Content recommendation method and apparatus, electronic device, and computer-readable storage medium

Abstract

The application relates to the technical field of artificial intelligence, and provides a content recommendation method and device, an electronic device and a computer readable storage medium, the method comprises the following steps: acquiring user information, the user information comprises user attribute information and user behavior information; recommending page content according to the user attribute information and the user behavior information; storing the access tag index of the page content into a to-be-observed fuse list; in a preset time, adjusting and processing the access tag index in the to-be-observed fuse list according to the access condition of the page content; and performing content recommendation processing according to the to-be-observed fuse list after the adjustment processing. Through the technical scheme, the content can be updated in time, and good user experience is brought to the user.

Description

Technical Field

[0001] This application relates to, but is not limited to, the field of artificial intelligence technology, and in particular to a content recommendation method, apparatus, electronic device, and computer-readable storage medium. Background Technology

[0002] In some existing financial industry websites, current page content recommendation technology mainly tags users based on their usage and search behaviors on the platform, assigning them preference tags. The backend then calculates and recommends content with similar tags to each user, providing personalized content display. However, current page content recommendation technology cannot update the tags of interest to users in a timely manner, resulting in a poor user experience. Summary of the Invention

[0003] The following is an overview of the subject matter described in detail herein. This overview is not intended to limit the scope of the claims.

[0004] To address the problems mentioned in the background section, embodiments of this application provide a content recommendation method, apparatus, electronic device, and computer-readable storage medium, which enable timely updates of recommended content and provide users with a better user experience.

[0005] In a first aspect, embodiments of this application provide a content recommendation method, the method comprising:

[0006] Obtain user information, which includes user attribute information and user behavior information;

[0007] Recommend page content based on the user attribute information and the user behavior information;

[0008] Store the access tag index of the page content in the circuit breaker list to be observed;

[0009] Within a preset time period, the index of the access tag in the circuit breaker list to be observed is adjusted according to the access status of the page content;

[0010] Content recommendation processing is performed based on the adjusted list of circuit breakers to be observed.

[0011] According to some embodiments of this application, the page content includes job display content and tag content, and the step of recommending page content based on the user attribute information and the user behavior information includes:

[0012] The job posting content is recommended based on the user attribute information and a preset first recommendation strategy.

[0013] The user behavior information is analyzed and processed to obtain behavior tag information, and the tag content is recommended based on the behavior tag information.

[0014] According to some embodiments of this application, the user attribute information includes job nature information, length of service information, and past performance information. The step of recommending the job display content based on the user attribute information and a preset first recommendation strategy includes:

[0015] First recommendation information is obtained based on the job nature information and the first recommendation strategy; second recommendation information is obtained based on the length of employment information and the first recommendation strategy; and third recommendation information is obtained based on the past performance information and the first recommendation strategy.

[0016] The job posting content is obtained by comprehensively analyzing the first recommendation information, the second recommendation information, and the third recommendation information.

[0017] According to some embodiments of this application, the user behavior information includes search history information and tag follow information, and the step of analyzing and processing the user behavior information to obtain behavior tag information includes:

[0018] The search record information is subjected to keyword analysis to obtain the first keyword information;

[0019] The tag attention information is processed by keyword extraction to obtain second keyword information;

[0020] The first keyword information and the second keyword information are merged and deduplicated to obtain the behavior tag information.

[0021] According to some embodiments of this application, recommending the tag content based on the behavioral tag information includes:

[0022] The first recommended keyword is determined based on the behavioral tag information;

[0023] The tagged content is extracted from the preset content database based on the first recommended keyword.

[0024] According to some embodiments of this application, the process of adjusting the access tag index in the circuit breaker list to be observed based on the access status of the page content includes:

[0025] If the number of times the page content is accessed is greater than or equal to a preset threshold, the access tag index corresponding to the page content is retained in the circuit breaker list to be observed.

[0026] If the number of times the page content is accessed is less than a preset threshold, the access tag index corresponding to the page content will be removed from the circuit breaker list to be observed.

[0027] According to some embodiments of this application, the content recommendation process based on the adjusted circuit breaker list includes:

[0028] Read the access tag index from the adjusted list of circuit breakers to be observed;

[0029] The corresponding page content is extracted from the preset content database based on the access tag index.

