Methods, devices, computer equipment, and storage media for ad placement recommendations

Abstract

This application belongs to the field of artificial intelligence technology and relates to a method for recommending ad placements. The method includes obtaining a preset number of content tag information for target content; matching the preset number of content tag information with at least one pre-stored product tag to obtain recommended display tags; when the number of recommended display tags exceeds a first preset value, obtaining the user's access records; if there are records related to recommended display tags in the access records, then determining the recommended display tags related to the access records as target display tags; if there are no records related to recommended display tags in the access records, then creating a user profile and determining the target display tag from the recommended display tags based on the profile results; and displaying the advertising information corresponding to the target display tag. This application also provides an ad placement recommendation device, computer equipment, and storage medium. This application can improve user satisfaction, increase the transaction volume of products corresponding to advertisements, and enhance user engagement with the platform.

Description

Technical Field

[0001] This application relates to the field of artificial intelligence technology, and in particular to methods, apparatus, computer equipment, and storage media for ad placement recommendation. Background Technology

[0002] Advertising is a way to increase company revenue. However, most advertisements on the market are not context-based and directly interrupt users' browsing behavior on mobile devices.

[0003] Traditional advertising recommendations require users to sift through massive amounts of information to find suitable product content. At the same time, marketing without context can easily cause user dissatisfaction, leading to user churn on the platform and often resulting in unsatisfactory advertising performance. Summary of the Invention

[0004] The purpose of this application is to provide a method, apparatus, computer device, and storage medium for recommending ad placements, in order to solve the problems of weak targeting and unsatisfactory results in existing advertising.

[0005] To address the aforementioned technical problems, this application provides a method for recommending ad slots, employing the following technical solution: Obtain a preset number of content tag information for the target content; The preset number of content tag information is matched with at least one pre-stored product tag to obtain recommended display tags; When the number of recommended display tags exceeds a first preset value, the user's access records are retrieved; If there is a record in the access record that is related to the recommended display tag, then the recommended display tag related to the access record is determined as the target display tag; If there is no record related to the recommended display tag in the access records, then a user profile is created, and the target display tag is determined in the recommended display tags based on the profile results; The advertising information corresponding to the target display label is displayed.

[0006] Furthermore, the acquisition of a preset number of content tag information for the target content includes: Obtain the target content; The target content is refined to obtain the preset number of content tag information.

[0007] Furthermore, after obtaining a preset number of content tag information, the method further includes: The preset number of content tag information are sorted in descending order of weight; The step of matching the preset number of content tag information with at least one pre-stored product tag to obtain recommended display tags includes: The preset number of content tag information is mapped to at least one pre-stored product tag in order of weight. When the mapping correlation between the preset number of content tag information and the pre-stored at least one product tag reaches a preset threshold, the pre-stored at least one product tag is determined as a recommended display tag.

[0008] Furthermore, after displaying the advertising information corresponding to the target display tag, the method further includes: Extract the corresponding display tags from a preset number of articles preceding the target content; Calculate the similarity between the previous display label and the target display label; Based on the similarity, the advertising information corresponding to the previously displayed label is displayed according to preset rules.

[0009] Furthermore, after displaying the advertising information corresponding to the target display tag, the method further includes: Within a preset time period, monitor whether the user clicks on the advertisement information; If no click is made, the similarity between the user and other users is calculated. The ad information will be displayed based on the user with the highest similarity to the user.

[0010] Furthermore, at least one product label includes at least a vehicle service function; After monitoring whether the user clicks on the advertisement information within a preset time period, the method further includes: If the user clicks on the advertisement information of the car service function, it is determined whether the advertisement information is associated with discount information; If the advertisement information contains promotional information, then all promotional information that matches the advertisement information is retrieved; Based on the usage requirements of the aforementioned promotional information, the most favorable target promotional information or a combination of target promotional information among all the promotional information will be pushed to you.

[0011] Furthermore, after obtaining the recommended display label, the method further includes: When the number of recommended display tags is less than or equal to a first preset value, the recommended display tags are determined as the target display tags.

