CN110659743A

CN110659743A - Label management method and device based on life cycle idea

Info

Publication number: CN110659743A
Application number: CN201910909094.7A
Authority: CN
Inventors: 沈林江; 张笑笑
Original assignee: Beijing MetarNet Technologies Co Ltd
Current assignee: Beijing MetarNet Technologies Co Ltd
Priority date: 2019-09-25
Filing date: 2019-09-25
Publication date: 2020-01-07

Abstract

The disclosure relates to a label management method and device based on a life cycle concept, an electronic device and a storage medium. Wherein, the method comprises the following steps: collecting all operator data related to daily behaviors of users, integrating the operator data into a full-dimensional data wide table through user numbers, and finishing data collection; inputting a full-dimensional data wide table by adopting a machine learning algorithm, and outputting a user label based on machine self-learning; outputting a label in a machine learning period and then performing preset label period association through a preset label weakening/fading period; and outputting the label by adopting a machine learning algorithm based on the user behavior data. According to the method and the system, dynamic updating of the user label is realized through label management based on the life cycle concept, and the social contact adhesion degree is improved.

Description

Label management method and device based on life cycle idea

Technical Field

The present disclosure relates to the field of internet, and in particular, to a method and an apparatus for managing tags based on a life cycle concept, an electronic device, and a computer-readable storage medium.

Background

With the development of the times, the individuality of users is highlighted to be a great trend, the user tags are used as abstractions of user behavior characteristics, and tag competition becomes core competition of service/product providers. Most of the existing label construction means define labels based on index data, the technology relies on accumulation of similar user behaviors, and sporadic behaviors in an accumulation time period are mostly ignored. This would form a vicious circle, with no updates to the tags, repeated pushing of content, similar behaviors accumulated by the user, and no updates to the tags continuing, gradually leading to user churn due to continuous repeated pushing.

Accordingly, there is a need for one or more methods to address the above-mentioned problems.

It is to be noted that the information disclosed in the above background section is only for enhancement of understanding of the background of the present disclosure, and thus may include information that does not constitute prior art known to those of ordinary skill in the art.

Disclosure of Invention

An object of the present disclosure is to provide a method, an apparatus, an electronic device, and a computer-readable storage medium for tag management based on a life cycle concept, thereby overcoming, at least to some extent, one or more of the problems due to the limitations and disadvantages of the related art.

According to an aspect of the present disclosure, there is provided a tag management method based on a life cycle concept, including:

a data acquisition step, wherein all operator data related to daily behaviors of a user are acquired and integrated into a full-dimensional data wide table through a user number to complete data acquisition;

a label defining step of a machine learning algorithm, which is to adopt the machine learning algorithm, input a full-dimensional data wide table and output a user label based on machine self-learning;

a step of presetting a life cycle of the label, namely, presetting a label cycle association after the label is output in a machine learning cycle through a preset label weakening/extinction cycle;

and (4) managing the life cycle of the label, namely outputting the label by adopting a machine learning algorithm based on the user behavior data.

In an exemplary embodiment of the present disclosure, the data collecting step further includes:

and (3) data processing: and cleaning the full-dimensional data wide table based on the principles of data loss, data abnormality and no label value to form user characteristic data suitable for label definition.

and collecting operator data of user internet behavior, voice call and service use, and integrating the operator data into a full-dimensional data wide table through the user number to finish data collection.

In an exemplary embodiment of the present disclosure, the machine learning algorithm defining tag step further comprises:

and inputting user characteristic data by adopting a machine learning algorithm, giving different weights by adopting the machine learning algorithm through machine self-learning, and forming a user label.

In an exemplary embodiment of the present disclosure, the tag lifecycle in the tag lifecycle presetting step further includes:

the label weakens the life cycle, and after the time point is reached, the grade of the label needs to be adjusted downwards, and other newly appeared labels are fused for label use;

and (4) the label disappears in the life cycle, and after the time point is reached, the label is not used any more when being off-line.

In an exemplary embodiment of the present disclosure, the tag lifecycle management step further comprises:

based on the user behavior data, adopting a machine learning algorithm to output labels;

if the output label is successfully matched with the weakening label, keeping the label but weakening the grade;

and if the output label is successfully matched with the extinction label, deleting the label of the type from the offline. Looking up new tags, and if the tags are fewer, introducing social influence to user tags.

