of the following: user tag information, user preference information for landing pages, information about content that users frequently browse, etc. That is, through the user information, the target landing page that is finally generated can be more in line with the personal habits of the user to be recommended, thereby enhancing the user experience.

[0031] For example: when the user information is tagged "literary", the generated material information can be image information such as movies, coffee, photography, etc., and then the target landing page is generated based on the image information such as movies, coffee, photography, etc., that is, the display content of the target landing page may include image information such as movies, coffee, photography, etc. In this way, when the obtained target landing page is presented to the user, it is more in line with the user's preference information, enhances the user's usage experience, and the user is more likely to click on the advertisements included in the displayed content of the target landing page, thereby further improving the conversion rate of the advertisements included in the displayed content of the target landing page.

[0032] Among them, the parameter information can be used to represent the display parameter information of the target landing page, and the specific content of the display parameter information is not limited here. Optionally, the display parameter information may include at least one of the following: display content and display style. The display content may include at least one of text content and image content. The display style may also be referred to as a visual style. For example, the display style may be anime style or realistic style.

[0033] Step 102: Acquire multiple pieces of to-be-recommended user tag information according to the user set information, wherein the users corresponding to the to-be-recommended user tag information are the to-be-recommended users in the user set information.

[0034] Among them, the user collection information may include user information of multiple users to be recommended. Optionally, the user information may be label information, that is, each user to be recommended may correspond to at least one user label information to be recommended. For example, user A to be recommended may correspond to user label information A1 and A2 to be recommended, and user B to be recommended may correspond to user label information B1, B2 and B3 to be recommended.

[0035] It should be noted that the user tag information to be recommended may be pre-generated and stored on the electronic device in the embodiment of the present application; or, the user tag information to be recommended may be pre-acquired from other electronic devices and stored on the electronic device in the embodiment of the present application, and the other electronic device may be a cloud server.

[0036] Specifically, when the tag information of the user to be recommended is stored on the electronic device, it can be stored in a tag database on the electronic device, and the tag database can also be called a tag library.

[0037] Step 103: input the plurality of user tag information to be recommended into a preset model, generate material information corresponding to each user tag information to be recommended, and obtain a plurality of material information. The preset model is a pre-trained model for generating material information.

[0038] Among them, the preset model can be a pre-trained model for generating material information. The specific steps for training the preset model can be referred to the following steps: obtaining multiple sample user label information from the sample pool, inputting the multiple sample user label information into the network model to be trained for the Nth training to generate multiple sample material information. When the matching rate between the multiple sample material information and the actual material information corresponding to the above-mentioned multiple sample user label information is greater than a preset value, the network model after the Nth training can be determined as the preset model.

[0039] As an optional implementation, the preset model is an artificial intelligence model.

[0040] Among them, the artificial intelligence model can be a model that adopts generative artificial intelligence (Artificial Intelligence Generated Content, AIGC) technology, so the above artificial intelligence model can also be called an AI model.

[0041] In the implementation manner of the present application, the preset model is an artificial intelligence model, which can further improve the generation efficiency of the target landing page and improve the intelligence level of the target landing page generation method.

[0042] The correspondence between the user tag information to be recommended and the material information can be understood as: the user tag information to be recommended and the material information can correspond one to one, for example: the user tag information to be recommended A1 corresponds to material information 1, and the user tag information to be recommended B1 corresponds to material information 2.

[0043] Step 104: Generate the target landing page based on the multiple material information and parameter information of the target landing page.

[0044] In an embodiment of the present application, through steps 101 to 104, a target input is received, and a plurality of user label information to be recommended is obtained according to the user set information, and the user corresponding to the user label information to be recommended is the user to be recommended in the user set information; the plurality of user label information to be recommended is input into a preset model, and material information corresponding to each user label information to be recommended is generated to obtain a plurality of material information, and the preset model is a pre-trained model for generating material information; the target landing page is generated according to the plurality of material information and the parameter information of the target landing page, so that the target landing page can be generated with high quality, in a personalized and automatic manner, thereby improving the generation efficiency of the target landing page and improving the generation quality of the target landing page, that is, the present embodiment can generate the target landing page with one click, and at the same time, reduce manpower cost and time consumption.

[0045] It should be noted that since the material information generated by the preset model corresponds to the user label information to be recommended, that is, different user label information to be recommended can correspond to different material information, this can increase the flexibility of the generation results of the user label information to be recommended, and at the same time, also improve the accuracy of the generation results of the user label information to be recommended.

[0046] In addition, the implementation mode of the present application can determine the tag information of the user to be recommended based on the demand information, and determine the material information according to the tag information of the user to be recommended, that is, different tag information of the user to be recommended can correspond to different material information. In this way, the personalization degree of the target landing page finally generated can be improved, and at the same time, the content of the generated target landing page can be made more in line with the needs of the user to be recommended, thereby improving the user experience and thus improving the conversion rate of the target advertisement.