[0030] Secondly, embodiments of this application also provide a content recommendation device, the device comprising:

[0031] The first processing module is used to acquire user information, which includes user attribute information and user behavior information.

[0032] The second processing module is used to recommend page content based on the user attribute information and the user behavior information;

[0033] The third processing module is used to store the access tag index of the page content into the circuit breaker list to be observed;

[0034] The fourth processing module is used to adjust the access tag index in the circuit breaker list to be observed based on the access status of the page content within a preset time.

[0035] The fifth processing module is used to perform content recommendation processing based on the adjusted list of circuit breakers to be observed.

[0036] Thirdly, embodiments of this application also provide an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the content recommendation method described in the first aspect above.

[0037] Fourthly, embodiments of this application also provide a computer-readable storage medium storing computer-executable instructions for performing the content recommendation method described in the first aspect above.

[0038] The content recommendation method according to the embodiments provided in this application has at least the following beneficial effects: First, user information is obtained, including user attribute information and user behavior information; then, page content is recommended based on the user attribute information and user behavior information; next, the access tag index of the page content is stored in a circuit breaker list to be observed; within a preset time, the access tag index in the circuit breaker list to be observed is adjusted according to the access status of the page content; finally, content recommendation processing is performed based on the adjusted circuit breaker list to be observed. Through the above technical solution, timely updates of recommended content can be achieved, reducing the recommendation of page content that users are not interested in, and providing users with a better user experience. Attached Figure Description

[0039] The accompanying drawings are used to provide a further understanding of the technical solutions of this application and constitute a part of the specification. They are used together with the embodiments of this application to explain the technical solutions of this application and do not constitute a limitation on the technical solutions of this application.

[0040] Figure 1 This is a flowchart of a content recommendation method provided in one embodiment of this application;

[0041] Figure 2 This is a flowchart illustrating the content recommendation method for a content recommendation page provided in one embodiment of this application;

[0042] Figure 3 This is a flowchart illustrating the content display of recommended job positions in a content recommendation method provided in one embodiment of this application;

[0043] Figure 4 This is a flowchart illustrating the generation of behavioral tag information in a content recommendation method provided in one embodiment of this application;

[0044] Figure 5 This is a flowchart illustrating the extraction of tag content in a content recommendation method provided in one embodiment of this application;

[0045] Figure 6 This is a flowchart illustrating the updating of the circuit breaker list to be observed in a content recommendation method provided in one embodiment of this application;

[0046] Figure 7 This is a flowchart illustrating page content recommendation based on an observation circuit breaker list in one embodiment of the content recommendation method provided in this application;

[0047] Figure 8 This is a schematic diagram of a content recommendation device provided in one embodiment of this application;

[0048] Figure 9 This is a schematic diagram of an electronic device provided in one embodiment of this application. Detailed Implementation

[0049] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0050] It should be noted that although functional modules are divided in the device schematic diagram and a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first," "second," etc., in the specification, claims, and the aforementioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0051] It should be noted that, 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 belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0052] The embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence (AI) is the theory, method, technology, and application system that uses 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.

[0053] AI is a new technical science that studies and develops theories, methods, technologies, and application systems for simulating, extending, and expanding human intelligence. Artificial intelligence is a branch of computer science that attempts to understand the essence of intelligence and produce new intelligent machines that can react in a way similar to human intelligence. Research in this field includes robotics, speech recognition, image recognition, natural language processing, and expert systems. Artificial intelligence can simulate the information processes of human consciousness and thought. Furthermore, artificial intelligence utilizes digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceiving the environment, acquiring knowledge, and using that knowledge to achieve optimal results—the theories, methods, technologies, and application systems available for use.

[0054] 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.

[0055] Artificial intelligence, or AI, is the theory, method, technology, and application system that uses 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.

[0056] The servers involved in artificial intelligence technology can be standalone servers or cloud servers that provide basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDN), and big data and artificial intelligence platforms.

[0057] This application provides a content recommendation method, apparatus, electronic device, and computer-readable storage medium. First, user information is acquired, including user attribute information and user behavior information. Then, page content is recommended based on the user attribute information and user behavior information. Next, the access tag index of the page content is stored in a monitoring circuit breaker list. Within a preset time period, the access tag index in the monitoring circuit breaker list is adjusted based on the page content access status. Finally, content recommendation is performed based on the adjusted monitoring circuit breaker list. This technical solution enables timely updates of recommended content, reduces the recommendation of page content that users are not interested in, and provides a better user experience.

