CN109150983B - Front-end display control method and device and data recommendation control method and device - Google Patents
Front-end display control method and device and data recommendation control method and device Download PDFInfo
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- CN109150983B CN109150983B CN201810841557.6A CN201810841557A CN109150983B CN 109150983 B CN109150983 B CN 109150983B CN 201810841557 A CN201810841557 A CN 201810841557A CN 109150983 B CN109150983 B CN 109150983B
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- H04—ELECTRIC COMMUNICATION TECHNIQUE
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- H04L67/00—Network arrangements or protocols for supporting network services or applications
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- H04L67/00—Network arrangements or protocols for supporting network services or applications
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Abstract
The invention discloses a front-end display control method, a front-end display control device and a front-end display control system, a data recommendation control method and a data recommendation control device, electronic equipment and a computer readable storage medium. According to the technical scheme provided by the invention, the general data is pulled from the recommendation side in advance for caching, so that the cached general data can be pushed to the front end for displaying when the personalized data is failed to be pulled, and the display of the front end is prevented from being displayed in a white screen mode. According to the scheme, the personalized data requested by the user does not need to be cached every time, so that the processing logic of personalized data recommendation with the white screen prevention function is simplified, and the data recommendation efficiency and stability are improved; in addition, when the personalized data is failed to be pulled, the cached personalized data of a certain user is not returned to the requesting user, but the general data cached in advance is returned, because the general data is universally applicable, the discomfort brought to the user by the returned data when the recommendation fails can be reduced, and the accuracy of data pushing is improved.
Description
Technical Field
The present invention relates to the field of internet technologies, and in particular, to a front-end display control method and apparatus, a data recommendation control method and apparatus, a front-end display control system, an electronic device, and a computer-readable storage medium.
Background
With the development of computing technology, internet products such as various websites and clients enrich the work and life of the public, and bring various conveniences to people. For example, the advent of short video-like software APP (application program) allows a user to acquire the most interesting content with less time.
The short video software APP mainly calculates personalized data in real time and pushes the personalized data to the user by combining information such as the portrait of the user and the label of the short video, and therefore the personalized data pushed to the user is the content which the user is interested in. Compared with the pushing of non-personalized static data, the real-time dynamic data has larger calculation amount, more time consumption and relative complexity, so that the situation of data pulling failure is easy to occur, and the display interface of the client is in a white screen state due to no content which can be displayed at the moment.
In order to prevent the display interface of the client from being blank, currently, the personalized data pushed to the user for the last time is mainly cached in a background server, so that the cached personalized data is pushed to all the users requesting the personalized data when the data pulling fails. Therefore, when the data pulling fails, all users receive the content which is interested by a certain user, and the accuracy of data pushing is reduced.
Disclosure of Invention
In order to solve the problem that all users receive the content which is interesting to a certain user and the accuracy of data pushing is reduced when personalized data pulling fails in the related technology, the invention provides a control method for front-end display.
In a first aspect, the present invention provides a method for controlling front-end display, the method including:
when pulling personalized data from a recommendation side fails, requesting to acquire general data cached in advance;
acquiring the general data according to the request, wherein the general data is data which is pre-pulled and cached from the recommending side;
and pushing the acquired general data to the front end requesting the personalized data for displaying.
In a second aspect, the present invention provides a method for controlling data recommendation, the method including:
receiving a data recall request, wherein the data recall request carries a random user account;
responding to the data recall request, and generating general data corresponding to the random user account;
returning the generated general data for the data recall request; the general data is used for displaying the general data at the front end when the personalized data returned to the front end fails.
In a third aspect, the present invention provides a control system for front-end display, the system comprising: the system comprises a recommendation server, an application server and a white screen prevention server;
the recommendation server is used for responding to a request sent by the application server and returning personalized data and responding to a request sent by the white screen prevention server and returning general data;
the white screen prevention server is used for requesting the recommendation server to pull the general data and pushing the general data to the application server for caching;
the application server is used for requesting the recommendation server to pull the personalized data, and pushing the cached general data to the front end requesting the personalized data to display when the personalized data is failed to be pulled.
In a fourth aspect, the present invention provides a front-end display control apparatus, comprising:
the universal data request module is used for requesting to acquire the universal data cached in advance when the personalized data pulling from the recommending side fails;
a general data acquisition module, configured to acquire the general data according to the request, where the general data is data that is pulled and cached in advance from the recommendation side;
and the universal data pushing module is used for pushing the acquired universal data to the front end which requests the personalized data to display.
In a fifth aspect, the present invention provides a control apparatus for data recommendation, the apparatus comprising:
the device comprises a recall request receiving module, a recall request processing module and a recall processing module, wherein the recall request receiving module is used for receiving a data recall request which carries a random user account;
the universal data generating module is used for responding to the data recall request and generating universal data corresponding to the random user account;
the general data return module is used for returning the generated general data for the data recall request; the general data is used for displaying the general data at the front end when the personalized data returned to the front end fails.
In a sixth aspect, the present invention provides an electronic device, comprising:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to execute the control method of the front-end display or execute the control method of the data recommendation.