[0047] As an optional implementation manner, the material information includes image material information and text material information, and the plurality of user tag information to be recommended is input into the artificial intelligence model to generate a plurality of material information, including:

[0048] Inputting the plurality of user tag information to be recommended into the artificial intelligence model to generate a plurality of image material information and a plurality of text material information;

[0049] Among them, the user tag information to be recommended, the image material information and the text material information correspond one to one.

[0050] Among them, the one-to-one correspondence between the user tag information to be recommended, the image material information and the text material information can be seen in Table 1. The user tag information to be recommended can be called user tag, the image material information can be called picture material, and the text material information can be called copywriting.

[0051] Table 1

[0052]

[0053]

[0054] In the implementation mode of the present application, since the material information includes image material information and text material information, multiple user label information to be recommended is input into the artificial intelligence model to generate multiple image material information and multiple text material information; the user label information to be recommended, the image material information and the text material information correspond one to one, so that the diversity of the material information generation types and the flexibility of the generation method can be increased.

[0055] As an optional implementation manner, generating the target landing page according to the multiple material information and parameter information of the target landing page includes:

[0056] Acquire, from a server for storing at least one landing page template, a landing page sample template having the highest matching degree with the parameter information of the target landing page, wherein the landing page template is a template for generating a landing page, and the landing page sample template is a template in the at least one landing page template;

[0057] Fill the various material information into corresponding positions in the landing page sample template to generate the target landing page.

[0058] Among them, at least one landing page sample template can be stored in the server, and the at least one landing page sample template can be traversed, and the matching degree between each landing page sample template and the parameter information of the target landing page can be calculated, so as to determine the landing page sample template with the highest matching degree, and then fill in various material information into the corresponding positions in the landing page sample template respectively to generate the target landing page.

[0059] It should be noted that when calculating the matching degree between each landing page sample template and the parameter information of the target landing page, please refer to the following statement for details: the parameter information of the target landing page can be of multiple types, and the parameter information of the landing page sample template can also be of multiple types. The more types of matches between the parameter information of the landing page sample template and the parameter information of the target landing page, the higher the matching degree of the landing page sample template. In this way, the landing page sample template with the highest matching degree can be screened out, and the landing page sample template can be determined as the landing page sample template with the highest matching degree with the parameter information of the target landing page.

[0060] In the implementation manner of the present application, since the parameter information of the landing page sample template has the highest match with the target landing page, a variety of material information is filled into the corresponding positions in the landing page sample template to generate the target landing page, thereby further improving the accuracy of the target landing page; at the same time, the present implementation manner can automatically select the landing page sample template, and can automatically fill in a variety of material information into the corresponding positions in the landing page sample template to generate the target landing page, thereby making the generation of the target landing page more efficient.

[0061] It should be noted that the specific method of filling various material information into corresponding positions in the landing page sample template to generate the target landing page is not limited here.

[0062] Optionally, a variety of material information can be randomly filled into corresponding positions in the landing page sample template to generate a target landing page.

[0063] As an optional implementation manner, each of the multiple material information carries identification information, and the filling of the multiple material information into corresponding positions in the landing page sample template to generate the target landing page includes:

[0064] Fill the multiple material information into corresponding positions in the landing page sample template in a preset order to generate the target landing page;

[0065] Wherein, when the identification information is digital information, the preset order is the order of the identification information from small to large, or the preset order is the order of the identification information from large to small.

[0066] Among them, the position on the landing page sample template used to fill in the material information may also have position identification information. For example: when the identification information is digital information, the position identification information of each position gradually increases from the top to the bottom of the landing page sample template, and the various material information can be sorted in a preset order, and the sorted various material information can be filled into each position in sequence according to the order of the position identification information from small to large, so as to generate a target landing page.

[0067] Alternatively, when the identification information is digital information, the sorted multiple material information can also be filled into each position in order from large to small according to the position identification information to generate a target landing page.

[0068] In the implementation mode of the present application, various material information are filled into corresponding positions in the landing page sample template according to a preset order to generate a target landing page. In this way, the filling method of various material information can be simplified, the consumption of computing resources can be reduced, and the generation efficiency of the target landing page can be further improved.

[0069] As an optional implementation manner, after filling the multiple material information into corresponding positions in the landing page sample template to generate the target landing page, the method further includes:

[0070] Receive a target input from a target user, where the target input is used to adjust at least part of the material information on the target landing page;

[0071] At least part of the material information on the target landing page is adjusted according to the target input to generate a revised target landing page.

