CN112084400A - Mobile internet user management method, device and system - Google Patents
Mobile internet user management method, device and system Download PDFInfo
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Abstract
The invention discloses a mobile internet user management method, a device and a system, which are characterized in that after user historical use data are received, use service item information of the user historical use data is extracted, a plurality of clustering label data are determined, behavior characteristic analysis is carried out on the clustering label data and each set label classifying behavior in a set of set label classifying behaviors to obtain a behavior characteristic analysis result, then a label classifying behavior component corresponding to target clustering label data meeting preset conditions determines a behavior characteristic directed space of the user historical use data, and finally a user portrait of mobile user equipment is generated to obtain a generation result based on the user historical use data and the determined behavior characteristic directed space of the user historical use data. Therefore, the user portrait in the mobile internet user management process is effectively identified, so that information configuration can be conveniently carried out on the user push service of the internet application service in a subsequent targeted manner, and the information push pertinence is improved.
Description
Technical Field
The invention relates to the technical field of big data, in particular to a mobile internet user management method, device and system.
Background
In the process that the mobile user equipment seeks the user push service through the internet application service, a lot of user historical use data can be generated, and the data can generally represent the reference basis of the push service of the user, so that the user portrait in the mobile internet user management process needs to be effectively identified, information configuration can be conveniently carried out on the user push service of the internet application service in a follow-up targeted mode, and the information push pertinence is improved.
Disclosure of Invention
In order to solve the technical problems in the related art, the invention provides a mobile internet user management method, a device and a system.
A mobile internet user management method applied to a server communicating with a mobile user equipment, the method comprising:
after receiving user historical use data sent by the mobile user equipment through internet application service in real time, extracting use service item information of the user historical use data, and clustering the user historical use data based on the use service item information to obtain a plurality of clustering label data; the service item information is service response information formed by program service nodes related to application program services in the user historical use data;
performing behavior characteristic analysis on each group of clustering label data of the user historical use data and each set label classification behavior in a set of set label classification behaviors; the set label classification behavior set stores a plurality of set label classification behaviors and behavior feature information corresponding to each set of set label classification behaviors, and the plurality of set label classification behaviors are label statistical behavior information sets of a service authority interval in which the application program service is located; if each group of cluster label data of the extracted user historical use data comprises a plurality of behavior feature information, performing behavior feature analysis in the following mode: performing multi-dimensional behavior feature analysis according to a behavior feature analysis program configured for each behavior feature information in advance; in each level of behavior feature analysis, performing behavior feature analysis only based on one behavior feature information in each group of clustering label data, and inputting clustering label data meeting preset conditions into a next level of behavior feature analysis program, so that the behavior feature analysis is performed based on the next behavior feature information, and the clustering label data is input into the next level of behavior feature analysis program, so that the behavior feature analysis is performed based on the next behavior feature information;
determining a behavior characteristic directed space of the historical user data according to a label classification behavior component corresponding to target clustering label data of which the behavior characteristic analysis result of the historical user data meets a preset condition;
generating a user portrait of the mobile user equipment based on the historical user use data and the determined behavior characteristic directed space of the historical user use data to obtain a generation result, and configuring information of a user push service of the internet application service according to the generation result.
In one example, extracting usage service item information of the user historical usage data, and clustering the user historical usage data based on the usage service item information to obtain a plurality of cluster label data includes:
listing historical response data segments corresponding to the historical user data, determining service response information of each historical response data segment, and sorting the historical response data segments according to preset response service serial numbers of the service response information to obtain the service item information;
acquiring a project service set of the service project information and a plurality of historical use service sets of the historical use data of the user; under the condition that the portrait matching feature exists in the user pushing service of the historical user data is determined according to the project service set, determining the service matching feature between each historical use service set of the historical user data under the target portrait matching feature and each historical use service set of the historical user data under the portrait matching feature according to the historical use service set of the historical user data under the portrait matching feature and the service calling interface information of the historical use service set of the historical user data under the portrait matching feature; adjusting the historical use service set of the user historical use data under the target portrait matching feature and the historical use service set under the portrait matching feature, wherein the historical use service set has the lowest service matching feature priority;
under the condition that a plurality of historical use service sets are contained in the target portrait matching feature corresponding to the historical use data of the user, determining a service matching feature among the historical use service sets of the historical use data of the user under the target portrait matching feature according to the historical use service sets of the historical use data under the portrait matching feature and service calling interface information of the historical use service sets, and fusing the historical use service sets under the target portrait matching feature based on the service matching feature among the historical use service sets; setting an updating thread for the target historical use service set obtained by fusion according to the historical use service set of the user historical use data under the portrait matching characteristic and the service calling interface information thereof, and adjusting the target historical use service set to a set portrait matching node of the portrait matching characteristic corresponding to the updating thread;
and clustering the historical service use set under the target portrait matching characteristic based on the service grading information of the target project service with the label classification service corresponding to the project service set in the service use project information to obtain a plurality of clustered label data.