In a seventh aspect, the present invention provides a computer-readable storage medium, which stores a computer program, where the computer program is executable by a processor to perform the control method for performing the front-end display or the control method for performing the data recommendation.
The technical scheme provided by the embodiment of the invention can have the following beneficial effects:
according to the technical scheme provided by the invention, the general data is pulled from the recommendation side in advance for caching, so that the cached general data can be pushed to the front end for displaying when the personalized data is failed to be pulled, and the display of the front end is prevented from being displayed in a white screen mode. According to the scheme, the personalized data requested by the user does not need to be cached every time, so that the processing logic of personalized data recommendation with the white screen prevention function is simplified, and the data recommendation efficiency and stability are improved; in addition, when the personalized data is failed to be pulled, the cached personalized data of a certain user is not returned to the requesting user, but the general data cached in advance is returned, because the general data is universally applicable, the discomfort brought to the user by the returned data when the recommendation fails can be reduced, and the accuracy of data pushing is improved.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
FIG. 1 is a schematic illustration of an implementation environment to which the present invention relates, according to an exemplary embodiment
Fig. 2 is a schematic structural diagram of a server according to an embodiment of the present invention;
FIG. 3 is a flow chart illustrating a method of controlling a front-end display in accordance with an exemplary embodiment;
FIG. 4 is a strategy diagram of non-personalized APP white screen prevention;
FIG. 5 is a strategy diagram of personalized APP white screen prevention;
fig. 6 is a flowchart of a front-end display control method according to another embodiment based on the corresponding embodiment in fig. 3;
FIG. 7 is a detailed flowchart of step 410 in a corresponding embodiment of FIG. 6;
FIG. 8 is a detailed flowchart of step 310 in a corresponding embodiment of FIG. 3;
FIG. 9 is a detailed flowchart of step 350 in the corresponding embodiment of FIG. 3;
FIG. 10 is a flow chart illustrating a method of controlling data recommendation in accordance with an exemplary embodiment.
FIG. 11 is a block diagram illustrating a control system for a front end display in accordance with an exemplary embodiment;
FIG. 12 is a flowchart illustrating steps performed by a recommendation server in the front-end display control system of the embodiment of FIG. 11;
fig. 13 is a flowchart illustrating steps performed by the white screen prevention server in the front-end display control system according to the embodiment of fig. 11;
FIG. 14 is a flowchart illustrating steps performed by an application server in the front-end display control system of the embodiment shown in FIG. 11;
FIG. 15 is a block diagram illustrating a control device for a front end display in accordance with an exemplary embodiment;
fig. 16 is a block diagram of a control device of a front-end display according to another embodiment based on the corresponding embodiment of fig. 15;
FIG. 17 is a detailed block diagram of a generic data pull module in a corresponding embodiment of FIG. 16;
FIG. 18 is a block diagram illustrating details of generic data request module 1710 in a corresponding embodiment of FIG. 15;
FIG. 19 is a detailed block diagram of the generic data push module 1750 in a corresponding embodiment of FIG. 15;
fig. 20 is a block diagram illustrating a control apparatus for data recommendation according to another exemplary embodiment.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the invention, as detailed in the appended claims.
FIG. 1 is a schematic diagram illustrating an implementation environment to which the present invention relates, according to an exemplary embodiment. The embodiment relates to an implementation environment comprising an application server 110, a recommendation server 120, a white screen prevention server 130 and a plurality of mobile terminals 140. It should be noted that the front-end display control method provided by the present invention may be deployed in a server cluster formed by the application server 110, the recommendation server 120, and the white screen prevention server 130.
The mobile terminal 140 may be a smart phone or a tablet computer, and the personalized APP (application program) is installed in the mobile terminal 140. The personalized APP is a recommended APP which pushes pictures, texts, short videos and live videos related to the portrait information and historical behaviors of a user through technical means such as a recommendation algorithm.
For display, the mobile terminal 140 may send a personalized data acquisition request to the application server 110 by running the personalized APP, where the personalized data acquisition request may carry a user identifier of a target user to which the mobile terminal 140 belongs. The application server 110 sends a personalized data acquisition request of the target user to the recommendation server 120. The recommendation server 120 may fail to extract the personalized data of the target user due to the large amount of calculation, or the communication between the application server 110 and the recommendation server 120 may fail, thereby causing the application server 110 to be unable to pull the personalized data of the target user from the recommendation server 120.
In order to solve the above problem, the white screen prevention server 130 of the present invention may periodically generate a random account to request the recommendation server 120 to pull the general data, and cache the pulled general data in the application server 110. When the application server 110 cannot acquire the personalized data of the target user from the recommendation server 120, in order to prevent a white screen phenomenon from occurring on a display interface of the mobile terminal 140 due to no content being displayable, the application server 110 pushes the previously cached general data to the mobile terminal 140 for displaying.
It should be noted that the application server 110, the recommendation server 120, and the white screen prevention server 130 may be separately arranged and connected through a wired or wireless network. If desired, the execution logic of any two servers or three servers may be deployed in the same server, for example, the execution logic of the application server 110 and the recommendation server 120 may be deployed in the same server, and the server may execute the steps executed by the application server 110 and the recommendation server 120.