In an exemplary embodiment of the present disclosure, the method further comprises:

the social influence users can analyze and judge through short message data and call data of operators, social platforms obtained through internet crawling and instant messaging data;

after the social influence on the user is judged, all tags of the user are introduced, but the tag level is lower than the personal tag output by machine learning.

In one aspect of the present disclosure, there is provided a tag management apparatus based on a life cycle concept, including:

the data acquisition module is used for acquiring all operator data related to daily behaviors of the user, integrating the operator data into a full-dimensional data wide table through the user number and finishing data acquisition;

the machine learning algorithm definition tag module is used for inputting a full-dimensional data wide table by adopting a machine learning algorithm and outputting a user tag based on machine self-learning;

the label life cycle presetting module is used for presetting label life cycle association after the label is output in a machine learning cycle through a label weakening/extinction cycle;

and the label life cycle management module is used for outputting labels by adopting a machine learning algorithm based on the user behavior data.

In one aspect of the present disclosure, there is provided an electronic device including:

a processor; and

a memory having computer readable instructions stored thereon which, when executed by the processor, implement a method according to any of the above.

In an aspect of the disclosure, a computer-readable storage medium is provided, on which a computer program is stored, which computer program, when being executed by a processor, realizes the method according to any one of the above.

According to the label management method based on the life cycle concept in the exemplary embodiment of the disclosure, all operator data related to daily behaviors of a user are collected, and the data are collected by integrating user numbers into a full-dimensional data wide table; inputting a full-dimensional data wide table by adopting a machine learning algorithm, and outputting a user label based on machine self-learning; outputting a label in a machine learning period and then performing preset label period association through a preset label weakening/fading period; and outputting the label by adopting a machine learning algorithm based on the user behavior data. On one hand, the method and the device effectively solve the problem of user loss caused by no updating of the tag by formulating the tag life cycle and creating a life shadow-level tag; on the other hand, the social influence user tags are introduced, social commonality is established, and social adhesion 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 disclosure.

Drawings

The above and other features and advantages of the present disclosure will become more apparent by describing in detail exemplary embodiments thereof with reference to the attached drawings.

FIG. 1 illustrates a flow diagram of a method for label management based on lifecycle ideas in accordance with an exemplary embodiment of the present disclosure;

FIG. 2 illustrates a schematic block diagram of a life cycle philosophy based tag management apparatus according to an exemplary embodiment of the present disclosure;

FIG. 3 schematically illustrates a block diagram of an electronic device according to an exemplary embodiment of the present disclosure; and

fig. 4 schematically illustrates a schematic diagram of a computer-readable storage medium according to an exemplary embodiment of the present disclosure.

Detailed Description

Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art. The same reference numerals denote the same or similar parts in the drawings, and thus, a repetitive description thereof will be omitted.

Furthermore, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the disclosure. One skilled in the relevant art will recognize, however, that the embodiments of the disclosure can be practiced without one or more of the specific details, or with other methods, components, materials, devices, steps, and so forth. In other instances, well-known structures, methods, devices, implementations, materials, or operations are not shown or described in detail to avoid obscuring aspects of the disclosure.

The block diagrams shown in the figures are functional entities only and do not necessarily correspond to physically separate entities. That is, these functional entities may be implemented in the form of software, or in one or more software-hardened modules, or in different networks and/or processor devices and/or microcontroller devices.

In the present exemplary embodiment, first, a tag management method based on a life cycle concept is provided; referring to fig. 1, the tag management method based on the life cycle concept may include the steps of:

a data acquisition step S110, wherein all operator data related to daily behaviors of a user are acquired and integrated into a full-dimensional data wide table through a user number to finish data acquisition;

a step S120 of defining labels by a machine learning algorithm, wherein the machine learning algorithm is adopted, a full-dimensional data wide table is input, and user labels are output based on machine self-learning;

a tag life cycle presetting step S130, wherein through a preset tag weakening/extinction cycle, after a tag is output in a machine learning cycle, a preset tag cycle association is carried out;

and a tag life cycle management step S140, wherein a machine learning algorithm is adopted to output tags based on the user behavior data.

Next, a tag management method based on the life cycle concept in the present exemplary embodiment will be further described.

In the data collection step S110, all operator data related to the daily behavior of the user may be collected, and the data collection is completed by integrating the user numbers into a full-dimensional data wide table.

In an embodiment of the present example, the data collecting step further comprises:

In the embodiment of the present example, operator data including internet access behavior, voice call, and service usage of a user is collected and integrated into a full-dimensional data wide table by a mobile phone number. And performing broad-table cleaning based on the principles of data loss, data abnormality, no tag value and the like to form user characteristic data suitable for tag definition.