In one example, determining a behavior feature directed space of the historical user data according to a tag classification behavior component corresponding to target clustering tag data whose behavior feature analysis result of the historical user data meets a preset condition includes:
extracting response parameters of the behavior feature analysis results, and determining target clustering label data corresponding to the response parameters when the response parameters meet preset behavior feature analysis parameters of the mobile user equipment;
extracting label logic information of the target clustering label data according to set service logic characters;
generating a label logic directed space corresponding to the label logic information and a label classification behavior directed space corresponding to target clustering label data, wherein the label logic directed space and the label classification behavior directed space respectively comprise a plurality of directed nodes of different label classification behaviors;
extracting service response information of the label logic information at one directed node in the label logic directed space, and determining the directed node with the shortest label classification behavior in the label classification behavior directed space as a candidate directed node;
configuring the business service response information into the candidate directed nodes to obtain response transferring interface information in the candidate directed nodes, and then generating logic combination information between the label logic information and the target clustering label data based on the business service response information and the response transferring interface information;
acquiring behavior feature updating directed distribution in the candidate directed nodes by taking the response mobilizing interface information as an interface protocol parameter, configuring the behavior feature updating directed distribution to the directed nodes where the service response information is located according to a logic merging unit corresponding to the logic merging information, and obtaining target behavior feature distribution information corresponding to the behavior feature updating directed distribution in the directed nodes where the service response information is located;
and listing the behavior characteristic distribution nodes and the distribution associated information in the target behavior characteristic distribution information, and generating a behavior characteristic directed space of the user historical use data according to the behavior characteristic distribution nodes and the distribution associated information.
In one example, generating a user representation of the mobile user equipment based on the historical user usage data and a determined behavioral characteristic directed space of the historical user usage data to obtain a generation result, and performing information configuration on a user push service of the internet application service according to the generation result, includes:
determining user portrait feature information corresponding to the user portrait, which is determined based on the user historical usage data and the behavior feature directed space, and performing depth extraction on the user portrait by adopting the user portrait feature information to obtain target user portrait feature information;
aiming at the current user portrait feature information in the target user portrait feature information, determining a user push service control set of the current user portrait feature information in a preset portrait service interval based on a first user push service control of the current user portrait feature information in the preset portrait service interval and second user push service controls of each target user portrait feature information in the preset portrait service interval;
determining target push marking information of the current user portrait characteristic information between two adjacent set time periods according to a user push service control set of the current user portrait characteristic information in two adjacent preset portrait service intervals, generating the user portrait of the mobile user equipment according to the target push marking information to obtain a generation result, and performing information configuration on a user push service of the internet application service according to the generation result.
A mobile internet user management apparatus for use with a server in communication with a mobile user device, the apparatus comprising:
the clustering module is used for extracting service item information of the historical user use data after receiving the historical user use data sent by the mobile user equipment through internet application service in real time, and clustering the historical user use data based on the service item information to obtain a plurality of clustering label data; the service item information is service response information formed by program service nodes related to application program services in the user historical use data;
the matching module is used for carrying out behavior characteristic analysis on each group of clustering label data of the historical user data and each set label classifying behavior in a set of set label classifying behaviors; the set label classification behavior set stores a plurality of set label classification behaviors and behavior feature information corresponding to each set of set label classification behaviors, and the plurality of set label classification behaviors are label statistical behavior information sets of a service authority interval in which the application program service is located; if each group of cluster label data of the extracted user historical use data comprises a plurality of behavior feature information, performing behavior feature analysis in the following mode: performing multi-dimensional behavior feature analysis according to a behavior feature analysis program configured for each behavior feature information in advance; in each level of behavior feature analysis, performing behavior feature analysis only based on one behavior feature information in each group of clustering label data, and inputting clustering label data meeting preset conditions into a next level of behavior feature analysis program, so that the behavior feature analysis is performed based on the next behavior feature information, and the clustering label data is input into the next level of behavior feature analysis program, so that the behavior feature analysis is performed based on the next behavior feature information;
the determining module is used for determining a behavior characteristic directed space of the historical user data according to a label classification behavior component corresponding to target clustering label data of which the behavior characteristic analysis result of the historical user data meets a preset condition;
and the generating module is used for generating a user portrait of the mobile user equipment based on the historical user data and the determined behavior characteristic directed space of the historical user data to obtain a generating result, and performing information configuration on a user push service of the internet application service according to the generating result.
A server comprising a processor and a memory in communication with each other, the processor being configured to retrieve a computer program from the memory and execute the computer program to perform the method described above.
A computer-readable storage medium, on which a computer program is stored which, when executed, implements the above-described method.
