Intelligent content label automatic generation method and device based on artificial intelligence

Abstract

The invention discloses an intelligent content label automatic generation method and device based on artificial intelligence. The method comprises the following steps: preprocessing an article title and content of a to-be-processed article to obtain preprocessed data; performing semantic association analysis on the preprocessed data to obtain a basic tag of the to-be-processed article; performing personalized optimization on the basic tag according to the user portrait of the to-be-processed article, and generating an automatic tag set of the to-be-processed article; and dynamic label weight distribution is carried out on the automatic labels, and sorting is carried out to obtain an intelligent label set.

Description

Technical Field

[0001] This invention relates to the field of tag generation technology, and more specifically, to a method and apparatus for automatically generating intelligent content tags based on artificial intelligence. Background Technology

[0002] The current content management system requires manual tagging for each article or activity when configuring content such as articles and activities. This presents the following technical issues: 1. Insufficient tag naming and explanation: Some tag names are vague or lack clear explanations, making it difficult for users and the system to understand the specific meaning of the tags, affecting the user experience. 2. Overly broad tag categories: Tag generation generates generalized groups, failing to support refined operational strategies. 3. Lack of tag lifecycle management: Unused tags that expire result in duplicate tags, leading to wasted resources. Summary of the Invention

[0003] To address the shortcomings of existing technologies, this invention provides a method and apparatus for automatically generating intelligent content tags based on artificial intelligence.

[0004] According to one aspect of the present invention, an artificial intelligence-based method for automatically generating intelligent content tags is provided, comprising:

[0005] The article title and content of the article to be processed are preprocessed to obtain preprocessed data;

[0006] Semantic association analysis is performed on the preprocessed data to obtain the basic tags of the articles to be processed;

[0007] Based on the user profile of the article to be processed, the basic tags are optimized in a personalized way to generate an automated tag set for the article to be processed;

[0008] The automated tags are dynamically weighted and sorted to obtain a smart tag set.

[0009] Optionally, semantic association analysis is performed on the preprocessed data to obtain the basic tags of the article to be processed, including:

[0010] Semantic analysis methods are used to capture semantic association information in preprocessed data to obtain basic tags for the articles to be processed.

[0011] Optionally, the basic tags are personalized and optimized based on the user profile of the article to be processed, generating an automated tag set for the article to be processed, including:

[0012] Analyze users' historical behavior, preferences, click records, and personalized characteristics to automatically generate personalized tags for users' interests;

[0013] The basic changes are optimized based on personalized tags to generate an automated tag set for the articles to be processed.

[0014] Optionally, dynamic label weight allocation is performed on automated labels, including:

[0015] The weights of each tag in the automated tag set are dynamically adjusted based on the correlation between the automated tags and the content in the preprocessed data, as well as user behavior information.

[0016] Optionally, it also includes: extracting cross-media tags by combining image, video, and audio data.

[0017] According to another aspect of the present invention, an artificial intelligence-based intelligent content tag automatic generation device is provided, comprising:

[0018] The preprocessing module is used to preprocess the article title and content of the article to be processed in order to obtain preprocessed data;

[0019] The analysis module is used to perform semantic association analysis on the preprocessed data to obtain the basic tags of the article to be processed;

[0020] The generation module is used to personalize and optimize basic tags based on the user profile of the article to be processed, and generate an automated tag set for the article to be processed.

[0021] The allocation module is used to dynamically allocate tag weights to automated tags and sort them to obtain a smart tag set.

[0022] According to another aspect of the present invention, a computer-readable storage medium is provided, the storage medium storing a computer program for performing the method of any of the above aspects of the present invention.

[0023] According to another aspect of the present invention, an electronic device is provided, comprising: a processor; a memory for storing executable instructions of the processor; the processor being configured to read the executable instructions from the memory and execute the instructions to implement the method described in any of the preceding aspects of the present invention.

[0024] Therefore, this invention preprocesses the article title and content of the article to be processed to obtain preprocessed data; performs semantic association analysis on the preprocessed data to obtain basic tags for the article to be processed; personalizes and optimizes the basic tags according to the user profile of the article to generate an automated tag set for the article to be processed; and dynamically assigns tag weights to the automated tags and sorts them to obtain an intelligent tag set. This greatly reduces operating costs. Attached Figure Description

[0025] Exemplary embodiments of the present invention can be more fully understood by referring to the following figures:

[0026] Figure 1This is a flowchart illustrating an exemplary embodiment of the present invention for an automatic generation method of intelligent content tags based on artificial intelligence.

[0027] Figure 2 This is a schematic diagram of the structure of an artificial intelligence-based intelligent content tag automatic generation device provided in an exemplary embodiment of the present invention;

[0028] Figure 3 This is the structure of an electronic device provided in an exemplary embodiment of the present invention. Detailed Implementation

[0029] Hereinafter, exemplary embodiments according to the present invention will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of the present invention, and not all embodiments of the present invention. It should be understood that the present invention is not limited to the exemplary embodiments described herein.