[0012] To address the aforementioned technical problems, this application also provides an ad placement recommendation device, which employs the following technical solution: The acquisition module is used to acquire a preset number of content tag information for the target content; The matching module is used to match the preset number of content tag information with at least one pre-stored product tag to obtain recommended display tags; The acquisition module is also used to acquire the user's access records when the number of recommended display tags is greater than a first preset value; The determination module is used to determine the recommended display tag as the target display tag when there is a record related to the recommended display tag in the access record; and to create a user profile and determine the target display tag from the recommended display tags based on the profile results when there is no record related to the recommended display tag in the access record. The display module is used to display the advertising information corresponding to the target display label.

[0013] Furthermore, the acquisition module includes a first acquisition unit and a refining unit; The first acquisition unit is used to acquire the target content; The extraction unit is used to extract the target content and obtain the preset number of content tag information.

[0014] Furthermore, the device also includes a sorting module; The sorting module is used to sort the preset number of content tag information in descending order of weight. The matching module includes a mapping unit and a determination unit; The mapping unit is used to map the preset number of content tag information to at least one pre-stored product tag according to weight order; The determining unit is configured to determine the at least one pre-stored product tag as a recommended display tag when the mapping correlation between the preset number of content tag information and the at least one pre-stored product tag reaches a preset threshold.

[0015] Furthermore, the acquisition module is also used to acquire the preceding display tags corresponding to a preset number of content extracts prior to the target content; The device further includes a calculation module for calculating the similarity between the front display label and the target display label; The display module is also used to display the advertising information corresponding to the previous display tag according to the similarity and a preset rule.

[0016] Furthermore, the device also includes a monitoring module for monitoring whether the user clicks on the advertisement information within a preset time period; The calculation module is also used to calculate the similarity between the user and other users when no click is made; The display module is also used to display information based on the user with the highest similarity to the user.

[0017] Furthermore, at least one product label includes at least a vehicle service function; The device further includes a judgment module, which is used to determine whether the advertising information is associated with discount information when the monitoring module detects that the user clicks on the advertising information of the car service function; The acquisition module is also used to acquire all the discount information that is consistent with the advertising information when the advertising information is bound to discount information; The device also includes a push module, which is used to push the most favorable target offer or a combination of target offers among all the offer information according to the usage requirements of the offer information.

[0018] Furthermore, the determining module is also used to determine the recommended display label as the target display label when the number of recommended display labels is less than or equal to a first preset value.

[0019] To address the aforementioned technical problems, this application also provides a computer device that employs the following technical solution: A computer device is provided, comprising: one or more processors; and a memory for storing one or more programs, such that the one or more processors implement the ad placement recommendation method described in any one of the preceding embodiments.

[0020] To address the aforementioned technical problems, this application also provides a computer-readable storage medium, employing the technical solution described below: A computer-readable storage medium storing a computer program that, when executed by a processor, implements the ad placement recommendation method described in any one of the preceding claims.

[0021] Compared with the prior art, the embodiments of this application have the following main advantages: This application extracts tags from target content by acquiring a preset number of content tag information; it matches the preset number of content tag information with at least one pre-stored product tag to obtain recommended display tags, thus mapping the tags of the target content to preset product tags; when the number of recommended display tags is greater than a first preset value, it acquires the user's access records; if there are records related to the recommended display tags in the access records, the recommended display tags related to the access records are determined as target display tags; if there are no records related to the recommended display tags in the access records, the user is profiled, and the target display tags are determined from the recommended display tags based on the profile results, thus determining the target display tags from the recommended display tags based on the user's personalized characteristics; finally, the advertising information corresponding to the target display tags is displayed, thus displaying advertising information corresponding to the target display tags for different users. The accurate matching of target content tags and product tags in this application can improve user satisfaction and prevent negative emotions towards advertisements, thereby increasing the sales volume of the products corresponding to the advertisements and enhancing user engagement with the platform's functions. Attached Figure Description

[0022] To more clearly illustrate the solutions in this application, the accompanying drawings used in the description of the embodiments of this application will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 This is an exemplary system architecture diagram to which this application can be applied; Figure 2 A flowchart of an embodiment of the ad placement recommendation method according to this application; Figure 3 This is a schematic diagram of one embodiment of the device for recommending ad placements according to this application; Figure 4 This is a schematic diagram of the structure of one embodiment of the computer device according to this application. Detailed Implementation

[0024] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein in the specification of the application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application; the terms "comprising" and "having," and any variations thereof, in the specification, claims, and foregoing drawings of this application, are intended to cover non-exclusive inclusion. The terms "first," "second," etc., in the specification, claims, or foregoing drawings of this application are used to distinguish different objects, not to describe a particular order.