Referring to fig. 2, fig. 2 is a schematic diagram of a server structure according to an embodiment of the present invention. The server 200 may vary significantly depending on configuration or performance, and may include one or more Central Processing Units (CPUs) 222 (e.g., one or more processors) and memory 232, one or more storage media 230 (e.g., one or more mass storage devices) storing applications 242 or data 244. Memory 232 and storage medium 230 may be, among other things, transient or persistent storage. The program stored in the storage medium 230 may include one or more modules (not shown), each of which may include a series of instruction operations for the server 200. Still further, the central processor 222 may be configured to communicate with the storage medium 230 to execute a series of instruction operations in the storage medium 230 on the server 200. Server 200 may also include one or more power supplies 226, one or moreOne or more wired or wireless network interfaces 250, one or more input-output interfaces 258, and/or one or more operating systems 241, such as Windows ServerTM，Mac OS XTM，UnixTM,LinuxTM，FreeBSDTMAnd so on. The steps performed by the server described in the embodiments of fig. 3, 6-10 below may be based on the server architecture shown in fig. 2.
It will be understood by those skilled in the art that all or part of the steps for implementing the following embodiments may be implemented by hardware, or may be implemented by a program instructing relevant hardware, and the program may be stored in a computer-readable storage medium, and the above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, etc.
FIG. 3 is a flow chart illustrating a method of controlling a front-end display in accordance with an exemplary embodiment. The control method of the front-end display may be performed by a server, which may include the application server 110, the recommendation server 120, and the recommendation server 130 in the implementation environment shown in fig. 1. The following is an example of an implementation environment as shown in fig. 1. As shown in fig. 3, the control method may include the following steps.
In step 310, when pulling the personalized data from the recommending side fails, the pre-cached general data is requested to be acquired.
Wherein, the recommending side can be the recommending server 120 in the implementation environment shown in fig. 1. The failure to pull the personalized data includes that the pulled personalized data is less than one page and that the personalized data is not pulled. The personalized data is generated by the recommending side, and when the pulled personalized data is less than one page or the personalized data is not pulled, the personalized data is considered to be failed to be pulled from the recommending side.
It should be noted that the personalized data is generated for a specific user and conforms to the interests and hobbies of the specific user. In other words, the personalized data does not conform to the public's hobbies. Suppose a user's hobby is sports and its personalized data is news, video, etc. related to sports. However, many female users do not belong to sports enthusiasts, and if sports-related news and videos are pushed to the female users blindly, the users are lost. The general data mentioned in the invention refers to information data which accords with the public hobbies and is not generated for a certain user.
Specifically, the white screen prevention server 130 may generate a random account at regular time and send a data recall request to the recommendation server 120, and the recommendation server 120 generates general data according to the random account and returns the general data to the white screen prevention server 130. The white screen prevention server 130 may write the general data into its own database and periodically push the general data to the application server 110 for caching. Therefore, when the pulling of the personalized data fails, the application server 110 may initiate a data acquisition request of the shared memory to request to acquire the general data cached in advance.
In step 330, the general data is obtained according to the request, and the general data is data that is pre-pulled and cached from the recommending side.
Specifically, when the application server 110 fails to pull the personalized data from the recommendation server 120, it initiates a data acquisition request of the local shared memory to acquire general data cached in advance in the local shared memory. The general data is obtained by the white screen prevention server 130 requesting to pull from the recommendation server 120 in advance, and is cached in the application server 110 as the information data meeting the public hobbies.
In step 350, the obtained general data is pushed to the front end requesting the personalized data for displaying.
It should be noted that the front end may be the mobile terminal 140 in the implementation environment shown in fig. 1, and the front end requests the application server 110 to obtain the personalized data, and triggers the application server 110 to pull the personalized data of the specified user from the recommendation server. When the pulling of the personalized data fails, the application server 110 acquires the cached general data from the local shared memory and pushes the acquired general data to the front end for display, thereby preventing the display of the front end from being displayed in a white screen.
Fig. 4 is a policy diagram of non-personalized APP white screen prevention, and as shown in fig. 4, a cross section indicates a link which is prone to failure. The non-personalized APP refers to an APP which does not carry out data push according to the hobbies and interests of each user. After the APP terminal 601 (which may correspond to the mobile terminal 140 of fig. 1) acquires data from the logical backend server 602 (which may correspond to the application server 110 of fig. 1), one-screen or multi-screen data is cached. Similarly, after the logic background server 602 acquires data from the recommendation system 603 (which may correspond to the recommendation server 120 in fig. 1), data of several screens is cached, so that when a link between the APP terminal 601 and the logic background server 602 fails or a link between the logic background server 602 and the recommendation system 603 fails, the cached data may be pushed to a user, and the purpose of preventing a screen from being blank is achieved.
This way of preventing white screen is feasible for non-personalized APPs, but for personalized APPs the results returned for different users are different if the last data returned to the user is cached each time. Assuming that the user a initiates a request for the last time, after the logic background server 602 returns the personalized data that the user a is interested in to the terminal to which the user a belongs, the logic background server 602 caches the personalized data corresponding to the user a. As shown in fig. 5, all users (user a, user B, user C … …) get personalized data for user a when the link between the logical backend server 602 and the recommendation system 603 fails. For example, a user who likes to watch a funny video suddenly sees a mysterious video of 28536and a savory video under a tear. This gives the user a sense of discomfort and may cause the user to be lost.