[0030] It should be noted that, unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps described in these embodiments do not limit the scope of the invention.

[0031] Those skilled in the art will understand that the terms "first," "second," etc., in the embodiments of the present invention are only used to distinguish different steps, devices, or modules, and do not represent any specific technical meaning, nor do they indicate a necessary logical order between them.

[0032] It should also be understood that in the embodiments of the present invention, "multiple" can refer to two or more, and "at least one" can refer to one, two or more.

[0033] It should also be understood that any component, data or structure mentioned in the embodiments of the present invention can generally be understood as one or more unless explicitly defined or given contrary instructions in the context.

[0034] Furthermore, the term "and / or" in this invention is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this invention generally indicates that the preceding and following related objects have an "or" relationship.

[0035] It should also be understood that the description of the various embodiments in this invention emphasizes the differences between the various embodiments, and the similarities or similarities can be referred to each other. For the sake of brevity, they will not be described in detail.

[0036] At the same time, it should be understood that, for ease of description, the dimensions of the various parts shown in the accompanying drawings are not drawn according to actual scale.

[0037] The following description of at least one exemplary embodiment is merely illustrative and is in no way intended to limit the invention or its application or use.

[0038] Techniques, methods, and equipment known to those skilled in the art may not be discussed in detail, but where appropriate, they should be considered part of the specification.

[0039] It should be noted that similar labels and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be discussed further in subsequent figures.

[0040] The embodiments of this invention can be applied to electronic devices such as terminal devices, computer systems, and servers, and can operate together with a wide range of other general-purpose or special-purpose computing system environments or configurations. Well-known examples of terminal devices, computing systems, environments, and / or configurations suitable for use with electronic devices such as terminal devices, computer systems, and servers include, but are not limited to: personal computer systems, server computer systems, thin clients, thick clients, handheld or laptop devices, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputer systems, mainframe computer systems, and distributed cloud computing environments including any of the above systems, etc.

[0041] Electronic devices such as terminal devices, computer systems, and servers can be described in the general context of computer system executable instructions (such as program modules) executed by a computer system. Typically, program modules can include routines, programs, object programs, components, logic, data structures, etc., which perform specific tasks or implement specific abstract data types. Computer systems / servers can be implemented in distributed cloud computing environments, where tasks are executed by remote processing devices linked through communication networks. In distributed cloud computing environments, program modules can reside on local or remote computing system storage media, including storage devices.

[0042] Exemplary methods

[0043] Figure 1 This is a flowchart illustrating an exemplary embodiment of an artificial intelligence-based intelligent content tag automatic generation method provided by the present invention. This embodiment can be applied to electronic devices, such as… Figure 1 As shown, the AI-based intelligent content tag automatic generation method 100 includes the following steps:

[0044] Step 101: Preprocess the article title and content of the article to be processed to obtain preprocessed data;

[0045] Step 102: Perform semantic association analysis on the preprocessed data to obtain the basic tags of the article to be processed;

[0046] Step 103: Based on the user profile of the article to be processed, perform personalized optimization of the basic tags to generate an automated tag set for the article to be processed;

[0047] Step 104: Perform dynamic label weight allocation on the automated labels and sort them to obtain a smart label set.

[0048] Specifically, in view of the technical problems existing in the prior art, the solution of the present invention is as follows:

[0049] 1. Article content preprocessing

[0050] Preprocess the article title and content to improve the accuracy of tag generation.

[0051] 2. Automatic tag generation

[0052] Use natural language processing models (such as OpenAI's GPT-3 / 4) to extract tags from articles, compare them with existing tag libraries, and generate new tags.

[0053] 3. Tag library update

[0054] The generated new tags are stored in the tag library of the backend system to ensure the tag library is updated and accurate.

[0055] Furthermore, optimizations are made to the automatic generation of intelligent content tags based on artificial intelligence:

[0056] 1. Personalized tag generation

[0057] Personalized recommendation systems are used to tailor recommendations to different user groups and contexts. By analyzing users' historical behavior, preferences, and click records, personalized tags that better match users' interests are automatically generated. These personalized tags not only improve the accuracy of content recommendations but also help increase user engagement.

[0058] 2. Semantic association analysis

[0059] Using semantic analysis techniques from deep learning, potential semantic connections in articles or activities can be automatically captured. For example, two seemingly unrelated topics (such as "travel" and "health") may have a deep connection in an article through the concept of "healthy lifestyle." This can generate more valuable and unique tags.