[0025] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0026] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.

[0027] like Figure 1 As shown, system architecture 100 may include terminal devices 101, 102, and 103, a network 104, and a server 105. Network 104 serves as the medium for providing communication links between terminal devices 101, 102, and 103 and server 105. Network 104 may include various connection types, such as wired or wireless communication links, or fiber optic cables, etc.

[0028] Users can use terminal devices 101, 102, and 103 to interact with server 105 via network 104 to receive or send messages, etc. Various communication client applications can be installed on terminal devices 101, 102, and 103, such as web browser applications, shopping applications, search applications, instant messaging tools, email clients, social media platform software, etc.

[0029] Terminal devices 101, 102, and 103 can be various electronic devices with displays and support web browsing, including but not limited to smartphones, tablets, e-book readers, MP3 players (Moving Picture Experts Group Audio Layer III), MP4 players (Moving Picture Experts Group Audio Layer IV), laptops, and desktop computers, etc.

[0030] Server 105 can be a server that provides various services, such as a backend server that supports the pages displayed on terminal devices 101, 102, and 103.

[0031] It should be noted that the ad placement recommendation method provided in this application embodiment is generally executed by a server / terminal device, and correspondingly, the ad placement recommendation device is generally set in the server / terminal device.

[0032] It should be understood that Figure 1 The number of terminal devices, networks, and servers shown is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, and servers can be included.

[0033] Continue to refer to Figure 2 A flowchart illustrating an embodiment of the ad placement recommendation method according to this application is shown. The ad placement recommendation method, applied to an advertising display platform, includes the following steps: Step 201: Obtain a preset number of content tag information for the target content.

[0034] Specifically, after the content is purchased and stored, the AI ​​tagging interface function can be called in real time in this embodiment to extract the most relevant tags for the article. Generally, 5-10 tags can be selected.

[0035] Step 202: Match the preset number of content tag information with at least one pre-stored product tag to obtain recommended display tags.

[0036] In this embodiment, the content provider pre-stores a tag mapping library, which includes tags for non-auto insurance products and auto service functions recommended in advertisements. Based on the extracted content tag information and product tag mapping, suitable non-auto insurance products and auto service functions are matched for recommendation based on relevance.

[0037] Step 203: When the number of recommended display tags is greater than a first preset value, obtain the user's access records.

[0038] Specifically, when multiple products return matching results, the system will obtain the user's recent browsing and clicking behavior, and then determine the corresponding target display tag from multiple recommendation display tags based on the user's recent browsing and clicking behavior.

[0039] Step 204: If there is a record related to the recommended display tag in the access record, then the recommended display tag related to the access record is determined as the target display tag; If there is no record related to the recommended display tag in the access records, then a user profile is created, and the target display tag is determined in the recommended display tags based on the profile results.

[0040] In practical use, for example, if three products are returned, and the user has previously viewed or clicked on one of them, then products matching their behavior will be recommended. If the user has no recent browsing or clicking behavior, recommendations will be made based on user profiles, such as the user's age, family members, and mode of transportation, to determine the corresponding target display tags from multiple recommendation display tags.

[0041] Step 205: Display the advertising information corresponding to the target display label.

[0042] Specifically, the content of the advertisements displayed on the front end is determined based on the target display tags.

[0043] This application extracts tags from target content by acquiring a preset number of content tag information; it matches the preset number of content tag information with at least one pre-stored product tag to obtain recommended display tags, thus mapping the tags of the target content to preset product tags; when the number of recommended display tags is greater than a first preset value, it acquires the user's access records; if there are records related to the recommended display tags in the access records, the recommended display tags related to the access records are determined as target display tags; if there are no records related to the recommended display tags in the access records, the user is profiled, and the target display tags are determined from the recommended display tags based on the profile results, thus determining the target display tags from the recommended display tags based on the user's personalized characteristics; finally, the advertising information corresponding to the target display tags is displayed, thus displaying advertising information corresponding to the target display tags for different users. The accurate matching of target content tags and product tags in this application can improve user satisfaction and prevent negative emotions towards advertisements, thereby increasing the sales volume of the products corresponding to the advertisements and enhancing user engagement with the platform's functions.