In addition, the white screen prevention function is an additional function of data personalized recommendation, if personalized data are returned each time, a cache is synchronously written once, the logic step of data recommendation is added, the efficiency of data recommendation is influenced, if the white screen prevention logic processing problem occurs, the normal operation of the personalized APP is also influenced, and therefore the negative influence is not compensated.
According to the technical scheme provided by the invention, the general data is pulled from the recommendation side in advance for caching, so that the cached general data can be pushed to the front end for displaying when the personalized data is failed to be pulled, and the display of the front end is prevented from being displayed in a white screen mode. According to the scheme, the personalized data requested by the user does not need to be cached every time, the processing logic of personalized data recommendation with the white screen prevention function is simplified, the data recommendation efficiency is improved, in addition, when the personalized data is pulled and taken unsuccessfully, the cached personalized data of a certain user is not returned to the requesting user, but general data cached in advance is returned, and due to the fact that the general data is universally applicable, the discomfort brought to the user by the returned data when the recommendation is failed can be reduced, and the data pushing accuracy is improved.
At present, in order to prevent white screen, synchronous caching is needed for pushing personalized data every time, that is, the logic of preventing white screen is coupled with the logic of personalized pushing, and when the logic of preventing white screen has a problem, pushing of personalized data is also affected. The pulling of the general data and the pulling of the personalized data are independent and do not influence each other, and even if the pulling and the caching of the general data fail, the normal operation of the recommendation of the personalized data is not influenced, so that the usability and the stability of the personalized data push are improved.
In an exemplary embodiment, as shown in fig. 6, the method for controlling the front-end display provided by the present invention may further include the following steps. The following steps 410-430 may be performed by a server, which may be the white screen prevention server 130 of the implementation environment shown in fig. 1.
In step 410, a random user account is generated to pull the general data on the recommendation side, so as to obtain the general data corresponding to the random user account.
For example, the recommending side may be the recommending server 120 according to the implementation scenario shown in fig. 1. The white screen prevention server 130 generates a random user account, sends a data recall request containing the random user account to the recommendation server 120 at regular time, and triggers the recommendation server 120 to generate general data. The white screen prevention server 130 performs pulling of the general data from the recommendation server 120 to obtain the general data corresponding to the random user account.
In step 430, general data corresponding to the random user account is cached.
In order to speed up the efficiency of the application server 110 returning the general data to the front end, the white screen prevention server 130 pushes the obtained general data to the application server 110 at regular time, and the application server 110 caches the general data in the local shared memory.
In an exemplary embodiment, as shown in fig. 7, the step 410 may be performed by the white screen prevention server 130, and specifically may include the following steps 411 to 413.
In step 411, a random user account is periodically generated.
Specifically, the white screen prevention server 130 generates a random user account at regular time. The random user account may be considered as a fictitious user equipment number, and does not represent any existing user, that is, there is no user representation or historical behavior corresponding to the random user account.
In step 412, the random user account requests the recommending side to recall data, and the recommending side is triggered to extract general data corresponding to the random user account from all information data according to the current information popularity.
The recommending side may be the recommending server 120, the white screen prevention server 130 may send a data recall request carrying a random user account to the recommending server 120, and the recommending server 120 is requested to extract the general data from all information data through a deployed general data recall algorithm.
It should be noted that the recalling of the general data by the recommendation server 120 is a common recommendation method not based on the user portrait and behavior, taking recommending short videos as an example, adding a certain coefficient to parameters such as the praise amount L, the play amount P, the comment number C, the forwarding amount F of the videos and the like, and performing a formula L a1+P*a2+C*a3+F*a4And calculating to obtain the popularity ranking of all the short videos, and recommending the videos with higher popularity to the user. That is, the general-purpose data may be information data extracted from all information data, which is highly hot at the current time.
In step 413, the recommendation side pulls the general data, and feeds back the identification information of the pulled data to the recommendation side, so that the recommendation side is pulled next time to return the general data with different identification information.
It should be noted that the general data may be divided into a plurality of batches, and the data size of each batch of general data may be the data size that needs to be cached when the front end is refreshed once. The recommendation server 120 generates n times refreshed generic data for each request of the white screen prevention server 130. After pulling each piece of general data from the recommendation server 120, the white screen prevention server 130 feeds back the identification information of the general data to the recommendation server 120. The identification information may be the number, name, etc. of the batch of pulled data. Thus, the next time the anti-white screen server 130 pulls data from the recommendation server 120, the recommendation server 120 may return a different batch of generic data, i.e., generic data of a different identification information. The white screen prevention server 130 writes the n-brushed general data into redis (a high-performance database in the form of key value pairs), and if the pulling of one-brushed general data fails, the random user account is regenerated to request the recommendation server 120 for recalling data.