The technical scheme provided by the embodiment of the invention comprises the steps of firstly extracting the service item information of the historical use data of the user after receiving the historical use data of the user and determining a plurality of clustering label data, secondly, performing behavior characteristic analysis on each group of clustering label data of the historical user data and each set label classifying behavior in the set of set label classifying behaviors to obtain a behavior characteristic analysis result, then determining a behavior characteristic directed space of the historical user data according to a label classification behavior component corresponding to the target clustering label data of which the behavior characteristic analysis result meets a preset condition, and finally generating a user portrait of the mobile user equipment based on the historical user data and the determined behavior characteristic directed space of the historical user data to obtain a generation result, and configuring information of the user push service of the internet application service according to the generation result. Therefore, the user portrait in the mobile internet user management process is effectively identified, so that information configuration can be conveniently carried out on the user push service of the internet application service in a subsequent targeted manner, and the information push pertinence 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 view of a mobile internet subscriber management system according to an embodiment of the present invention.
Fig. 2 is a flowchart of a method for managing a mobile internet user according to an embodiment of the present invention.
Fig. 3 is a block diagram of a mobile internet subscriber management apparatus according to an embodiment of the present invention.
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 application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.
In order to solve the technical problems of poor accuracy and reliability of a method for managing mobile internet users of mobile user equipment in the prior art, the embodiment of the invention provides a method, a device and a system for managing the mobile internet users.
Referring first to fig. 1, which is a block diagram illustrating a mobile internet subscriber management system 10 according to an embodiment of the present invention, the mobile internet subscriber management system 10 may include a server 100 and a mobile user equipment 200, wherein the server is in communication with the mobile user equipment 200.
Referring to fig. 2, a flowchart of a mobile internet user management method according to an embodiment of the present invention is shown, where the mobile internet user management method may be applied to the server 100 in fig. 1, and specifically may include the program service node described in the following steps.
Step S110, after receiving the historical user use data sent by the mobile user equipment through the Internet application service in real time, extracting the use service item information of the historical user use data, and clustering the historical user use data based on the use service item information to obtain a plurality of clustering label data. The service item information is a set formed by program service nodes related to application program service in the user historical use data.
And step S120, performing behavior characteristic analysis on each group of clustering label data of the historical user data and each set label classification behavior in the set of set label classification behaviors. The set label classification behavior set stores a plurality of set label classification behaviors and behavior characteristic information corresponding to each set of set label classification behaviors, and the plurality of set label classification behaviors are label statistical behavior information sets of a service authority interval in which the application program service is located. If each group of cluster label data of the extracted user historical use data comprises a plurality of behavior feature information, performing behavior feature analysis in the following mode: and performing multi-dimensional behavior feature analysis according to a behavior feature analysis program configured for each behavior feature information in advance. In each level of behavior feature analysis, behavior feature analysis is performed only based on one behavior feature information in each group of cluster label data, and the cluster label data meeting preset conditions are input into a next level of behavior feature analysis program, so that the cluster label data are subjected to the behavior feature analysis based on the next behavior feature information and are input into the next level of behavior feature analysis program, and the behavior feature analysis is performed based on the next behavior feature information.
Step S130, determining a behavior characteristic directed space of the historical user data according to a label classification behavior component corresponding to the target clustering label data of which the behavior characteristic analysis result of the historical user data meets the preset condition.
And step S140, generating a user portrait of the mobile user equipment based on the historical user data and the determined behavior characteristic directed space of the historical user data to obtain a generation result, and configuring information of a user push service of the Internet application service according to the generation result.
When the method described in the above-mentioned steps S110 to S140 is applied, first, after receiving the user history usage data, the usage service item information of the user history usage data is extracted and a plurality of cluster label data is determined, secondly, performing behavior characteristic analysis on each group of clustering label data of the historical user data and each set label classifying behavior in the set of set label classifying behaviors to obtain a behavior characteristic analysis result, and finally, generating a user portrait of the mobile user equipment based on the user historical use data and the determined behavior characteristic directed space of the user historical use data to obtain a generation result, and performing information configuration on a user push service of the internet application service according to the generation result. Therefore, the user portrait in the mobile internet user management process is effectively identified, so that information configuration can be conveniently carried out on the user push service of the internet application service in a subsequent targeted manner, and the information push pertinence is improved.
In an alternative embodiment, in order to accurately perform clustering processing on the user historical usage data, in step S110, usage service item information of the user historical usage data is extracted, and the user historical usage data is clustered based on the usage service item information to obtain a plurality of clustering label data, which may specifically include the contents described in the following steps.
And step S111, listing the historical response data segments corresponding to the historical use data of the user, determining the service response information of each historical response data segment, and sorting the historical response data segments according to the preset serial number of the response service of the service response information to obtain the information of the service use item.
In step S112, a project service set using the service project information and a plurality of historical usage service sets of the user historical usage data are acquired. Under the condition that the portrait matching feature exists in the user push service of the historical use data of the user is determined according to the project service set, the service matching feature between each historical use service set of the historical use data of the user under the target portrait matching feature and each historical use service set of the historical use data of the user under the portrait matching feature is determined according to the historical use service set of the historical use data of the user under the portrait matching feature and the service calling interface information of the historical use service set. And adjusting the historical use service set with the lowest service matching feature priority of the historical use data of the user under the target portrait matching feature and the historical use service set under the portrait matching feature to the portrait matching feature.