[0058] The content recommendation method provided in this application relates to the field of artificial intelligence technology. The content recommendation method provided in this application can be applied to a terminal, a server, or software running on either a terminal or a server. In some embodiments, the terminal can be a smartphone, tablet, laptop, desktop computer, etc.; the server can be configured as an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms; the software can be an application implementing the content recommendation method, but is not limited to the above forms.

[0059] This application can be used in a wide variety of general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0060] It should be noted that in all specific embodiments of this application, when processing data related to user identity or characteristics, such as user information, user behavior data, user historical data, and user location information, user permission or consent is obtained first. Furthermore, the collection, use, and processing of this data comply with relevant laws, regulations, and standards of the relevant countries and regions. In addition, when embodiments of this application require access to sensitive personal information of users, separate permission or consent from the user is obtained through pop-ups or redirects to confirmation pages. Only after obtaining the user's separate permission or consent is the necessary user-related data for the proper functioning of the embodiments of this application obtained.

[0061] The embodiments of this application will be further described below with reference to the accompanying drawings.

[0062] like Figure 1 As shown, Figure 1 This is a flowchart of a content recommendation method provided in one embodiment of this application, which includes, but is not limited to, steps S100 to S500.

[0063] Step S100: Obtain user information, which includes user attribute information and user behavior information;

[0064] Step S200: Recommend page content based on user attribute information and user behavior information;

[0065] Step S300: Store the access tag index of the page content into the circuit breaker list to be observed;

[0066] Step S400: Within a preset time period, adjust the access tag index in the circuit breaker list to be observed based on the access status of the page content.

[0067] Step S500: Perform content recommendation processing based on the adjusted list of circuit breakers to be observed.

[0068] It should be noted that the process first involves acquiring user information, including user attribute information and user behavior information. Next, page content is recommended based on this information. Then, the access tag index of the page content is stored in a monitoring and triggering list. Within a preset time period, the access tag index in the monitoring and triggering list is adjusted based on page content access patterns. Finally, content recommendation is performed based on the adjusted monitoring and triggering list. This technical solution enables timely updates of recommended content, reduces the recommendation of page content that users are not interested in, and provides a better user experience.

[0069] It should be noted that the acquisition of user information in this application embodiment requires the user's confirmation and permission, and related content and information will not be obtained without the user's knowledge; furthermore, the acquired content and information will be kept confidential, and will not be disclosed without the user's permission.

[0070] It is understandable that user information includes user attribute information, which can refer to the user's identity information. In some cases, a user's identity information can be defined based on their length of service. For example, a company can differentiate employees based on their years of service: those with less than one year of service are classified as new hires, those with one to five years of service as junior staff, those with five to ten years of service as mid-level staff, and those with more than ten years of service as senior staff. Alternatively, an employee's identity information can be defined and categorized based on their job title. For example, employee A is a junior staff member in the R&D department, employee B is a senior staff member in the marketing department, employee C is a mid-level staff member in the operations department, and employee D is a management staff member.

[0071] It is understandable that user information, including user behavior information, can be determined based on the user's search or browsing habits on the front-end page or their clicking behavior on relevant content tags. Before obtaining user behavior information, the user needs to confirm the relevant permission, and user behavior information will not be collected and processed without the user's confirmation of relevant permission.

[0072] It should be noted that page content is recommended based on user attribute information and user behavior information. For example, if the user attribute information determines that the user is a newly hired employee, the company's relevant structure and rules and regulations will be recommended to the user so that the new employee can familiarize himself with the company's relevant rules and regulations. If the user attribute information determines that the user is a mid-level employee, relevant page content will be recommended to the user based on the collected user behavior information.

[0073] It's worth noting that each page content corresponds to an access tag index. After the relevant page content is displayed to the user, its corresponding access tag index is cached in the observation circuit breaker list. This observation circuit breaker list can be set in a specific table in the database, used to cache the relevant access tag indexes. When a user needs to view the relevant page content, the system simply retrieves the relevant access tag index from the observation circuit breaker list based on the user's identity and related user behavior information, and then retrieves the relevant page content from the database based on the access tag index. Each user can be associated with a specific observation circuit breaker list, allowing for the rapid recommendation of relevant page content to a designated user.