[0072] The specific method of target input is not limited here. Optionally, the target input can be touch input, press input or voice input.

[0073] Among them, adjusting at least part of the material information on the target landing page according to the target input can be understood as: adjusting the position or content of at least part of the material information on the target landing page according to the target input, and the adjustment of the position can be: swapping the position of at least part of the material information on the target landing page according to the target input; and the adjustment of the content can be: modifying or replacing at least part of the material information according to the target input, for example: the image material information on the target landing page can be replaced, and for another example: the text material information on the target landing page can be modified.

[0074] In an implementation manner of the present application, the position of at least part of the material information on the target landing page is adjusted according to the target input to generate a revised target landing page. In this way, the position of at least part of the material information on the target landing page can be adjusted according to the target input of the target user, thereby further improving the accuracy of the target landing page.

[0075] It should be noted that after generating the revised target landing page, the files corresponding to the target landing page can be exported, and the export format of the files corresponding to the target landing page is not limited here. For example: the files corresponding to the target landing page can be in Hypertext Markup Language (HTML) file format; or, after generating the revised target landing page, the files corresponding to the target landing page can be sent to the target server so that the files corresponding to the target landing page can be published on the target server, and the above-mentioned target server can be a cloud server or a platform server for publishing landing pages.

[0076] In order to more fully illustrate the above embodiment, a specific embodiment is used as an example below. Figure 2 As shown, Figure 2 The user in the description may refer to the target user, the demand may refer to the demand information, the target audience may refer to the user to be recommended, the promotion product introduction may refer to the introduction of the target advertisement, the main content may refer to the display content of the target landing page, the visual style may refer to the display style of the target landing page, the system may refer to the electronic device used in the above embodiment, the user tag library may store multiple user tag information to be recommended, the AI ​​model may refer to the preset model, the picture and copy content may refer to a variety of material information, the template selection may refer to selecting the landing page sample template with the highest matching degree with the parameter information of the target landing page, the content filling may refer to filling a variety of material information into the corresponding positions in the landing page sample template, the local adjustment may refer to adjusting at least part of the material information on the target landing page according to the target input, and the publish / export page may refer to publishing or exporting the corrected target landing page.

[0077] See also Figure 3 , Figure 3 is a schematic diagram of the structure of the landing page generation device provided in the embodiment of the present application, such as Figure 3 As shown, the landing page generating device 300 includes:

[0078] The first receiving module 301 is used to receive a target input, wherein the target input is used to input demand information, and the demand information is used to generate a target landing page. The demand information includes user set information and parameter information of the target landing page, and the user set information includes user information of users to be recommended to the target landing page, and the parameter information is used to represent display parameter information of the target landing page;

[0079] An acquisition module 302 is used to acquire a plurality of user tag information to be recommended according to the user set information, wherein the users corresponding to the user tag information to be recommended are the users to be recommended in the user set information;

[0080] A first generating module 303 is used to input the plurality of user tag information to be recommended into a preset model, generate material information corresponding to each user tag information to be recommended, and obtain a plurality of material information, wherein the preset model is a pre-trained model for generating material information;

[0081] The second generating module 304 is used to generate the target landing page according to the multiple material information and the parameter information of the target landing page.

[0082] As an optional implementation manner, the material information includes image material information and text material information, and the first generating module 303 is further used to input the plurality of user tag information to be recommended into the artificial intelligence model to generate a plurality of the image material information and a plurality of the text material information;

[0083] Among them, the user tag information to be recommended, the image material information and the text material information correspond to each other one by one.

[0084] As an optional implementation, the second generation module 304 includes:

[0085] an acquisition submodule, configured to acquire, from a server for storing at least one landing page template, a landing page sample template having the highest matching degree with the parameter information of the target landing page, wherein the landing page template is a template for generating a landing page, and the landing page sample template is a template in the at least one landing page template;

[0086] A generation submodule is used to fill the various material information into corresponding positions in the landing page sample template to generate the target landing page.

[0087] As an optional implementation, each of the multiple material information carries identification information, and the generating submodule is further used to fill the multiple material information into corresponding positions in the landing page sample template in a preset order to generate the target landing page;

[0088] Wherein, when the identification information is digital information, the preset order is the order of the identification information from small to large, or the preset order is the order of the identification information from large to small.

[0089] As an optional implementation, the landing page generating device 300 further includes:

[0090] A second receiving module is used to receive a target input from a target user, where the target input is used to adjust at least part of the material information on the target landing page;

[0091] An adjustment module is used to adjust at least part of the material information on the target landing page according to the target input to generate a revised target landing page.

[0092] As an optional implementation, the preset model is an artificial intelligence model.