[0044] In some optional implementations of this embodiment, in step 201, a preset number of content tag information of the target content are obtained. The electronic device may also perform the following steps: Obtain the target content; The target content is refined to obtain the preset number of content tag information.

[0045] Specifically, after the content is purchased and stored, the AI ​​tagging interface function can be called in real time in this embodiment to extract multiple tags with high relevance to the article, generally 5-10 tags can be selected.

[0046] In some optional implementations of this embodiment, after obtaining a preset number of content tag information in the above steps, the electronic device may further perform the following steps: The preset number of content tag information are sorted in descending order of weight.

[0047] In some optional implementations of this embodiment, step 202 above matches the preset number of content tag information with at least one pre-stored product tag to obtain recommended display tags. The electronic device may also perform the following steps: The preset number of content tag information is mapped to at least one pre-stored product tag in order of weight. When the mapping correlation between the preset number of content tag information and the pre-stored at least one product tag reaches a preset threshold, the pre-stored at least one product tag is determined to be a recommended display tag; Specifically, in this embodiment, multiple content tag information is sorted from high to low according to weight and returned to the content provider. Based on the weight of the content tag information and the matching degree of the mapping, recommended display tags can be obtained through word splitting.

[0048] In some alternative implementations, after displaying the advertising information corresponding to the target display label in step 205 above, the electronic device may perform the following steps: Extract the corresponding display tags from a preset number of articles preceding the target content; Calculate the similarity between the previous display label and the target display label; Based on the similarity, the advertising information corresponding to the previously displayed label is displayed according to preset rules.

[0049] In this embodiment, advertisements corresponding to previously viewed content can be partially displayed. Specifically, the target display tags extracted from the previous or a preset number of viewed content can be compared with the target display tags extracted from the currently viewed content. The similarity between the two sets of tags is calculated. The more different the two sets of tags are, the more advertisements corresponding to the current set of target display tags will be displayed. In other words, during display, a portion of the current target display tag information and a portion of the previous target display tag information are displayed, but the displayed content is content that has not been displayed before.

[0050] In some alternative implementations, after displaying the advertising information corresponding to the target display label in step 205 above, the electronic device may perform the following steps: Within a preset time period, monitor whether the user clicks on the advertisement information; If no click is made, the similarity between the user and other users is calculated. The ad information will be displayed based on the user with the highest similarity to the user.

[0051] Specifically, when displaying ads, the platform prioritizes recommending ads currently being promoted, followed by ads that the user has frequently used recently. If a user hasn't used the platform recently, the platform analyzes the click behavior of similar customer profiles to recommend ads. For example, if user A is 30 years old, drives a gasoline car, and has never used the platform's car-related services, but another user B is also 30 years old, drives a gasoline car, and has the same car brand, and user B most frequently uses certain car-related services on the platform, then the platform will recommend the car-related services used by user B to user A.

[0052] In some optional implementations, the at least one product label includes at least a car service function; after monitoring whether the user clicks on the advertisement information within a preset time period, the electronic device can perform the following steps: If the user clicks on the advertisement information of the car service function, it is determined whether the advertisement information is associated with discount information; If the advertisement information contains promotional information, then all promotional information that matches the advertisement information is retrieved; Based on the usage requirements of the aforementioned promotional information, the most favorable target promotional information or a combination of target promotional information among all the promotional information will be pushed to you.

[0053] In actual use, the platform pre-stores all tags for the car service functions within the platform. For car service functions, coupons are retrieved by calling the coupon center's API to determine if a coupon exists for that service. If a coupon is available and valid, it is displayed; otherwise, the coupon entry is not shown. Generally, product coupons can include merchant-configured coupons, platform-provided coupons, and institution-provided coupons. Based on recommended products, coupons matching the product are extracted. After deduplication, and considering the usage feedback and target audience of the coupons, the most favorable coupons are extracted and displayed as pop-ups on the front end to guide users to claim and use them. The coupon also needs to determine if the user has already claimed it. If not, "Claim Now" is displayed; if already claimed, "Use Now" is displayed.