[0074] It should be noted that, within a preset time period, the access tag index in the monitoring circuit breaker list can be adjusted and updated based on the page content access data. For example, if the preset time period is 2 days, and the page content is accessed less than the preset number of 3 times within 2 days, the access tag index corresponding to that page content will be removed from the monitoring circuit breaker list. If the preset time period is 2 days, and the page content is accessed more than the preset number of 3 times within 2 days, the corresponding access tag index will be retained in the monitoring circuit breaker list.

[0075] It should be noted that after updating the access tag index in the list of circuit breakers to be monitored, page content can be recommended to users based on the adjusted access tag index in the list of circuit breakers to be monitored. This allows for timely recommendations of more relevant page content to users, eliminating the need to browse less relevant content and providing a better user experience.

[0076] In some embodiments, such as Figure 2 As shown, the page content includes job postings and tag content. Step S200 may include, but is not limited to, steps S210 to S220.

[0077] Step S210: Recommend job postings based on user attribute information and a preset first recommendation strategy;

[0078] Step S220: Analyze and process user behavior information to obtain behavior tag information, and recommend tag content based on behavior tag information.

[0079] It should be noted that the page content includes job postings and tag content. Job postings are recommended based on user attribute information and a preset primary recommendation strategy. Then, user behavior information is analyzed and processed to obtain behavior tag information, and tag content is recommended based on the behavior tag information.

[0080] Understandably, job postings are recommended based on user attribute information and a preset first recommendation strategy. For example, when user attribute information indicates that the user is a new employee, the first recommendation strategy will recommend information about the company's development and related rules and regulations to the user, so that the new employee can understand the company's relevant content and integrate into the new work environment.

[0081] It is understandable that analyzing and processing user behavior information, such as user search records or browsing records, can yield behavioral tag information. Then, based on the obtained behavioral tag information, tag content can be recommended and displayed to users with highly relevant tags, so that users can become familiar with and carry out related tasks.

[0082] In some embodiments, such as Figure 3 As shown, the user attribute information includes job nature information, length of service information and past performance information. The above step S210 may also include, but is not limited to, steps S211 to S212.

[0083] Step S211: Obtain first recommendation information based on job nature information and the first recommendation strategy; obtain second recommendation information based on length of service and the first recommendation strategy; and obtain third recommendation information based on past performance information and the first recommendation strategy.

[0084] Step S212: Perform a comprehensive analysis of the first, second, and third recommended information to obtain the job posting content.

[0085] It should be noted that user attribute information includes job nature information, length of service information, and past performance information. In the process of recommending job display content, the first recommendation information is obtained based on the job nature information and the first recommendation strategy; the second recommendation information is obtained based on the length of service information and the first recommendation strategy; and the third recommendation information is obtained based on the past performance information and the first recommendation strategy. Finally, the first, second, and third recommendation information are comprehensively analyzed to obtain the corresponding job display content.

[0086] Understandably, the first recommendation information can be obtained based on job nature information and the first recommendation strategy. That is, page content suitable for recommending to users is selected according to their job nature information. For example, if the user belongs to the company's operations and maintenance department, page content related to product operations and maintenance will be recommended to the user. The second recommendation information can be obtained based on the user's length of service with the first recommendation strategy. For example, if the user has been with the company for less than one year, they will be classified as a junior employee, and page content suitable for junior employees will be recommended to the user. The third recommendation information can be obtained based on past performance information and the first recommendation strategy. For example, if the user's past performance is average and there is room for improvement, page content guiding the user's further development will be recommended to the user. By comprehensively analyzing the above three recommendation information, job display content suitable for the user's job can be recommended.

[0087] In some embodiments, such as Figure 4 As shown, user behavior information includes search history information and tag attention information. The above step S220 may include, but is not limited to, steps S221, S222 and S223.

[0088] Step S221: Perform keyword analysis on the search record information to obtain the first keyword information;

[0089] Step S222: Extract keywords from the tag attention information to obtain the second keyword information;

[0090] Step S223: Merge and deduplicate the first keyword information and the second keyword information to obtain the behavior tag information.

[0091] It should be noted that keyword analysis of search records yields the first keyword information; keyword extraction of tag-related information yields the second keyword information; and merging and deduplicating the first and second keyword information yields the behavioral tag information.