[0060] 3. Dynamic Tag Weight

[0061] A weighted system is introduced for tags in each article. Tags are not just static keywords; their weights can be dynamically adjusted based on their relevance to the content, user behavior, and other factors. This allows the system to prioritize tags, making more important tags more prominent.

[0062] 4. Multimodal data processing

[0063] Extending tag generation to multimodal data processing allows for cross-media tag extraction by combining other content formats such as images, videos, and audio. For example, tags can be generated from audio content and visual elements in videos, rather than being limited to text content.

[0064] 5. Real-time label prediction and feedback loop

[0065] The system analyzes and generates tags in real time, allowing users to provide real-time feedback. This feedback loop helps adjust and optimize the generation algorithm, improving the accuracy of future tag generation. Based on reinforcement learning algorithms (such as Deep Q-Learning), the feedback mechanism continuously optimizes the tag generation model.

[0066] Therefore, this invention preprocesses the article title and content of the article to be processed to obtain preprocessed data; performs semantic association analysis on the preprocessed data to obtain basic tags for the article to be processed; personalizes and optimizes the basic tags according to the user profile of the article to generate an automated tag set for the article to be processed; and dynamically assigns tag weights to the automated tags and sorts them to obtain an intelligent tag set. This greatly reduces operating costs.

[0067] Exemplary device

[0068] Figure 2 This is a schematic diagram of the structure of an artificial intelligence-based intelligent content tag automatic generation device provided in an exemplary embodiment of the present invention. For example... Figure 2 As shown, the device 200 includes:

[0069] The preprocessing module 210 is used to preprocess the article title and content of the article to be processed in order to obtain preprocessed data;

[0070] Analysis module 220 is used to perform semantic association analysis on preprocessed data to obtain the basic tags of the article to be processed;

[0071] The generation module 230 is used to personalize and optimize the basic tags based on the user profile of the article to be processed, and generate an automated tag set for the article to be processed.

[0072] The allocation module 240 is used to dynamically allocate tag weights to automated tags and sort them to obtain a smart tag set.

[0073] Optionally, the analysis module 220 includes:

[0074] The capture submodule is used to capture semantic association information in preprocessed data using semantic analysis methods to obtain the basic tags of the article to be processed.

[0075] Optionally, the generation module 230 includes:

[0076] The analysis submodule is used to analyze users' historical behavior, preferences, click records and personalized characteristics, and automatically generate personalized tags for users' interests.

[0077] The generation submodule is used to optimize the basic changes based on personalized tags and generate an automated tag set for the article to be processed.

[0078] Optionally, the allocation module 240 performs dynamic tag weight allocation for automated tags, including:

[0079] The adjustment submodule is used to dynamically adjust the weight of each tag in the automated tag set based on the correlation between the automated tags and the content in the preprocessed data, as well as user behavior information.

[0080] Optionally, the device 200 also includes an extraction module for extracting cross-media tags by combining image, video, and audio data.

[0081] Exemplary electronic devices

[0082] Figure 3 This is the structure of an electronic device provided in an exemplary embodiment of the present invention. For example... Figure 3 As shown, the electronic device 30 includes one or more processors 31 and memory 32.

[0083] The processor 31 may be a central processing unit (CPU) or other form of processing unit with data processing and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions.

[0084] The memory 32 may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 31 may execute the program instructions to implement the methods of the software programs of the various embodiments of the present invention described above, and / or other desired functions. In one example, the electronic device may also include an input device 33 and an output device 34, these components being interconnected via a bus system and / or other forms of connection mechanisms (not shown).

[0085] In addition, the input device 33 may also include, for example, a keyboard, a mouse, etc.

[0086] The output device 34 can output various information to the outside. The output device 34 may include, for example, a display, a speaker, a printer, and a communication network and its connected remote output devices, etc.

[0087] Of course, for the sake of simplicity, Figure 3 Only some of the components of the electronic device relevant to the present invention are shown, omitting components such as buses, input / output interfaces, etc. In addition, the electronic device may include any other suitable components depending on the specific application.

[0088] Exemplary computer program products and computer-readable storage media

[0089] In addition to the methods and apparatus described above, embodiments of the present invention may also be computer program products, which include computer program instructions that, when executed by a processor, cause the processor to perform the steps in the methods according to various embodiments of the present invention described in the "Exemplary Methods" section above.

[0090] The computer program product can be written in any combination of one or more programming languages ​​to perform the operations of the embodiments of the present invention. The programming languages ​​include object-oriented programming languages ​​such as Java and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0091] Furthermore, embodiments of the present invention may also be computer-readable storage media storing computer program instructions thereon, which, when executed by a processor, cause the processor to perform the steps of the methods for information mining of historical change records according to various embodiments of the present invention as described in the "Exemplary Methods" section above.

[0092] The computer-readable storage medium may be any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or any combination thereof. More specific examples (a non-exhaustive list) of readable storage media include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof.