Step S113, under the condition that a plurality of historical use service sets are included under the target portrait matching feature corresponding to the historical use data of the user, determining the service matching feature among the historical use service sets of the historical use data under the target portrait matching feature according to the historical use service sets of the historical use data under the portrait matching feature and the service calling interface information thereof, and fusing the historical use service sets under the target portrait matching feature based on the service matching feature among the historical use service sets. And setting an updating thread for the target historical use service set obtained by fusion according to the historical use service set of the user historical use data under the portrait matching characteristic and the service calling interface information thereof, and adjusting the target historical use service set to a set portrait matching node of the portrait matching characteristic corresponding to the updating thread.
And step S114, clustering the historical use service set under the matching characteristic of the target portrait based on the service grading information of the target project service with the label classification service corresponding to the project service set in the use service project information to obtain a plurality of clustering label data.
It can be understood that, through the above steps S111 to S114, the target image matching feature of the historical usage data of the user and the historical usage service set under the image matching feature can be adjusted and updated, so that the historical usage service set under the target image matching feature can be clustered based on the service ranking information of the target item service of the label classification service corresponding to the item service set in the usage service item information to obtain a plurality of cluster label data, thereby accurately clustering the historical usage data of the user.
In an alternative embodiment, in order to ensure the continuity of the behavior feature directed space and avoid the behavior feature directed space from generating faults, in step S130, the behavior feature directed space of the historical usage data of the user is determined according to the label classification behavior component corresponding to the target cluster label data whose behavior feature analysis result meets the preset condition, which may specifically include the content described in the following steps.
Step S131, extracting response parameters of the behavior feature analysis result, and determining target cluster label data corresponding to the response parameters when the response parameters meet preset behavior feature analysis parameters of the mobile user equipment.
And step S132, extracting the label logic information of the target clustering label data according to the set service logic characters.
Step S133, generating a label logic directed space corresponding to the label logic information and a label classification behavior directed space corresponding to the target clustering label data, where the label logic directed space and the label classification behavior directed space respectively include a plurality of directed nodes of different label classification behaviors.
Step S134, extracting the service response information of the label logic information in one directed node in the label logic directed space, and determining the directed node with the shortest label classification behavior in the label classification behavior directed space as a candidate directed node.
Step S135, configuring the service response information into the candidate directed node to obtain response mobilizing interface information in the candidate directed node, and then generating logic merging information between the tag logic information and the target clustered tag data based on the service response information and the response mobilizing interface information.
Step S136, behavior characteristic updating directed distribution is obtained in the candidate directed nodes by taking the response mobilizing interface information as an interface protocol parameter, the behavior characteristic updating directed distribution is configured to the directed node where the service response information is located according to the logic merging unit corresponding to the logic merging information, and target behavior characteristic distribution information corresponding to the behavior characteristic updating directed distribution is obtained in the directed node where the service response information is located.
And step S137, listing the behavior characteristic distribution nodes and the distribution associated information in the target behavior characteristic distribution information, and generating a behavior characteristic directed space of the user historical use data according to the behavior characteristic distribution nodes and the distribution associated information.
It can be understood that, through the contents described in the above steps S131 to S137, continuity of the behavior feature directed space can be ensured, and a fault in the behavior feature directed space can be avoided.
In a specific implementation, in order to ensure the accuracy and reliability of the generated result, in step S140, a user profile of the mobile user equipment is generated based on the historical user data and the determined behavioral characteristic directed space of the historical user data, so as to obtain the generated result, and information configuration is performed on a user push service of the internet application service according to the generated result, which may specifically include the contents described in the following steps.
Step S141, user portrait feature information corresponding to the user portrait determined based on the user historical use data and the behavior feature directed space is determined, and the user portrait feature information is adopted to conduct depth extraction on the user portrait to obtain target user portrait feature information.
Step S142, aiming at the current user portrait characteristic information in the target user portrait characteristic information, based on a first user push service control of the current user portrait characteristic information in a preset portrait service interval and a second user push service control of each target user portrait characteristic information in the preset portrait service interval, determining a user push service control set of the current user portrait characteristic information in the preset portrait service interval.
Step S143, determining target push marking information of the current user portrait characteristic information between two adjacent set time periods according to the user push service control assembly of the current user portrait characteristic information in two adjacent preset portrait service intervals, generating the user portrait of the mobile user equipment according to the target push marking information to obtain a generation result, and configuring information of a user push service of the Internet application service according to the generation result.
Through the contents described in the above steps S141 to S143, the accuracy and reliability of the generated result can be ensured.