[0092] It is worth noting that in this embodiment, the search record information is processed by keyword analysis. That is, keyword analysis is performed on the search records entered by the user to obtain the first keyword information that the user is inclined to view and understand; keyword extraction processing is performed on the tag attention information to obtain the second keyword information. Here, the tag attention information refers to the relevant tags that the user clicks to follow on the front-end page. Then, keyword extraction processing is performed on the tag attention information to obtain the second keyword information; finally, the first keyword information and the second keyword information are merged and deduplicated to obtain the behavioral tag information.

[0093] In some embodiments, such as Figure 5 As shown, step S220 may also include, but is not limited to, steps S224 and S225.

[0094] Step S224: Determine the first recommended keyword based on the behavioral tag information;

[0095] Step S225: Extract tag content from the preset content database based on the first recommended keyword.

[0096] It should be noted that in the process of obtaining tag content, the first recommended keyword is determined based on the behavioral tag information; then, relevant tag content is extracted from the preset content database based on the first recommended keyword.

[0097] It is worth noting that the first recommended keyword is determined based on behavioral tag information; thus, the corresponding tag content can be extracted from the preset content database based on the first recommended keyword.

[0098] In some embodiments, such as Figure 6 As shown, step S400 may include, but is not limited to, steps S410 and S420.

[0099] Step S410: If the number of times the page content is accessed is greater than or equal to a preset threshold, retain the access tag index corresponding to the page content in the circuit breaker list to be observed.

[0100] Step S420: If the number of times the page content is accessed is less than a preset threshold, remove the access tag index corresponding to the page content from the circuit breaker list to be observed.

[0101] It should be noted that if the number of times a page content is accessed is greater than or equal to a preset threshold, the access tag index corresponding to the page content will be retained in the circuit breaker list to be observed; if the number of times a page content is accessed is less than the preset threshold, the access tag index corresponding to the page content will be removed from the circuit breaker list to be observed, so as to update the circuit breaker list to be observed.

[0102] It is worth noting that the access tag index corresponding to the page content is adjusted based on the number of times the page content is accessed within a preset time period. For example, the time period is set to 3 days, and the preset number of accesses is 5. If a page content is accessed 4 times within 3 days, which is less than the preset number of accesses, the access tag index corresponding to that page content will be removed from the monitoring circuit breaker list. If another page content is accessed 6 times within 3 days, which is more than the preset number of accesses, the access tag index corresponding to that page content will be retained in the monitoring circuit breaker list.

[0103] In some embodiments, such as Figure 7 As shown, step S500 may include, but is not limited to, steps S510 and S520.

[0104] Step S510: Read the access tag index from the adjusted list of circuit breakers to be observed;

[0105] Step S520: Extract the corresponding page content from the preset content database according to the access tag index.

[0106] It should be noted that after the list of pending circuit breakers is updated, when a user logs in again to view the page content, the access tag index will be read from the adjusted list of pending circuit breakers. Then, based on the access tag index, the corresponding page content will be extracted from the preset content database, so that page content with relatively high attention can be recommended to the user in a timely manner, bringing a good user experience.

[0107] Understandably, each user can be assigned a monitoring list, and the two are linked. The access tag indexes corresponding to frequently viewed page content are stored in the monitoring list, and these indexes are updated based on access patterns within a preset timeframe. This allows for timely recommendations of highly relevant page content, improving user convenience. For example, if a user frequently browses page A but no longer needs to, the access tag index for page A will be removed from the monitoring list, ensuring timely updates to the page content the user is interested in.

[0108] In addition, such as Figure 8 As shown, one embodiment of this application also provides a content recommendation device 10, including:

[0109] The first processing module 100 is used to obtain user information, which includes user attribute information and user behavior information.

[0110] The second processing module 200 is used to recommend page content based on user attribute information and user behavior information;

[0111] The third processing module 300 is used to store the access tag index of the page content into the circuit breaker list to be observed;

[0112] The fourth processing module 400 is used to adjust the access tag index in the circuit breaker list under observation according to the access status of the page content within a preset time.

[0113] The fifth processing module 500 is used to perform content recommendation processing based on the adjusted list of circuit breakers to be observed.