[0054] In some optional implementations, after step 202 matches the preset number of content tag information with at least one pre-stored product tag to obtain the recommended display tag, the electronic device can perform the following steps: When the number of recommended display tags is less than or equal to a first preset value, the recommended display tags are determined as the target display tags.

[0055] It should be emphasized that, to further ensure the privacy and security of the aforementioned advertising information, the advertising information can also be stored in a blockchain node.

[0056] The blockchain referred to in this application is a novel application model of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanisms, and encryption algorithms. Essentially, a blockchain is a decentralized database, a chain of data blocks linked together using cryptographic methods. Each data block contains information about a batch of network transactions, used to verify the validity of the information (anti-counterfeiting) and generate the next block. A blockchain can include an underlying blockchain platform, a platform product service layer, and an application service layer.

[0057] The embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence (AI) refers to the theories, methods, technologies, and application systems that use digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results. Foundational technologies for artificial intelligence generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing, operating / interactive systems, and mechatronics. AI software technologies mainly encompass computer vision, robotics, biometrics, speech processing, natural language processing, and machine learning / deep learning.

[0058] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by instructing related hardware through computer-readable instructions. These computer-readable instructions can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above methods. The aforementioned storage medium can be a non-volatile storage medium such as a magnetic disk, optical disk, or read-only memory (ROM), or random access memory (RAM).

[0059] It should be understood that although the steps in the flowcharts of the accompanying figures are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the accompanying figures may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0060] Further reference Figure 3 As a response to the above Figure 2 To implement the method shown, this application provides an embodiment of an ad placement recommendation device, which is similar to... Figure 2 Corresponding to the method embodiments shown, this device can be specifically applied to various electronic devices.

[0061] like Figure 3 As shown, the ad placement recommendation device 300 described in this embodiment includes: an acquisition module 301, a matching module 302, a determination module 303, and a display module 304. Wherein: The acquisition module 301 is used to acquire a preset number of content tag information for the target content; The matching module 302 is used to match the preset number of content tag information with at least one pre-stored product tag to obtain recommended display tags; The acquisition module 301 is also used to acquire the user's access records when the number of recommended display tags is greater than a first preset value; The determining module 303 is used to determine the recommended display tag related to the access record as the target display tag when there is a record related to the recommended display tag in the access record; and to create a profile of the user and determine the target display tag in the recommended display tag according to the profile results when there is no record related to the recommended display tag in the access record. Display module 304 is used to display the advertising information corresponding to the target display label.

[0062] In this embodiment, the acquisition module 301 acquires a preset number of content tag information of the target content to extract tags from the target content; the matching module 302 matches the preset number of content tag information with at least one pre-stored product tag to obtain recommended display tags, thereby mapping the tags of the target content to the preset product tags; the acquisition module 301 also acquires the user's access records when the number of recommended display tags is greater than a first preset value; the determination module 303 determines the recommended display tag related to the access record as the target display tag when there is a record related to the recommended display tag in the access record; if the determination module 303 does not have a record related to the recommended display tag in the access record, it profiles the user and determines the target display tag from the recommended display tags based on the profile results, thereby determining the target display tag from the recommended display tags based on the user's personalized characteristics; the display module 304 displays the advertising information corresponding to the target display tag, thereby displaying the advertising information corresponding to the target display tag for different users. This application's precise matching of target content tags and product tags can improve user satisfaction and prevent negative emotions towards advertisements, thereby increasing sales of the corresponding products and enhancing user engagement with the platform's functions.

[0063] In some optional implementations of this embodiment, the above-mentioned acquisition module 301 further includes: a first acquisition unit 3011 and a refining unit 3012; The first acquisition unit 3011 is used to acquire the target content; The extraction unit 3012 is used to extract the target content and obtain the preset number of content tag information.

[0064] Specifically, after the content is purchased and stored, the AI ​​tagging interface function can be called in real time in this embodiment to extract multiple tags with high relevance to the article, generally 5-10 tags can be selected.