Referring to fig. 3, there is provided a mobile internet user management apparatus 210 for a server communicating with a mobile user equipment, the apparatus 210 comprising:
a clustering module 211, configured to, after receiving historical user usage data sent by the mobile user equipment through an internet application service in real time, extract usage service item information of the historical user usage data, and perform clustering processing on the historical user usage data based on the usage service item information to obtain multiple clustering tag data; the service item information is a set formed by program service nodes related to application program services in the user historical use data;
a matching module 212, configured to perform behavior feature analysis on each group of clustering label data of the historical user data and each set label classification behavior in a set of set label classification behaviors; the set label classification behavior set stores a plurality of set label classification behaviors and behavior feature information corresponding to each set of set label classification behaviors, and the plurality of set label classification behaviors are label statistical behavior information sets of a service authority interval in which the application program service is located; if each group of cluster label data of the extracted user historical use data comprises a plurality of behavior feature information, performing behavior feature analysis in the following mode: performing multi-dimensional behavior feature analysis according to a behavior feature analysis program configured for each behavior feature information in advance; in each level of behavior feature analysis, performing behavior feature analysis only based on one behavior feature information in each group of clustering label data, and inputting clustering label data meeting preset conditions into a next level of behavior feature analysis program, so that the behavior feature analysis is performed based on the next behavior feature information, and the clustering label data is input into the next level of behavior feature analysis program, so that the behavior feature analysis is performed based on the next behavior feature information;
a determining module 213, configured to determine a behavior feature directed space of the historical user data according to a tag classification behavior component corresponding to target clustering tag data whose behavior feature analysis result of the historical user data meets a preset condition;
a generating module 214, configured to generate a user portrait of the mobile user equipment based on the historical user data and the determined behavioral characteristic directed space of the historical user data to obtain a generation result, and perform information configuration on a user push service of the internet application service according to the generation result.
In a possible implementation manner, the cluster processing module 211 is configured to:
listing historical response data segments corresponding to the historical user data, determining service response information of each historical response data segment, and sorting the historical response data segments according to preset response service serial numbers of the service response information to obtain the service item information;
acquiring a project service set of the service project information and a plurality of historical use service sets of the historical use data of the user; under the condition that the portrait matching feature exists in the user pushing service of the historical user data is determined according to the project service set, determining the service matching feature between each historical use service set of the historical user data under the target portrait matching feature and each historical use service set of the historical user data under the portrait matching feature according to the historical use service set of the historical user data under the portrait matching feature and the service calling interface information of the historical use service set of the historical user data under the portrait matching feature; adjusting the historical use service set of the user historical use data under the target portrait matching feature and the historical use service set under the portrait matching feature, wherein the historical use service set has the lowest service matching feature priority;
under the condition that a plurality of historical use service sets are contained in the target portrait matching feature corresponding to the historical use data of the user, determining a service matching feature among the historical use service sets of the historical use data of the user under the target portrait matching feature according to the historical use service sets of the historical use data under the portrait matching feature and service calling interface information of the historical use service sets, and fusing the historical use service sets under the target portrait matching feature based on the service matching feature among the historical use service sets; setting an updating thread for the target historical use service set obtained by fusion according to the historical use service set of the user historical use data under the portrait matching characteristic and the service calling interface information thereof, and adjusting the target historical use service set to a set portrait matching node of the portrait matching characteristic corresponding to the updating thread;
and clustering the historical service use set under the target portrait matching characteristic based on the service grading information of the target project service with the label classification service corresponding to the project service set in the service use project information to obtain a plurality of clustered label data.
In a possible implementation manner, the determining module 213 is configured to:
extracting response parameters of the behavior feature analysis results, and determining target clustering label data corresponding to the response parameters when the response parameters meet preset behavior feature analysis parameters of the mobile user equipment;
extracting label logic information of the target clustering label data according to set service logic characters;
generating a label logic directed space corresponding to the label logic information and a label classification behavior directed space corresponding to target clustering label data, wherein the label logic directed space and the label classification behavior directed space respectively comprise a plurality of directed nodes of different label classification behaviors;
extracting service response information of the label logic information at one directed node in the label logic directed space, and determining the directed node with the shortest label classification behavior in the label classification behavior directed space as a candidate directed node;
configuring the business service response information into the candidate directed nodes to obtain response transferring interface information in the candidate directed nodes, and then generating logic combination information between the label logic information and the target clustering label data based on the business service response information and the response transferring interface information;
acquiring behavior feature updating directed distribution in the candidate directed nodes by taking the response mobilizing interface information as an interface protocol parameter, configuring the behavior feature updating directed distribution to the directed nodes where the service response information is located according to a logic merging unit corresponding to the logic merging information, and obtaining target behavior feature distribution information corresponding to the behavior feature updating directed distribution in the directed nodes where the service response information is located;
and listing the behavior characteristic distribution nodes and the distribution associated information in the target behavior characteristic distribution information, and generating a behavior characteristic directed space of the user historical use data according to the behavior characteristic distribution nodes and the distribution associated information.
In a possible implementation manner, the generating module 214 is configured to:
determining user portrait feature information corresponding to the user portrait, which is determined based on the user historical usage data and the behavior feature directed space, and performing depth extraction on the user portrait by adopting the user portrait feature information to obtain target user portrait feature information;
aiming at the current user portrait feature information in the target user portrait feature information, determining a user push service control set of the current user portrait feature information in a preset portrait service interval based on a first user push service control of the current user portrait feature information in the preset portrait service interval and second user push service controls of each target user portrait feature information in the preset portrait service interval;
determining target push marking information of the current user portrait characteristic information between two adjacent set time periods according to a user push service control set of the current user portrait characteristic information in two adjacent preset portrait service intervals, generating the user portrait of the mobile user equipment according to the target push marking information to obtain a generation result, and performing information configuration on a user push service of the internet application service according to the generation result.