[0093] The landing page generation device 300 can realize the embodiment of the present application Figure 1 The various processes of the method embodiment and the same beneficial effects are achieved, and will not be described again here to avoid repetition.

[0094] The present application also provides an electronic device. Figure 4 , the electronic device may include a processor 401, a memory 402, and a program 4021 stored in the memory 402 and executable on the processor 401. When the program 4021 is executed by the processor 401, Figure 1 Any steps in the corresponding method embodiments and achieving the same beneficial effects will not be repeated here.

[0095] A person skilled in the art can understand that all or part of the steps of implementing the above-mentioned embodiment method can be completed by hardware related to program instructions, and the program can be stored in a readable medium. The present application also provides a readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the above-mentioned Figure 1 Any steps in the corresponding method embodiments can achieve the same technical effect and will not be repeated here to avoid repetition.

[0096] The storage medium may be a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0097] The present application also provides a computer program product, including computer instructions, which can achieve the above-mentioned Figure 1 Any steps in the corresponding method embodiments can achieve the same technical effect and will not be repeated here to avoid repetition.

[0098] The above is a preferred implementation of the embodiment of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles described in the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A landing page generation method, characterized in that: include: Receive a target input, the target input is used to input demand information, the demand information is used to generate a target landing page, the demand information includes user set information and parameter information of the target landing page, the user set information includes user information of users to be recommended to the target landing page, and the parameter information is used to represent display parameter information of the target landing page; Acquire multiple pieces of to-be-recommended user tag information according to the user set information, wherein the users corresponding to the to-be-recommended user tag information are the to-be-recommended users in the user set information; Inputting the plurality of user tag information to be recommended into a preset model, generating material information corresponding to each user tag information to be recommended, and obtaining a plurality of material information, wherein the preset model is a pre-trained model for generating material information; The target landing page is generated according to the multiple material information and the parameter information of the target landing page.

2. The method according to claim 1, characterized in that The material information includes image material information and text material information. The plurality of user tag information to be recommended is input into the artificial intelligence model to generate a plurality of material information, including: Inputting the plurality of user tag information to be recommended into the artificial intelligence model to generate a plurality of image material information and a plurality of text material information; Among them, the user tag information to be recommended, the image material information and the text material information correspond one to one.

3. The method according to claim 1, characterized in that The generating the target landing page according to the multiple material information and the parameter information of the target landing page includes: Acquire, from a server for storing at least one landing page template, a landing page sample template having the highest matching degree with the parameter information of the target landing page, wherein the landing page template is a template for generating a landing page, and the landing page sample template is a template in the at least one landing page template; Fill the various material information into corresponding positions in the landing page sample template to generate the target landing page.

4. The method according to claim 3, characterized in that Each of the multiple material information carries identification information, and filling the multiple material information into corresponding positions in the landing page sample template to generate the target landing page includes: Fill the multiple material information into corresponding positions in the landing page sample template in a preset order to generate the target landing page; Wherein, when the identification information is digital information, the preset order is the order of the identification information from small to large, or the preset order is the order of the identification information from large to small.

5. The method according to claim 4, characterized in that After filling the multiple material information into corresponding positions in the landing page sample template to generate the target landing page, the method further includes: Receive a target input from a target user, where the target input is used to adjust at least part of the material information on the target landing page; At least part of the material information on the target landing page is adjusted according to the target input to generate a revised target landing page.

6. The method according to any one of claims 1 to 5, characterized in that The preset model is an artificial intelligence model.

7. A landing page generation device, characterized in that: include: A first receiving module is used to receive a target input, wherein the target input is used to input demand information, and the demand information is used to generate a target landing page, wherein the demand information includes user set information and parameter information of the target landing page, wherein the user set information includes user information of users to be recommended to the target landing page, and the parameter information is used to represent display parameter information of the target landing page; An acquisition module, configured to acquire a plurality of user tag information to be recommended according to the user set information, wherein the users corresponding to the user tag information to be recommended are the users to be recommended in the user set information; A first generating module is used to input the plurality of user tag information to be recommended into a preset model, and obtain a plurality of material information corresponding to each user tag information to be recommended, wherein the preset model is a pre-trained model for generating material information; The second generating module is used to generate the target landing page according to the multiple material information and the parameter information of the target landing page.

8. An electronic device comprising: A memory, a processor, and a program stored in the memory and executable on the processor; characterized in that the processor is used to read the program in the memory to implement the steps in the landing page generation method as described in any one of claims 1 to 6.

9. A readable storage medium for storing a program, characterized in that: When the program is executed by a processor, the steps in the landing page generation method as described in any one of claims 1 to 6 are implemented.

10. A computer program product, characterized in that It includes computer instructions, which, when executed by a processor, implement the steps in the landing page generation method as described in any one of claims 1 to 6.

Images