The white screen prevention server 130 pushes n-brushed data stored in redis to the application server 110 for caching at regular time, and the application server 110 may also request to pull new general data from the white screen prevention server 130 for caching when the local general data cached in the local cache is invalid or expired. The application server 110 may obtain the cached general data of the corresponding page according to the page turning information of the front end, and push the general data to the front end for displaying.
In an exemplary embodiment, as shown in fig. 8, the step 310 may be performed by the application server 110 of the implementation environment shown in fig. 1, and the step 310 may specifically include the following steps 311 to 312.
In step 311, according to a request for obtaining personalized data of a specified user, which is sent by a front end for display, the personalized data of the specified user is requested to be pulled from the recommending side.
The front end may be the mobile terminal 140 in the implementation environment shown in fig. 1, and the designated user may be a user to which the mobile terminal 140 belongs. The designated user personalization data refers to information data that is of interest to the front-end user. Specifically, the mobile terminal 140 sends a request for obtaining personalized data of a specified user to the application server 110 for display, the application server 110 sends a data recall request to the recommendation server 120 according to the request, and triggers the recommendation server 120 to generate personalized data for the specified user through a personalized data recall algorithm and return the personalized data to the application server 110.
In step 312, when the personalized data is failed to be pulled, an obtaining request for the general data cached in the shared memory is initiated.
If the recommendation server 120 fails to generate the personalized data or the connection between the recommendation server 120 and the application server 110 is interrupted, the application server 110 fails to pull the personalized data. When the personalized data is not pulled, the application server 110 initiates a request for obtaining the general data of the local shared memory, reads the cached general data, and pushes the general data to the mobile terminal 140 for display.
In an exemplary embodiment, as shown in fig. 9, the step 350 may be performed by the application server 110 in the implementation environment shown in fig. 1, and the step 350 may specifically include the following steps 351-352:
in step 351, when the personalized data pulled from the recommending side is less than one page, the personalized data is supplemented by the acquired general data, and full-screen display data containing the personalized data and the general data is formed.
It should be noted that, the personalized data acquisition failure can be considered as the personalized data acquisition failure due to the fact that the personalized data acquisition fails to reach one page. When the personalized data cannot be obtained (including failure in generating the personalized data or transmission interruption), the cached general data is directly obtained and pushed to the front end for display. When the personalized data is not obtained for one page, the application server 110 supplements the personalized data with the general data obtained from the cache to obtain a whole page of data. The full screen display data refers to a whole page of data containing general data and personalized data, and can fill the whole display screen.
In step 352, the full screen display data including the personalized data and the general data is pushed to the front end for display.
Taking the mobile terminal 140 as an example at the front end, the application server 110 pushes a whole page of data obtained by combining the personalized data and the general data to the mobile terminal 140, and triggers the mobile terminal 140 to perform full-screen display of the personalized data and the general data.
FIG. 10 is a flow chart illustrating a method of controlling data recommendation in accordance with an exemplary embodiment. The control method of the data recommendation may be executed by a server, which may be the recommendation server 120 in the implementation environment shown in fig. 1. The following is also illustrated in the context of the embodiment shown in FIG. 1. As shown in fig. 10, the control method may include the following steps.
In step 1210, receiving a data recall request, where the data recall request carries a random user account;
taking the implementation environment shown in fig. 1 as an example, the data recall request may be sent by the white screen prevention server 130 to the recommendation server 120, and the recommendation server 120 is requested to run an un-personalized data recall algorithm to generate general data meeting public interests. The recommendation server 120 receives a data recall request sent by the white screen prevention server 130, where the data recall request carries a random user account generated by the white screen prevention server 130.
In step 1230, generating general data corresponding to the random user account in response to the data recall request;
the recommendation server 120 responds to the data recall request sent by the white screen prevention server 130, and extracts general data which is in line with public interest from all information data through a configured non-personalized data recall algorithm instead of according to user portrait and historical behaviors.
In an exemplary embodiment, the step 1230 specifically includes: and extracting data from all information data according to the current information heat to obtain general data corresponding to the random user account.
Taking the short video as an example, the recommendation server 120 adds a certain coefficient according to parameters such as the praise amount L, the play amount P, the comment number C, the forwarding amount F and the like of each short video segment, and uses a formula L a1+P*a2+C*a3+F*a4Calculating to obtain the heat of each short video segment, and collecting all informationAnd sorting the data according to the heat degree, and taking the information data with higher heat degree as the general data corresponding to the random user account.
In step 1250, returning the generated generic data for the data recall request; the general data is used for displaying the general data at the front end when the personalized data returned to the front end fails.
Specifically, the recommendation server 120 returns the generated general data to the white-screen prevention server 130 that sent the data recall request, so that the white-screen prevention server 130 pushes the returned general data to the application server 110 for caching. The application server 110 responds to the personalized data acquisition request sent by the front end, requests the recommendation server 120 to pull the personalized data, and when the personalized data is failed to be pulled from the recommendation server 110, namely when the personalized data is failed to be returned to the front end, the application server 110 acquires the cached general data and displays the general data at the front end.
Wherein, the step 1250 specifically includes: and returning the general data successively for the data recall request, and successively returning the general data with different identification information according to the identification information of the returned general data each time.