On the basis of the above, there is also provided a server, comprising a processor and a memory, which are in communication with each other, the processor being configured to retrieve a computer program from the memory, and to execute the computer program to implement the above-mentioned method.
Further, a computer-readable storage medium is provided, on which a computer program is stored, which computer program realizes the above-mentioned method when executed.
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 (10)
1. A mobile internet user management method applied to a server communicating with a mobile user equipment, the method comprising:
after receiving user historical use data sent by the mobile user equipment through internet application service in real time, extracting use service item information of the user historical use data, and clustering the user historical use data based on the use service item information to obtain a plurality of clustering label data; the service item information is service response information formed by program service nodes related to application program services in the user historical use data;
performing behavior characteristic analysis on each group of clustering label data of the user historical use data and each set label classification behavior in a set of set label classification behaviors; the set label classification behavior set stores a plurality of set label classification behaviors and behavior feature information corresponding to each set of set label classification behaviors, and the plurality of set label classification behaviors are label statistical behavior information sets of a service authority interval in which the application program service is located; if each group of cluster label data of the extracted user historical use data comprises a plurality of behavior feature information, performing behavior feature analysis in the following mode: performing multi-dimensional behavior feature analysis according to a behavior feature analysis program configured for each behavior feature information in advance; in each level of behavior feature analysis, performing behavior feature analysis only based on one behavior feature information in each group of clustering label data, and inputting clustering label data meeting preset conditions into a next level of behavior feature analysis program, so that the behavior feature analysis is performed based on the next behavior feature information, and the clustering label data is input into the next level of behavior feature analysis program, so that the behavior feature analysis is performed based on the next behavior feature information;
determining a behavior characteristic directed space of the historical user data according to a label classification behavior component corresponding to target clustering label data of which the behavior characteristic analysis result of the historical user data meets a preset condition;
generating a user portrait of the mobile user equipment based on the historical user use data and the determined behavior characteristic directed space of the historical user use data to obtain a generation result, and configuring information of a user push service of the internet application service according to the generation result.
2. The method of claim 1, wherein extracting usage service item information of the historical usage data of the user and clustering the historical usage data of the user based on the usage service item information to obtain a plurality of clustering label data comprises:
listing historical response data segments corresponding to the historical user data, determining service response information of each historical response data segment, and sorting the historical response data segments according to preset response service serial numbers of the service response information to obtain the service item information;
acquiring a project service set of the service project information and a plurality of historical use service sets of the historical use data of the user; under the condition that the portrait matching feature exists in the user pushing service of the historical user data is determined according to the project service set, determining the service matching feature between each historical use service set of the historical user data under the target portrait matching feature and each historical use service set of the historical user data under the portrait matching feature according to the historical use service set of the historical user data under the portrait matching feature and the service calling interface information of the historical use service set of the historical user data under the portrait matching feature; adjusting the historical use service set of the user historical use data under the target portrait matching feature and the historical use service set under the portrait matching feature, wherein the historical use service set has the lowest service matching feature priority;
under the condition that a plurality of historical use service sets are contained in the target portrait matching feature corresponding to the historical use data of the user, determining a service matching feature among the historical use service sets of the historical use data of the user under the target portrait matching feature according to the historical use service sets of the historical use data under the portrait matching feature and service calling interface information of the historical use service sets, and fusing the historical use service sets under the target portrait matching feature based on the service matching feature among the historical use service sets; setting an updating thread for the target historical use service set obtained by fusion according to the historical use service set of the user historical use data under the portrait matching characteristic and the service calling interface information thereof, and adjusting the target historical use service set to a set portrait matching node of the portrait matching characteristic corresponding to the updating thread;
and clustering the historical service use set under the target portrait matching characteristic based on the service grading information of the target project service with the label classification service corresponding to the project service set in the service use project information to obtain a plurality of clustered label data.
3. The method according to claim 1, wherein determining the behavior feature directed space of the historical user data according to the label classification behavior component corresponding to the target clustering label data of which the behavior feature analysis result of the historical user data meets the preset condition comprises:
extracting response parameters of the behavior feature analysis results, and determining target clustering label data corresponding to the response parameters when the response parameters meet preset behavior feature analysis parameters of the mobile user equipment;
extracting label logic information of the target clustering label data according to set service logic characters;
generating a label logic directed space corresponding to the label logic information and a label classification behavior directed space corresponding to target clustering label data, wherein the label logic directed space and the label classification behavior directed space respectively comprise a plurality of directed nodes of different label classification behaviors;
extracting service response information of the label logic information at one directed node in the label logic directed space, and determining the directed node with the shortest label classification behavior in the label classification behavior directed space as a candidate directed node;
configuring the business service response information into the candidate directed nodes to obtain response transferring interface information in the candidate directed nodes, and then generating logic combination information between the label logic information and the target clustering label data based on the business service response information and the response transferring interface information;
acquiring behavior feature updating directed distribution in the candidate directed nodes by taking the response mobilizing interface information as an interface protocol parameter, configuring the behavior feature updating directed distribution to the directed nodes where the service response information is located according to a logic merging unit corresponding to the logic merging information, and obtaining target behavior feature distribution information corresponding to the behavior feature updating directed distribution in the directed nodes where the service response information is located;
and listing the behavior characteristic distribution nodes and the distribution associated information in the target behavior characteristic distribution information, and generating a behavior characteristic directed space of the user historical use data according to the behavior characteristic distribution nodes and the distribution associated information.