[0114] It should be noted that the process first involves acquiring user information, including user attribute information and user behavior information. Next, page content is recommended based on this information. Then, the access tag index of the page content is stored in a monitoring and triggering list. Within a preset time period, the access tag index in the monitoring and triggering list is adjusted based on page content access patterns. Finally, content recommendation is performed based on the adjusted monitoring and triggering list. This technical solution enables timely updates of recommended content, reduces the recommendation of page content that users are not interested in, and provides a better user experience.

[0115] It should be noted that the acquisition of user information in this application embodiment requires the user's confirmation and permission, and related content and information will not be obtained without the user's knowledge; furthermore, the acquired content and information will be kept confidential, and will not be disclosed without the user's permission.

[0116] It is understandable that user information, including user behavior information, can be determined based on the user's search or browsing habits on the front-end page or their clicking behavior on relevant content tags. Before obtaining user behavior information, the user needs to confirm the relevant permission, and user behavior information will not be collected and processed without the user's confirmation of relevant permission.

[0117] It should be noted that page content is recommended based on user attribute information and user behavior information. For example, if the user attribute information determines that the user is a newly hired employee, the company's relevant structure and rules and regulations will be recommended to the user so that the new employee can familiarize himself with the company's relevant rules and regulations. If the user attribute information determines that the user is a mid-level employee, relevant page content will be recommended to the user based on the collected user behavior information.

[0118] It's worth noting that each page content corresponds to an access tag index. After the relevant page content is displayed to the user, its corresponding access tag index is cached in the observation circuit breaker list. This observation circuit breaker list can be set in a specific table in the database, used to cache the relevant access tag indexes. When a user needs to view the relevant page content, the system simply retrieves the relevant access tag index from the observation circuit breaker list based on the user's identity and related user behavior information, and then retrieves the relevant page content from the database based on the access tag index. Each user can be associated with a specific observation circuit breaker list, allowing for the rapid recommendation of relevant page content to a designated user.

[0119] It should be noted that, within a preset time period, the access tag index in the monitoring circuit breaker list can be adjusted and updated based on the page content access data. For example, if the preset time period is 2 days, and the page content is accessed less than the preset number of 3 times within 2 days, the access tag index corresponding to that page content will be removed from the monitoring circuit breaker list. If the preset time period is 2 days, and the page content is accessed more than the preset number of 3 times within 2 days, the corresponding access tag index will be retained in the monitoring circuit breaker list.

[0120] It should be noted that after updating the access tag index in the list of circuit breakers to be monitored, page content can be recommended to users based on the adjusted access tag index in the list of circuit breakers to be monitored. This allows for timely recommendations of more relevant page content to users, eliminating the need to browse less relevant content and providing a better user experience.

[0121] The specific implementation of the content recommendation device 10 is basically the same as the specific embodiment of the content recommendation method described above, and will not be repeated here.

[0122] In addition, such as Figure 9 As shown, one embodiment of this application also provides an electronic device 700, which includes: a memory 720, a processor 710, and a computer program stored on the memory 720 and executable on the processor 710.

[0123] The processor 710 and memory 720 can be connected via a bus or other means.

[0124] The non-transitory software program and instructions required to implement the content recommendation method of the above embodiments are stored in the memory 720. When executed by the processor 710, the content recommendation method of each of the above embodiments is executed, for example, the method described above is executed. Figure 1 Method steps S100 to S500 Figure 2Method steps S210 to S220, Figure 3 Method steps S211 to S212 in the text Figure 4 Method steps S221 to S223 in the text Figure 5 Method steps S224 to S225 in the text Figure 6 Method steps S410 to S420 and Figure 7 Method steps S510 to S520.

[0125] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0126] Furthermore, one embodiment of this application also provides a computer-readable storage medium storing computer-executable instructions that are executed by a processor 710 or a controller, for example, by a processor 710 in the above-described device embodiment, causing the processor 710 to perform the content recommendation method described above, for example, performing the above-described... Figure 1 Method steps S100 to S500 Figure 2 Method steps S210 to S220, Figure 3 Method steps S211 to S212 in the text Figure 4 Method steps S221 to S223 in the text Figure 5 Method steps S224 to S225 in the text Figure 6 Method steps S410 to S420 and Figure 7 Method steps S510 to S520.

[0127] The above embodiments can be used in combination, and modules with the same name in different embodiments may be the same or different.