[0093] The basic principles of the present invention have been described above with reference to specific embodiments. However, it should be noted that the advantages, benefits, and effects mentioned in the present invention are merely examples and not limitations, and should not be considered as essential features of each embodiment of the present invention. Furthermore, the specific details disclosed above are for illustrative and facilitative purposes only, and are not limitations. These details do not limit the present invention to the necessity of employing the aforementioned specific details.

[0094] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For system embodiments, since they largely correspond to method embodiments, the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.

[0095] The block diagrams of devices, systems, devices, and systems involved in this invention are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, systems, devices, and systems can be connected, arranged, and configured in any manner. Words such as “comprising,” “including,” “having,” etc., are open-ended terms meaning “including but not limited to,” and are used interchangeably with them. The terms “or” and “and” as used herein refer to the terms “and / or,” and are used interchangeably with them unless the context clearly indicates otherwise. The term “such as” as used herein refers to the phrase “such as but not limited to,” and is used interchangeably with it.

[0096] The methods and systems of the present invention may be implemented in many ways. For example, they may be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above-described order of steps for the methods is for illustrative purposes only, and the steps of the methods of the present invention are not limited to the order specifically described above unless otherwise specifically stated. Furthermore, in some embodiments, the present invention may also be implemented as a program recorded on a recording medium, the program comprising machine-readable instructions for implementing the methods according to the present invention. Thus, the present invention also covers recording media storing programs for performing the methods according to the present invention.

[0097] It should also be noted that in the systems, apparatus, and methods of the present invention, the components or steps can be disassembled and / or recombined. These disassemblies and / or recombinations should be considered equivalents of the present invention. The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use the invention. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein can be applied to other aspects without departing from the scope of the invention. Therefore, the invention is not intended to be limited to the aspects shown herein, but rather to be carried out within the widest scope consistent with the principles and novel features disclosed herein.

[0098] The above description has been given for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of the invention to the forms disclosed herein. Although numerous exemplary aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations therein.

Claims

1. A method for automatically generating intelligent content tags based on artificial intelligence, characterized in that, include: The article title and content of the article to be processed are preprocessed to obtain preprocessed data; Semantic association analysis is performed on the preprocessed data to obtain the basic tags of the article to be processed; Based on the user profile of the article to be processed, the basic tags are personalized and optimized to generate an automated tag set for the article to be processed; The automated tags are dynamically weighted and sorted to obtain a smart tag set.

2. The method according to claim 1, characterized in that, Semantic association analysis is performed on the preprocessed data to obtain the basic tags of the article to be processed, including: Semantic analysis methods are used to capture semantic association information in the preprocessed data to obtain the basic tags of the article to be processed.

3. The method according to claim 1, characterized in that, Based on the user profile of the article to be processed, the basic tags are personalized and optimized to generate an automated tag set for the article to be processed, including: Analyze users' historical behavior, preferences, click records, and personalized characteristics to automatically generate personalized tags for users' interests; The basic transitions are optimized based on the personalized tags to generate an automated tag set for the article to be processed.

4. The method according to claim 1, characterized in that, Dynamically assigning tag weights to the automated tags includes: The weights of each tag in the automated tag set are dynamically adjusted based on the correlation between the automated tags and the content in the preprocessed data, as well as user behavior information.

5. The method according to claim 1, characterized in that, Also includes: Cross-media tag extraction is performed by combining image, video, and audio data.

6. An intelligent content tag automatic generation device based on artificial intelligence, characterized in that, include: The preprocessing module is used to preprocess the article title and content of the article to be processed in order to obtain preprocessed data; The analysis module is used to perform semantic association analysis on the preprocessed data to obtain the basic tags of the article to be processed; The generation module is used to personalize and optimize the basic tags based on the user profile of the article to be processed, and generate an automated tag set for the article to be processed. The allocation module is used to dynamically allocate tag weights to the automated tags and sort them to obtain a smart tag set.

7. The method according to claim 6, characterized in that, The analysis module includes: The capture submodule is used to capture semantic association information in the preprocessed data using semantic analysis methods to obtain the basic tags of the article to be processed.

8. The apparatus according to claim 6, characterized in that, The generation module includes: The analysis submodule is used to analyze users' historical behavior, preferences, click records and personalized characteristics, and automatically generate personalized tags for users' interests. A generation submodule is used to optimize the basic transitions based on the personalized tags and generate an automated tag set for the article to be processed.

9. A computer-readable storage medium, characterized in that, The storage medium stores a computer program for performing the method described in any one of claims 1-5.

10. An electronic device, characterized in that, The electronic device includes: processor; Memory used to store the processor's executable instructions; The processor is configured to read the executable instructions from the memory and execute the instructions to implement the method described in any one of claims 1-5.

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