[0065] In some optional implementations of this embodiment, the above-mentioned device 300 further includes a sorting module 305; The sorting module 305 is used to sort the preset number of content tag information according to the order of weight. The matching module 302 includes a mapping unit 3021 and a determination unit 3022; The mapping unit 3021 is used to map the preset number of content tag information to at least one pre-stored product tag according to a weight order; The determining unit 3022 is used to determine the at least one pre-stored product tag as a recommended display tag when the mapping correlation between the preset number of content tag information and the at least one pre-stored product tag reaches a preset threshold.

[0066] Specifically, in this embodiment, multiple content tag information is sorted from high to low according to weight and returned to the content provider. Based on the weight of the content tag information and the matching degree of the mapping, recommended display tags can be obtained through word splitting.

[0067] In some optional implementations of this embodiment, the acquisition module 301 is further configured to acquire the front display tags corresponding to a preset number of content extracts preceding the target content; The device 300 further includes a calculation module 306 for calculating the similarity between the front display label and the target display label; The display module 304 is also used to display the advertising information corresponding to the previous display tag according to the similarity and a preset rule.

[0068] In this embodiment, advertisements corresponding to previously viewed content can be partially displayed. Specifically, the target display tags extracted from the previous or a preset number of viewed content can be compared with the target display tags extracted from the currently viewed content. The similarity between the two sets of tags is calculated. The more different the two sets of tags are, the more advertisements corresponding to the current set of target display tags will be displayed. In other words, during display, a portion of the current target display tag information and a portion of the previous target display tag information are displayed, but the displayed content is content that has not been displayed before.

[0069] In some optional implementations of this embodiment, the device 300 further includes a monitoring module 307, used to monitor whether the user clicks on the advertising information within a preset time period; The calculation module 306 is also used to calculate the similarity between the user and other users when no click is made; The display module 304 is also used to display information based on the clicks of the advertising information by the user with the highest similarity to the user.

[0070] Specifically, when displaying ads, the platform prioritizes recommending ads currently being promoted, followed by ads that the user has frequently used recently. If a user hasn't used the platform recently, the platform analyzes the click behavior of similar customer profiles to recommend ads. For example, if user A is 30 years old, drives a gasoline car, and has never used the platform's car-related services, but another user B is also 30 years old, drives a gasoline car, and has the same car brand, and user B most frequently uses certain car-related services on the platform, then the platform will recommend the car-related services used by user B to user A.

[0071] In some optional implementations of this embodiment, the at least one product label includes at least a vehicle service function; The device 300 further includes a judgment module 308, which is used to determine whether the advertising information is bound to discount information when the monitoring module detects that the user clicks on the advertising information of the car service function; The acquisition module 301 is also used to acquire all the discount information that is consistent with the advertising information when the advertising information is bound to discount information; The device 300 further includes a push module 309, which is used to push the most favorable target offer or a combination of target offers among all the offer information according to the usage requirements of the offer information.

[0072] In actual use, the platform pre-stores all tags for the car service functions within the platform. For car service functions, coupons are retrieved by calling the coupon center's API to determine if a coupon exists for that service. If a coupon is available and valid, it is displayed; otherwise, the coupon entry is not shown. Generally, product coupons can include merchant-configured coupons, platform-provided coupons, and institution-provided coupons. Based on recommended products, coupons matching the product are extracted. After deduplication, and considering the usage feedback and target audience of the coupons, the most favorable coupons are extracted and displayed as pop-ups on the front end to guide users to claim and use them. The coupon also needs to determine if the user has already claimed it. If not, "Claim Now" is displayed; if already claimed, "Use Now" is displayed.

[0073] In some optional implementations of this embodiment, the determining module 303 is further configured to determine the recommended display label as the target display label when the number of recommended display labels is less than or equal to a first preset value.

[0074] It should be emphasized that, to further ensure the privacy and security of the aforementioned advertising information, the advertising information can also be stored on a blockchain node.

[0075] To address the aforementioned technical problems, embodiments of this application also provide a computer device. Please refer to [link / reference] for details. Figure 4 , Figure 4 This is a basic structural block diagram of the computer device in this embodiment.

[0076] The computer device 4 includes a memory 41, a processor 42, and a network interface 43 that are interconnected via a system bus. It should be noted that only the computer device 4 with components 41-43 is shown in the figure; however, it should be understood that it is not required to implement all the shown components, and more or fewer components can be implemented alternatively. Those skilled in the art will understand that the computer device described here is a device capable of automatically performing numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes, but is not limited to, microprocessors, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc.