It should be noted that the general data generated by the recommendation server 120 may include a plurality of batches representing data required for a plurality of refreshes, and each batch may have a corresponding number. The recommendation server 120 returns a batch of the general data to the white screen prevention server 130, and receives the identification information (such as a number) of the batch of data returned by the white screen prevention server 130, so that the recommendation server 120 subsequently returns different identification information, i.e. different batches of the general data, to the white screen prevention server 130, thereby preventing the same data from being returned.
FIG. 11 is a block diagram illustrating a control system for a front-end display, the system including: recommendation server 120, application server 110, and anti-white screen server 130;
the recommendation server 120 is configured to return personalized data in response to a request sent by an application server and return general data in response to a request sent by a white screen prevention server;
the white screen prevention server 130 is configured to request the recommendation server to pull the general data, and push the general data to the application server for caching;
the application server 110 is configured to request the recommendation server to pull the personalized data, and push the cached general data to the front end that requests the personalized data to display when the personalized data is not pulled.
It should be noted that, for the functions and implementation processes of the recommendation server 120, the white screen prevention server 130 and the application server 110, reference may be made to the embodiment of the front-end display control method.
Fig. 12 is a flowchart of the execution steps of the recommendation server 120 in the front-end display control system, and as shown in fig. 12, the execution steps of the recommendation server 120 include:
in step 1401, receiving a data recall request containing a random user account;
in step 1402, responding to the data recall request, calculating the hotness ranking of all the information data;
in step 1403, according to the information data with higher popularity, generating general data corresponding to the random user account;
in step 1404, the generic data is returned to the white screen prevention server 130 in batches.
Fig. 13 is a flowchart of the execution steps of the white screen prevention server 130 in the front-end display control system, and as shown in fig. 13, the execution steps of the white screen prevention server 130 include:
in step 1501, a random user account is generated periodically and a data recall request is sent to the recommendation server 120;
in step 1502, general data returned by the recommendation server 120 is received;
in step 1503, the general data is written into the redis, and the general data in the redis is pushed to the application server 110 for caching at regular time.
Fig. 14 is a flowchart of the execution steps of the application server 110 in the front-end display control system, and as shown in fig. 14, the execution steps of the application server 110 include:
in step 1601, receiving and caching general data regularly pushed by the white screen prevention server 130;
in step 1602, when pulling the personalized data from the recommendation server 120 fails, an obtaining request of the cached general data is initiated;
in step 1603, if the cached data is expired or invalid, a request is made for pulling the general data from the white screen prevention server 130; and if the request fails, pushing expired or invalid data to the front end for display, and preventing the front end home page from displaying a white screen.
In step 1604, if the cached data is not expired or invalid, the specified page of the cached general data is obtained according to the page turning information of the front end, and is pushed to the front end for displaying.
In one embodiment, the first page white screen prevention policy of personalized APP is as shown in table 1 below.
TABLE 1 white screen prevention strategy
The service logic background can be regarded as the application server 110 and the white screen prevention server 130 in the front-end display control system, and the recommendation side can be regarded as the recommendation server 120 in the front-end display control system.
As can be seen from table 1, for the behavior of returning personalized data from the recommending side, whether the business logic background needs to be supplemented with non-personalized data (i.e. general data) includes the following situations:
(1) and the recommending side correctly returns data and the data is sufficient, namely the display content of one screen is met, the data does not need to be supplemented, and the data of the recommending side can meet the requirement.
(2) The recommendation side returns data, but the data is not enough for one screen, and general data is needed to be used for filling, so that one screen of data is achieved.
(3) The recommending side returns null data, and does not need to supplement, because the data of the user is already null, the user can be prompted, and no new short video exists temporarily.
(4) And if the request fails to recommend, data needs to be supplemented, and the universal data is used for preventing white screen from being trapped at the bottom.
2. Whether the non-personalized data (i.e. the general data) needs to be used for supplementation according to whether the non-personalized pool has data (i.e. whether the general data is cached) or not comprises the following conditions:
(1) the data is returned correctly and is sufficient, and no matter whether the non-personalized pool has the data or not, the data does not need to be supplemented.
(2) And recommending that returned data is insufficient, supplementing data to achieve one-screen output when the non-personalized pool has data, and performing null prompt when the non-personalized pool does not have data.
(3) When the feedback of the null data is recommended, no matter whether the non-personalized pool has the data or not, the null prompt is carried out without supplementing the data.
(4) And when the recommendation is wrong or fails, if the non-personalized pool has data, returning from front to back according to the page turning information and the data of the pool until the non-personalized pool has no redundant data, and returning to the empty prompt. And if the non-personalized pool has no data, directly returning to the empty prompt.
The following is an embodiment of the apparatus of the present invention, which can be used to execute an embodiment of the control method of the front-end display executed by the above-mentioned server of the present invention. For details not disclosed in the embodiment of the control device of the front end display of the present invention, please refer to the embodiment of the control method of the front end display of the present invention.
Fig. 15 is a block diagram of a front-end display control device according to an exemplary embodiment, which may be used in a server in the implementation environment shown in fig. 1 to execute all or part of the steps of the front-end display control method shown in any one of fig. 3, 6 to 9. As shown in fig. 15, the apparatus includes, but is not limited to: a general data request module 1710, a general data acquisition module 1730, and a general data push module 1750.