In the step S120 of defining the label by the machine learning algorithm, the machine learning algorithm may be adopted to input the full-dimensional data width table and output the user label based on the machine self-learning.

In an embodiment of the present example, the machine learning algorithm defining tag step further comprises:

In the embodiment of the present example, user characteristic data is input, and different weights are given and user tags are formed through machine self-learning by using a machine learning algorithm.

In the step S130 of presetting the life cycle of the tag, the preset tag cycle association may be performed after the tag is output in the machine learning cycle through the preset tag weakening/extinction cycle.

In an embodiment of the present example, the tag lifecycle in the tag lifecycle presetting step further includes:

In the embodiment of the present example, a dynamic tag refers to a tag that changes with time and user behavior, such as a user activity tag, and changes according to the change of a specific behavior of a user in a specific time period, so that such a tag needs to be used in combination with a life cycle to be valuable.

The label life cycle comprises two types, namely a label weakening life cycle, and after the time point is reached, the grade of the label of the type needs to be adjusted downwards, and other newly appeared labels are fused for label use; and the second is the life cycle of label extinction, and after the time point is reached, the labels are not used any more when being off-line.

The preset life cycle of the label can be formulated through business parties with profound cognition on business and industry.

In the tag lifecycle management step S140, tag output may be performed using a machine learning algorithm based on the user behavior data.

In an embodiment of the present example, the tag lifecycle management step further comprises:

In an embodiment of the present example, the method further comprises:

In the embodiment of the present example, based on user behavior data, a machine learning algorithm is employed for tag output. If the output label is successfully matched with the weakening label, keeping the label but weakening the grade; and if the output label is successfully matched with the extinction label, deleting the label of the type from the offline. Looking up new tags, and if the tags are fewer, introducing social influence to user tags.

The social influence users can analyze and judge through short message data and call data of operators, social platforms obtained through internet crawling and instant messaging data. After the social influence on the user is judged, all tags of the user are introduced, but the tag level is lower than the personal tag output by machine learning.

In the embodiment of the example, the present disclosure provides a tag management method of a life cycle concept, in which deep analysis tags are used as core competitiveness, but contradictory conflicts caused by various problems in the existing tag means are innovated to provide a management concept of the life cycle of the tags, and firstly, by formulating the life cycle of the tags, shadow-level tags of the life cycle are created, so that user loss caused by no update of the tags is effectively solved; secondly, social influence user tags are introduced, social commonality is established, and social adhesion is improved.

In the embodiment of the present example, the present disclosure aims to provide a label management method of a life cycle concept, which is implemented in a way that a whole amount of operator data capable of constructing a user label is collected and processed, and the user label is output through machine self-learning based on a machine learning algorithm. The service party having profound understanding on the service and industry participates in the preset label life cycle, and is in correlation matching with the preset label life cycle in each label generation cycle, if the upper weakening cycle is matched, the label is kept but the weakening grade is weakened, and if the upper weakening cycle is matched, the label is off-line. In order to ensure the diversity of the tags, a mode of introducing social interaction to influence the tags of the users is adopted, and the requirements of the social interaction common topics of the users are met. The method gives vitality and vitality to the label, and really realizes that the label is made into the shadow of the user.

In an embodiment of the present example, the present disclosure provides a tag management approach to lifecycle concepts, comprising: data acquisition and processing, label definition by a machine learning algorithm, label life cycle presetting and label life cycle management. And collecting all operator data related to daily behaviors of the user, and integrating the operator data into a full-dimensional data wide table through the user number. And cleaning the wide table based on the principles of data missing, data abnormity, no tag value and the like to form user characteristic data suitable for tag definition. And inputting the full-dimensional characteristic data of the user by adopting a machine learning algorithm, and outputting the user label based on machine self-learning. The label life cycle management method includes the steps of introducing label life cycle presetting and label life cycle management according to a label life cycle management concept, presetting a label weakening/extinction cycle through a service expert, carrying out preset label cycle association after a machine learning cycle outputs labels, and weakening grades or inserting labels off line if matching is successful. Social interaction is introduced to influence the user tags, so that the diversity of the tags is guaranteed, the social interaction common topic requirements of the users are guaranteed, and the user tags which are vital and have the shape like movies are really created.