[0077] The computer device can be a desktop computer, laptop, handheld computer, or cloud server, etc. The computer device can interact with the user via a keyboard, mouse, remote control, touchpad, or voice control.

[0078] The memory 41 includes at least one type of readable storage medium, including flash memory, hard disk, multimedia card, card-type memory (e.g., SD or DX memory), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 41 may be an internal storage unit of the computer device 4, such as the hard disk or memory of the computer device 4. In other embodiments, the memory 41 may also be an external storage device of the computer device 4, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the computer device 6. Of course, the memory 41 may include both the internal storage unit and its external storage device of the computer device 4. In this embodiment, the memory 41 is typically used to store the operating system and various application software installed on the computer device 4, such as computer-readable instructions for methods of recommending advertising slots. In addition, the memory 41 can also be used to temporarily store various types of data that have been output or will be output.

[0079] In some embodiments, the processor 42 may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chip. The processor 42 is typically used to control the overall operation of the computer device 4. In this embodiment, the processor 42 is used to execute computer-readable instructions stored in the memory 41 or to process data, such as computer-readable instructions for executing the method of recommending the ad placement.

[0080] The network interface 43 may include a wireless network interface or a wired network interface, which is typically used to establish communication connections between the computer device 4 and other electronic devices.

[0081] This application's computer device extracts tags from target content by acquiring a preset number of content tag information. It then matches these content tag information with at least one pre-stored product tag to obtain recommended display tags, thus mapping the target content tags to preset product tags. When the number of recommended display tags exceeds a first preset value, it acquires the user's access records. If the access records contain records related to the recommended display tags, these tags are identified as target display tags. If the access records do not contain records related to the recommended display tags, the user is profiled, and the target display tag is determined from the recommended display tags based on the profile results, thus determining the target display tag from the recommended display tags according to the user's personalized characteristics. Finally, the advertising information corresponding to the target display tags is displayed, enabling the display of advertising information corresponding to the target display tags for different users. This application's precise matching of target content tags and product tags can improve user satisfaction, prevent negative emotions towards advertisements, increase sales of the advertised products, and enhance user engagement with the platform's functions.

[0082] This application also provides another embodiment, namely, providing a computer-readable storage medium storing computer-readable instructions that can be executed by at least one processor to cause the at least one processor to perform the steps of the ad placement recommendation method as described above.

[0083] This application's computer-readable storage medium extracts tags from target content by acquiring a preset number of content tag information. It then matches the preset number of content tag information with at least one pre-stored product tag to obtain recommended display tags, thus mapping the tags of the target content to preset product tags. When the number of recommended display tags exceeds a first preset value, it acquires the user's access records. If the access records contain records related to the recommended display tags, the recommended display tags related to the access records are determined as target display tags. If the access records do not contain records related to the recommended display tags, a user profile is created, and the target display tags are determined from the recommended display tags based on the profile results, thus determining the target display tags from the recommended display tags according to the user's personalized characteristics. Finally, it displays the advertising information corresponding to the target display tags, thus displaying advertising information corresponding to the target display tags for different users. This application's accurate matching of target content tags and product tags can improve user satisfaction, prevent negative emotions towards advertisements, increase sales of the advertised products, and enhance user engagement with the platform's functions.

[0084] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0085] Obviously, the embodiments described above are only some embodiments of this application, not all embodiments. The accompanying drawings show preferred embodiments of this application, but do not limit the patent scope of this application. This application can be implemented in many different forms; rather, the purpose of providing these embodiments is to provide a more thorough and comprehensive understanding of the disclosure of this application. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing specific embodiments, or make equivalent substitutions for some of the technical features. Any equivalent structures made using the content of this application's specification and drawings, directly or indirectly applied to other related technical fields, are similarly within the scope of patent protection of this application.