A universal data request module 1710, configured to request to acquire pre-cached universal data when pulling personalized data from a recommendation side fails;
a general data obtaining module 1730, configured to obtain the general data according to the request, where the general data is data that is pulled and cached in advance from the recommending side;
and the universal data pushing module 1750 is used for pushing the acquired universal data to the front end which requests the personalized data to display.
The implementation processes of the functions and actions of each module in the device are specifically detailed in the implementation processes of the corresponding steps in the control method for front-end display, and are not described again here.
The universal data request module 1710 can be, for example, one of the physical configurations of the wired or wireless network interface 250 of fig. 2.
The general data obtaining module 1730 and the general data pushing module 1750 may also be functional modules, configured to execute corresponding steps in the control method for front-end display. It is understood that these modules may be implemented in hardware, software, or a combination of both. When implemented in hardware, these modules may be implemented as one or more hardware modules, such as one or more application specific integrated circuits. When implemented in software, the modules may be implemented as one or more computer programs executing on one or more processors, such as programs stored in memory 232 for execution by central processor 222 of FIG. 2.
In an exemplary embodiment, as shown in fig. 16, the control device of the front-end display further includes but is not limited to:
a general data pulling module 1810, configured to pull the general data on the recommendation side by generating a random user account to obtain general data corresponding to the random user account;
a universal data caching module 1830, configured to perform universal data caching corresponding to the random user account.
In an exemplary embodiment, as shown in fig. 17, the general data pulling module 1810 specifically includes but is not limited to:
a random account generation unit 1811, configured to generate a random user account at regular time;
a general data recall unit 1812, configured to request, by using the random user account, to recall data on the recommendation side, and trigger the recommendation side to extract general data corresponding to the random user account from all information data according to the current information popularity;
a general data pulling unit 1813, configured to pull the general data from the recommending side, and feed back the identification information of the pulled data to the recommending side, so that the recommending side is pulled next time to return general data with different identification information.
In an exemplary embodiment, as shown in FIG. 18, the generic data request module 1710 includes, but is not limited to:
a personalized data request unit 1711, configured to request, according to a request for obtaining personalized data of a specified user, which is sent by a front end for display, to pull the personalized data of the specified user from the recommending side;
a general data requesting unit 1712, configured to initiate an obtaining request for general data cached in the shared memory when the personalized data is failed to be pulled.
In an exemplary embodiment, as shown in FIG. 19, the generic data push module 1750 includes, but is not limited to:
a data supplementing unit 1751, configured to, when the personalized data pulled from the recommending side is less than one page, supplement the personalized data with the acquired general data to form full-screen display data including the personalized data and the general data;
and the data pushing unit 1752 is used for pushing the full-screen display data containing the personalized data and the general data to the front end for displaying.
Fig. 20 is a block diagram illustrating a data recommendation control apparatus, which may be used in the recommendation server 120 of the implementation environment shown in fig. 1, to perform all or part of the steps of the data recommendation control method shown in fig. 10, according to another exemplary embodiment. As shown in fig. 20, the control device for data recommendation includes but is not limited to: recall request receiving module 2210, general data generating module 2230, and general data returning module 2250.
A recall request receiving module 2210, configured to receive a data recall request, where the data recall request carries a random user account;
a general data generating module 2230, configured to generate general data corresponding to the random user account in response to the data recall request;
a general data return module 2250, configured to return the generated general data for the data recall request; the general data is used for displaying the general data at the front end when the personalized data returned to the front end fails.
The implementation processes of the functions and actions of each module in the device are specifically described in the implementation processes of the corresponding steps in the data recommendation control method, and are not described herein again.
Optionally, the general data generating module 2230 includes, but is not limited to:
and the general data extraction unit is used for extracting data from all information data according to the current information heat degree to obtain general data corresponding to the random user account.
Optionally, the general data return module 2250 includes, but is not limited to:
and the batch returning unit is used for successively returning the general data for the data recall request and successively returning the general data with different identification information according to the identification information of the returned general data each time.
Optionally, the present invention further provides an electronic device, which may be used in the server in the implementation environment shown in fig. 1 to execute all or part of the steps of the front-end display control method shown in any one of fig. 3, 6 to 9. The electronic device includes:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to execute the control method of the front end display described in the above exemplary embodiment.
The specific manner in which the processor of the electronic device performs operations in this embodiment has been described in detail in the embodiment related to the control method of the front-end display, and will not be elaborated here.
Optionally, the present invention further provides another electronic device, which can be used in the recommendation server 120 in the implementation environment shown in fig. 1 to execute all or part of the steps of the control method for data recommendation shown in fig. 10. The electronic device includes:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to execute the control method of data recommendation described in the above exemplary embodiment.
The specific manner in which the processor of the electronic device performs operations in this embodiment has been described in detail in the embodiment of the control method related to the data recommendation, and will not be elaborated upon here.
In an exemplary embodiment, a storage medium is also provided that is a computer-readable storage medium, such as may be transitory and non-transitory computer-readable storage media, including instructions. The storage medium stores a computer program executable by the central processor 222 of the server 200 to perform the control method of the front-end display or the control method of the data recommendation.