4. The method of claim 1, wherein generating a user representation of the mobile user equipment based on the historical user usage data and the determined behavioral characteristic directed space of the historical user usage data to obtain a generation result, and performing information configuration on a user push service of the internet application service according to the generation result comprises:
determining user portrait feature information corresponding to the user portrait, which is determined based on the user historical usage data and the behavior feature directed space, and performing depth extraction on the user portrait by adopting the user portrait feature information to obtain target user portrait feature information;
aiming at the current user portrait feature information in the target user portrait feature information, determining a user push service control set of the current user portrait feature information in a preset portrait service interval based on a first user push service control of the current user portrait feature information in the preset portrait service interval and second user push service controls of each target user portrait feature information in the preset portrait service interval;
determining target push marking information of the current user portrait characteristic information between two adjacent set time periods according to a user push service control set of the current user portrait characteristic information in two adjacent preset portrait service intervals, generating the user portrait of the mobile user equipment according to the target push marking information to obtain a generation result, and performing information configuration on a user push service of the internet application service according to the generation result.
5. A mobile internet user management apparatus for use with a server in communication with a mobile user device, the apparatus comprising:
the clustering module is used for extracting service item information of the historical user use data after receiving the historical user use data sent by the mobile user equipment through internet application service in real time, and clustering the historical user use data based on the service item information to obtain a plurality of clustering label data; the service item information is service response information formed by program service nodes related to application program services in the user historical use data;
the matching module is used for carrying out behavior characteristic analysis on each group of clustering label data of the historical user data and each set label classifying behavior in a set of set label classifying behaviors; the set label classification behavior set stores a plurality of set label classification behaviors and behavior feature information corresponding to each set of set label classification behaviors, and the plurality of set label classification behaviors are label statistical behavior information sets of a service authority interval in which the application program service is located; if each group of cluster label data of the extracted user historical use data comprises a plurality of behavior feature information, performing behavior feature analysis in the following mode: performing multi-dimensional behavior feature analysis according to a behavior feature analysis program configured for each behavior feature information in advance; in each level of behavior feature analysis, performing behavior feature analysis only based on one behavior feature information in each group of clustering label data, and inputting clustering label data meeting preset conditions into a next level of behavior feature analysis program, so that the behavior feature analysis is performed based on the next behavior feature information, and the clustering label data is input into the next level of behavior feature analysis program, so that the behavior feature analysis is performed based on the next behavior feature information;
the determining module is used for determining a behavior characteristic directed space of the historical user data according to a label classification behavior component corresponding to target clustering label data of which the behavior characteristic analysis result of the historical user data meets a preset condition;
and the generating module is used for generating a user portrait of the mobile user equipment based on the historical user data and the determined behavior characteristic directed space of the historical user data to obtain a generating result, and performing information configuration on a user push service of the internet application service according to the generating result.
6. The apparatus of claim 5, wherein the cluster processing module is configured to:
listing historical response data segments corresponding to the historical user data, determining service response information of each historical response data segment, and sorting the historical response data segments according to preset response service serial numbers of the service response information to obtain the service item information;
acquiring a project service set of the service project information and a plurality of historical use service sets of the historical use data of the user; under the condition that the portrait matching feature exists in the user pushing service of the historical user data is determined according to the project service set, determining the service matching feature between each historical use service set of the historical user data under the target portrait matching feature and each historical use service set of the historical user data under the portrait matching feature according to the historical use service set of the historical user data under the portrait matching feature and the service calling interface information of the historical use service set of the historical user data under the portrait matching feature; adjusting the historical use service set of the user historical use data under the target portrait matching feature and the historical use service set under the portrait matching feature, wherein the historical use service set has the lowest service matching feature priority;
under the condition that a plurality of historical use service sets are contained in the target portrait matching feature corresponding to the historical use data of the user, determining a service matching feature among the historical use service sets of the historical use data of the user under the target portrait matching feature according to the historical use service sets of the historical use data under the portrait matching feature and service calling interface information of the historical use service sets, and fusing the historical use service sets under the target portrait matching feature based on the service matching feature among the historical use service sets; setting an updating thread for the target historical use service set obtained by fusion according to the historical use service set of the user historical use data under the portrait matching characteristic and the service calling interface information thereof, and adjusting the target historical use service set to a set portrait matching node of the portrait matching characteristic corresponding to the updating thread;
and clustering the historical service use set under the target portrait matching characteristic based on the service grading information of the target project service with the label classification service corresponding to the project service set in the service use project information to obtain a plurality of clustered label data.