Claims

1. A method for recommending ad placements, characterized in that, Includes the following steps: Obtain a preset number of content tag information for the target content; The preset number of content tag information is matched with at least one pre-stored product tag to obtain recommended display tags; When the number of recommended display tags exceeds a first preset value, the user's access records are retrieved; If there is a record in the access record that is related to the recommended display tag, then the recommended display tag related to the access record is determined as the target display tag; If there is no record related to the recommended display tag in the access records, then a user profile is created, and the target display tag is determined in the recommended display tags based on the profile results; Display the advertising information corresponding to the target display tag; The method further includes, after displaying the advertising information corresponding to the target display label: Extract the corresponding display tags from a preset number of articles preceding the target content; Calculate the similarity between the previous display label and the target display label; Based on the similarity, the advertising information corresponding to the previously displayed tag is displayed according to preset rules; The process involves comparing the target display tags extracted from the previous or a preset number of browsing articles with the target display tags extracted from the current browsing content. The similarity between the two sets of tags is calculated. The more different the two sets of tags are, the more ads will be displayed corresponding to the target display tags of the current set. When displaying the ads, a portion of the current target display tag information and a portion of the previous target display tag information are displayed, but the content that has not been displayed before is shown.

2. The method for recommending ad slots according to claim 1, characterized in that, The preset number of content tag information for obtaining the target content includes: Obtain the target content; The target content is refined to obtain the preset number of content tag information.

3. The method for recommending ad slots according to claim 2, characterized in that, After obtaining a preset number of content tag information for the target content, the method further includes: The preset number of content tag information are sorted in descending order of weight; The step of matching the preset number of content tag information with at least one pre-stored product tag to obtain recommended display tags includes: The preset number of content tag information is mapped to at least one pre-stored product tag in order of weight. When the mapping correlation between the preset number of content tag information and the pre-stored at least one product tag reaches a preset threshold, the pre-stored at least one product tag is determined as a recommended display tag.

4. The method for recommending ad slots according to claim 1, characterized in that, After displaying the advertising information corresponding to the target display tag, the method further includes: Within a preset time period, monitor whether the user clicks on the advertisement information; If no click is made, the similarity between the user and other users is calculated. The ad information will be displayed based on the user with the highest similarity to the user.

5. The method for recommending ad slots according to claim 4, characterized in that, The pre-stored at least one product tag includes at least a vehicle service function; After monitoring whether the user clicks on the advertisement information within a preset time period, the method further includes: If the user clicks on the advertisement information of the car service function, it is determined whether the advertisement information is associated with discount information; If the advertisement information contains promotional information, then all promotional information in the advertisement information is retrieved; Based on the usage requirements of the aforementioned promotional information, the most favorable target promotional information or a combination of target promotional information among all the promotional information will be pushed to you.

6. The method for recommending ad slots according to claim 1, characterized in that, After obtaining the recommended display tags, the method further includes: When the number of recommended display tags is less than or equal to a first preset value, the recommended display tags are determined as the target display tags.

7. A device for recommending ad slots, characterized in that, include: The acquisition module is used to acquire a preset number of content tag information for the target content; The matching module is used to match the preset number of content tag information with at least one pre-stored product tag to obtain recommended display tags; The acquisition module is also used to acquire the user's access records when the number of recommended display tags is greater than a first preset value; The determination module is used to determine the recommended display tag as the target display tag when there is a record related to the recommended display tag in the access record; and to create a user profile and determine the target display tag from the recommended display tags based on the profile results when there is no record related to the recommended display tag in the access record. The display module is used to display the advertising information corresponding to the target display label; The acquisition module is further configured to acquire the front display tags corresponding to a preset number of content extracts preceding the target content; The device further includes a calculation module for calculating the similarity between the front display label and the target display label; The display module is also used to display the advertising information corresponding to the previous display tag according to the similarity and a preset rule; The process involves comparing the target display tags extracted from the previous or a preset number of browsing articles with the target display tags extracted from the current browsing content. The similarity between the two sets of tags is calculated. The more different the two sets of tags are, the more ads will be displayed corresponding to the target display tags of the current set. When displaying the ads, a portion of the current target display tag information and a portion of the previous target display tag information are displayed, but the content that has not been displayed before is shown.

8. A computer device comprising a memory and a processor, the memory storing computer-readable instructions, wherein the processor, when executing the computer-readable instructions, implements the steps of the method for recommending ad placements as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-readable instructions that, when executed by a processor, implement the steps of the method for recommending ad slots as described in any one of claims 1 to 6.

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