It will be understood that the invention is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the invention is limited only by the appended claims.
Claims (13)
1. A method for controlling a front-end display, the method comprising:
pulling general data from a recommendation side, and feeding back identification information of the pulled data to the recommendation side so as to enable the recommendation side to return general data with different identification information in the next pulling;
according to a personalized data acquisition request of a specified user sent by a front end for display, requesting to pull the personalized data of the specified user from the recommending side;
when the personalized data is not pulled from the recommending side, requesting to acquire the general data cached in advance;
acquiring the general data according to the request;
when the personalized data pulled from the recommending side is less than one page, supplementing the personalized data by the acquired general data to form full-screen display data containing the personalized data and the general data;
and pushing the full-screen display data containing the personalized data and the general data to a front end for display.
2. The method according to claim 1, wherein the pulling of the general data from the recommendation side comprises:
pulling the general data at the recommendation side by generating a random user account to obtain the general data corresponding to the random user account;
and caching the general data corresponding to the random user account.
3. The method according to claim 2, wherein the obtaining general data corresponding to a random user account by generating the random user account to pull the general data on the recommendation side comprises:
generating a random user account at regular time;
and requesting the recommendation side to recall data through the random user account, and triggering the recommendation side to extract general data corresponding to the random user account from all information data according to the current information popularity.
4. The method of claim 1, wherein the requesting to obtain pre-cached general data when pulling personalized data from the recommending side fails comprises:
and when the personalized data is failed to be pulled, initiating an acquisition request for the general data cached in the shared memory.
5. A method for controlling data recommendation, the method comprising:
receiving a data recall request, wherein the data recall request carries a random user account;
responding to the data recall request, and generating general data corresponding to the random user account;
returning the generated general data for the data recall request, and successively returning general data with different identification information according to the identification information of the returned general data each time; the general data is used for forming full-screen display data containing the personalized data and the general data when the personalized data pulled from the recommendation side is less than one page, and the full-screen display data is used for displaying at the front end.
6. The method of claim 5, wherein generating generic data corresponding to the random user account in response to the data recall request comprises:
and extracting data from all information data according to the current information heat to obtain general data corresponding to the random user account.
7. A control system for a front-end display, the system comprising: the system comprises a recommendation server, an application server and a white screen prevention server;
the recommendation server is used for responding to the request sent by the application server to return personalized data and responding to the request sent by the white screen prevention server to return general data, and successively returns general data with different identification information according to the identification information of the general data returned each time;
the white screen prevention server is used for requesting the recommendation server to pull the general data and pushing the general data to the application server for caching;
the application server is used for requesting the recommendation server to pull the personalized data, forming the cached general data into full-screen display data containing the personalized data and the general data when the personalized data pulled from the recommendation server is less than one page, and pushing the full-screen display data to the front end requesting the personalized data for displaying.
8. A control apparatus for a front-end display, the apparatus comprising:
the general data pulling module is used for pulling general data from a recommending side and feeding back the identification information of the pulled data to the recommending side so as to enable the recommending side to return general data with different identification information in the next pulling;
the general data request module is used for requesting to acquire the general data cached in advance when the personalized data pulling from the recommending side fails;
a general data acquisition module, configured to acquire the general data according to the request, where the general data is data that is pulled and cached in advance from the recommendation side;
the data supplement unit is used for supplementing the personalized data through the acquired general data when the personalized data pulled from the recommendation side is less than one page, and forming full-screen display data containing the personalized data and the general data;
and the general data pushing module is used for pushing the full-screen display data containing the personalized data and the general data to the front end for display.
9. The apparatus of claim 8, further comprising:
and the universal data caching module is used for caching the universal data corresponding to the random user account.
10. The apparatus of claim 8, wherein the generic data pull module comprises:
the random account generation unit is used for generating a random user account at regular time;
and the general data recall unit is used for requesting the recommendation side to recall data through the random user account and triggering the recommendation side to extract general data corresponding to the random user account from all information data according to the current information popularity.
11. A control apparatus for data recommendation, the apparatus comprising:
the device comprises a recall request receiving module, a recall request processing module and a recall processing module, wherein the recall request receiving module is used for receiving a data recall request which carries a random user account;
the universal data generating module is used for responding to the data recall request, generating universal data corresponding to the random user account, and successively returning the universal data with different identification information according to the identification information of the returned universal data each time;
the general data return module is used for returning the generated general data for the data recall request; the general data is used for forming full-screen display data containing the personalized data and the general data when the personalized data pulled from the recommending side is less than one page, and the full-screen display data is used for displaying at the front end.
12. An electronic device, characterized in that the electronic device comprises:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to execute the control method of the front end display of any one of claims 1 to 4 or the control method of the data recommendation of any one of claims 5 to 6.
13. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program executable by a processor to perform the control method of the front-end display according to any one of claims 1 to 4 or the control method of the data recommendation according to any one of claims 5 to 6.
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| CN111488521A (en) * | 2020-04-07 | 2020-08-04 | 支付宝(杭州)信息技术有限公司 | Data processing method and device |
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