[0128] The foregoing has described specific embodiments of this application; other embodiments are within the scope of the appended claims. In some cases, the actions or steps described in the claims may be performed in a different order than those shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily have to follow the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0129] The various embodiments in this application are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the embodiments of apparatus, devices, and computer-readable storage media are basically similar to the method embodiments, so the descriptions are relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0130] The apparatus, device, computer-readable storage medium and method provided in the embodiments of this application are corresponding. Therefore, the apparatus, device and non-volatile computer storage medium also have similar beneficial technical effects as the corresponding method. Since the beneficial technical effects of the method have been described in detail above, the beneficial technical effects of the corresponding apparatus, device and computer storage medium will not be described again here.

[0131] In the 1990s, improvements to a technology could be clearly distinguished as either hardware improvements (e.g., improvements to the circuit structure of diodes, transistors, switches, etc.) or software improvements (improvements to the methodology). However, with technological advancements, many methodological improvements today can be considered direct improvements to the hardware circuit structure. Designers almost always obtain the corresponding hardware circuit structure by programming the improved methodology into the hardware circuit. Therefore, it cannot be said that a methodological improvement cannot be implemented using hardware physical modules. For example, a Programmable Logical Device (PLD) (such as a Field Programmable Gate Array (FPGA)) is such an integrated circuit whose logic function is determined by the user programming the device. Designers can program and "integrate" a digital system onto a PLD themselves, without needing chip manufacturers to design and manufacture dedicated integrated circuit chips. Moreover, nowadays, instead of manually manufacturing integrated circuit chips, this programming is mostly implemented using "logic compiler" software. It's similar to the software compiler used in program development, and the source code before compilation must be written in a specific programming language, called a Hardware Description Language (HDL). There are many HDLs, such as ABEL (Advanced Board Express Language), AHDL (Altera Hardware Description Language), Confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), Lava, Lola, MyHDL, PALASM, and RHDL (Ruby Hardware Description Language). Currently, VHDL (Very-High-Speed ​​Intelligent Logic Logic) is the most widely used. ntegrated Ci rcu itHardware Descr i pt i on Language) and Ver il og.Those skilled in the art should also understand that by simply performing some logic programming on the method flow using the aforementioned hardware description languages ​​and programming it into an integrated circuit, the hardware circuit that implements the logic method flow can be easily obtained.

[0132] The controller can be implemented in any suitable manner. For example, it can take the form of a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro)processor, logic gates, switches, application-specific integrated circuits (ASICs), programmable logic controllers, and embedded microcontrollers. Examples of controllers include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PI C18F26K20, and Sillicore Labs C8051F320. Memory controllers can also be implemented as part of the control logic of the memory. Those skilled in the art will also recognize that, in addition to implementing the controller in purely computer-readable program code form, the same functionality can be achieved by logically programming the method steps to make the controller take the form of logic gates, switches, ASICs, programmable logic controllers, and embedded microcontrollers. Therefore, such a controller can be considered a hardware component, and the means included therein for implementing various functions can also be considered as structures within the hardware component. Alternatively, the device used to implement various functions can be viewed as either a software module that implements the method or a structure within a hardware component.

[0133] The systems, devices, modules, or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, a computer can be, for example, a personal computer, laptop computer, cellular phone, camera phone, smartphone, personal digital assistant, media player, navigation device, email device, game console, tablet computer, wearable device, or any combination of these devices.

[0134] For ease of description, the above apparatus is described by dividing it into various functional units. Of course, in implementing the embodiments of this application, the functions of each unit can be implemented in one or more software and / or hardware.

[0135] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, embodiments of this application can take the form of entirely hardware embodiments, entirely software embodiments, or embodiments combining software and hardware aspects. Furthermore, embodiments of this application can take the form of computer program products implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0136] This specification is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0137] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0138] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0139] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0140] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash memory. Memory is an example of computer-readable media.

[0141] Computer-readable media include both permanent and non-permanent, removable and non-removable media that can store information by any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transfer medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media do not include transient computer-readable media, such as modulated data signals and carrier waves.

[0142] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0143] In this application embodiment, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent the existence of A alone, A and B simultaneously, or B alone. A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one of the following" and similar expressions refer to any combination of these items, including any combination of singular or plural items. For example, at least one of a, b, and c can represent: a, b, c, a and b, a and c, b and c, or a and b and c, where a, b, and c can be single or multiple.