7. The apparatus of claim 5, wherein the determining module is configured to:
extracting response parameters of the behavior feature analysis results, and determining target clustering label data corresponding to the response parameters when the response parameters meet preset behavior feature analysis parameters of the mobile user equipment;
extracting label logic information of the target clustering label data according to set service logic characters;
generating a label logic directed space corresponding to the label logic information and a label classification behavior directed space corresponding to target clustering label data, wherein the label logic directed space and the label classification behavior directed space respectively comprise a plurality of directed nodes of different label classification behaviors;
extracting service response information of the label logic information at one directed node in the label logic directed space, and determining the directed node with the shortest label classification behavior in the label classification behavior directed space as a candidate directed node;
configuring the business service response information into the candidate directed nodes to obtain response transferring interface information in the candidate directed nodes, and then generating logic combination information between the label logic information and the target clustering label data based on the business service response information and the response transferring interface information;
acquiring behavior feature updating directed distribution in the candidate directed nodes by taking the response mobilizing interface information as an interface protocol parameter, configuring the behavior feature updating directed distribution to the directed nodes where the service response information is located according to a logic merging unit corresponding to the logic merging information, and obtaining target behavior feature distribution information corresponding to the behavior feature updating directed distribution in the directed nodes where the service response information is located;
and listing the behavior characteristic distribution nodes and the distribution associated information in the target behavior characteristic distribution information, and generating a behavior characteristic directed space of the user historical use data according to the behavior characteristic distribution nodes and the distribution associated information.
8. The apparatus of claim 5, wherein the generating module is configured to:
determining user portrait feature information corresponding to the user portrait, which is determined based on the user historical usage data and the behavior feature directed space, and performing depth extraction on the user portrait by adopting the user portrait feature information to obtain target user portrait feature information;
aiming at the current user portrait feature information in the target user portrait feature information, determining a user push service control set of the current user portrait feature information in a preset portrait service interval based on a first user push service control of the current user portrait feature information in the preset portrait service interval and second user push service controls of each target user portrait feature information in the preset portrait service interval;
determining target push marking information of the current user portrait characteristic information between two adjacent set time periods according to a user push service control set of the current user portrait characteristic information in two adjacent preset portrait service intervals, generating the user portrait of the mobile user equipment according to the target push marking information to obtain a generation result, and performing information configuration on a user push service of the internet application service according to the generation result.
9. A mobile internet user management system, characterized in that the mobile internet user management system comprises a server and a mobile user equipment in communication with the server;
the server is used for extracting service item information of the historical user use data after receiving the historical user use data sent by the mobile user equipment through internet application service in real time, and clustering the historical user use data based on the service item information to obtain a plurality of clustering label data; the service item information is service response information formed by program service nodes related to application program services in the user historical use data;
the server is used for carrying out behavior feature analysis on each group of clustering label data of the historical user data and each set label classification behavior in a set of set label classification behaviors; the set label classification behavior set stores a plurality of set label classification behaviors and behavior feature information corresponding to each set of set label classification behaviors, and the plurality of set label classification behaviors are label statistical behavior information sets of a service authority interval in which the application program service is located; if each group of cluster label data of the extracted user historical use data comprises a plurality of behavior feature information, performing behavior feature analysis in the following mode: performing multi-dimensional behavior feature analysis according to a behavior feature analysis program configured for each behavior feature information in advance; in each level of behavior feature analysis, performing behavior feature analysis only based on one behavior feature information in each group of clustering label data, and inputting clustering label data meeting preset conditions into a next level of behavior feature analysis program, so that the behavior feature analysis is performed based on the next behavior feature information, and the clustering label data is input into the next level of behavior feature analysis program, so that the behavior feature analysis is performed based on the next behavior feature information;
the server is used for determining a behavior characteristic directed space of the historical user data according to a label classification behavior component corresponding to target clustering label data, of which the behavior characteristic analysis result of the historical user data meets a preset condition;
the server is used for generating a user portrait of the mobile user equipment based on the user historical use data and the determined behavior characteristic directed space of the user historical use data to obtain a generation result, and performing information configuration on a user push service of the internet application service according to the generation result.
10. A computer-readable storage medium, on which a computer program is stored which, when executed, implements the method of any of claims 1-4.
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| CN113806638A (en) * | 2021-09-29 | 2021-12-17 | 中国平安人寿保险股份有限公司 | Personalized recommendation method based on user portrait and related equipment |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN113806638A (en) * | 2021-09-29 | 2021-12-17 | 中国平安人寿保险股份有限公司 | Personalized recommendation method based on user portrait and related equipment |
| CN113806638B (en) * | 2021-09-29 | 2023-12-08 | 中国平安人寿保险股份有限公司 | Personalized recommendation method based on user portrait and related equipment |
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