[0144] The embodiments of this application can be described in the general context of computer-executable instructions, such as program modules, that are executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform a specific task or implement a specific abstract data type. The embodiments of this application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In a distributed computing environment, program modules can reside in local and remote computer storage media, including storage devices.

[0145] The various embodiments in this application are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0146] The above description is merely an embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of this application should be included within the scope of the claims of this application.

Claims

1. A content recommendation method, characterized in that, The method includes: Obtain user information, which includes user attribute information and user behavior information; Recommend page content based on the user attribute information and the user behavior information; Store the access tag index of the page content in the circuit breaker list to be observed; Within a preset time period, the index of the access tag in the circuit breaker list to be observed is adjusted according to the access status of the page content; Content recommendation processing is performed based on the adjusted list of circuit breakers to be observed. The user behavior information includes search history information and tag follow information; the user behavior information is analyzed and processed to obtain behavior tag information, including: The search record information is subjected to keyword analysis to obtain the first keyword information; The tag attention information is processed by keyword extraction to obtain second keyword information; The first keyword information and the second keyword information are merged and deduplicated to obtain the behavior tag information; The process of adjusting the access tag index in the circuit breaker list based on the access status of the page content includes: If the number of times the page content is accessed is greater than or equal to a preset threshold, the access tag index corresponding to the page content is retained in the circuit breaker list to be observed. If the number of times the page content is accessed is less than a preset threshold, the access tag index corresponding to the page content will be removed from the circuit breaker list to be observed.

2. The content recommendation method according to claim 1, characterized in that, The page content includes job postings and tag content. The step of recommending page content based on user attribute information and user behavior information includes: The job posting content is recommended based on the user attribute information and a preset first recommendation strategy. Recommend the tag content based on the behavioral tag information.

3. The content recommendation method according to claim 2, characterized in that, The user attribute information includes job nature information, length of service information, and past performance information. The step of recommending job display content based on the user attribute information and a preset first recommendation strategy includes: First recommendation information is obtained based on the job nature information and the first recommendation strategy; second recommendation information is obtained based on the length of employment information and the first recommendation strategy; and third recommendation information is obtained based on the past performance information and the first recommendation strategy. The job posting content is obtained by comprehensively analyzing the first recommendation information, the second recommendation information, and the third recommendation information.

4. The content recommendation method according to claim 2, characterized in that, The step of recommending the tag content based on the behavior tag information includes: The first recommended keyword is determined based on the behavioral tag information; The tagged content is extracted from the preset content database based on the first recommended keyword.

5. The content recommendation method according to claim 1, characterized in that, The content recommendation process based on the adjusted list of circuit breakers to be observed includes: Read the access tag index from the adjusted list of circuit breakers to be observed; The corresponding page content is extracted from the preset content database based on the access tag index.

6. A content recommendation device, characterized in that, The device includes: The first processing module is used to acquire user information, which includes user attribute information and user behavior information. The second processing module is used to recommend page content based on the user attribute information and the user behavior information; The third processing module is used to store the access tag index of the page content into the circuit breaker list to be observed; The fourth processing module is used to adjust the access tag index in the circuit breaker list to be observed based on the access status of the page content within a preset time. The fifth processing module is used to perform content recommendation processing based on the adjusted circuit breaker list to be observed; The user behavior information includes search history information and tag follow information; the user behavior information is analyzed and processed to obtain behavior tag information, including: The search record information is subjected to keyword analysis to obtain the first keyword information; The tag attention information is processed by keyword extraction to obtain second keyword information; The first keyword information and the second keyword information are merged and deduplicated to obtain the behavior tag information; The process of adjusting the access tag index in the circuit breaker list based on the access status of the page content includes: If the number of times the page content is accessed is greater than or equal to a preset threshold, the access tag index corresponding to the page content is retained in the circuit breaker list to be observed. If the number of times the page content is accessed is less than a preset threshold, the access tag index corresponding to the page content will be removed from the circuit breaker list to be observed.

7. An electronic device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor, when executing the computer program, implements the content recommendation method as described in any one of claims 1 to 5.

8. A computer-readable storage medium storing computer-executable instructions, characterized in that, The computer-executable instructions are used to execute the content recommendation method according to any one of claims 1 to 5.

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