It should be noted that although the various steps of the methods of the present disclosure are depicted in the drawings in a particular order, this does not require or imply that these steps must be performed in this particular order, or that all of the depicted steps must be performed, to achieve desirable results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step execution, and/or one step broken down into multiple step executions, etc.

In addition, in the present exemplary embodiment, a tag management apparatus based on a life cycle concept is also provided. Referring to fig. 2, the life cycle concept-based tag management apparatus 200 may include: the data collection module 210, the machine learning algorithm definition tag module 220, the tag lifecycle provisioning module 230, and the tag lifecycle management module 240. Wherein:

the data acquisition module 210 is used for acquiring all operator data related to daily behaviors of the user, integrating the operator data into a full-dimensional data wide table through the user number and completing data acquisition;

a machine learning algorithm definition tag module 220 for inputting a full-dimensional data wide table by adopting a machine learning algorithm and outputting a user tag based on machine self-learning;

the tag life cycle presetting module 230 is configured to perform preset tag cycle association after a tag is output in a machine learning cycle through a preset tag weakening/extinction cycle;

and the tag life cycle management module 240 is configured to output a tag by using a machine learning algorithm based on the user behavior data.

The specific details of each module of the above tag management apparatus based on the life cycle concept have been described in detail in the corresponding tag management method based on the life cycle concept, and therefore are not described herein again.

It should be noted that although several modules or units of the tag management apparatus 200 based on the life cycle concept are mentioned in the above detailed description, such division is not mandatory. Indeed, the features and functionality of two or more modules or units described above may be embodied in one module or unit, according to embodiments of the present disclosure. Conversely, the features and functions of one module or unit described above may be further divided into embodiments by a plurality of modules or units.

In addition, in an exemplary embodiment of the present disclosure, an electronic device capable of implementing the above method is also provided.

As will be appreciated by one skilled in the art, aspects of the present invention may be embodied as a system, method or program product. Thus, various aspects of the invention may be embodied in the form of: an entirely hardware embodiment, an entirely software embodiment (including firmware, microcode, etc.) or an embodiment combining hardware and software aspects that may all generally be referred to herein as a "circuit," module "or" system.

An electronic device 300 according to such an embodiment of the invention is described below with reference to fig. 3. The electronic device 300 shown in fig. 3 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present invention.

As shown in fig. 3, electronic device 300 is embodied in the form of a general purpose computing device. The components of electronic device 300 may include, but are not limited to: the at least one processing unit 310, the at least one memory unit 320, a bus 330 connecting different system components (including the memory unit 320 and the processing unit 310), and a display unit 340.

Wherein the storage unit stores program code that is executable by the processing unit 310 to cause the processing unit 310 to perform steps according to various exemplary embodiments of the present invention as described in the above section "exemplary method" of the present specification. For example, the processing unit 310 may perform steps S110 to S140 as shown in fig. 1.

The storage unit 320 may include readable media in the form of volatile storage units, such as a random access memory unit (RAM)3201 and/or a cache memory unit 3202, and may further include a read only memory unit (ROM) 3203.

The storage unit 320 may also include a program/utility 3204 having a set (at least one) of program modules 3205, such program modules 3205 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may comprise an implementation of a network environment.

Bus 330 may be one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

The electronic device 300 may also communicate with one or more external devices 370 (e.g., keyboard, pointing device, bluetooth device, etc.), with one or more devices that enable a user to interact with the electronic device 300, and/or with any devices (e.g., router, modem, etc.) that enable the electronic device 300 to communicate with one or more other computing devices. Such communication may occur via an input/output (I/O) interface 350. Also, the electronic device 300 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the internet) via the network adapter 360. As shown, network adapter 360 communicates with the other modules of electronic device 300 via bus 330. It should be appreciated that although not shown in the figures, other hardware and/or software modules may be used in conjunction with electronic device 300, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.

Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which may be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.

In an exemplary embodiment of the present disclosure, there is also provided a computer-readable storage medium having stored thereon a program product capable of implementing the above-described method of the present specification. In some possible embodiments, aspects of the invention may also be implemented in the form of a program product comprising program code means for causing a terminal device to carry out the steps according to various exemplary embodiments of the invention described in the above-mentioned "exemplary methods" section of the present description, when said program product is run on the terminal device.

Referring to fig. 4, a program product 400 for implementing the above method according to an embodiment of the present invention is described, which may employ a portable compact disc read only memory (CD-ROM) and include program code, and may be run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited in this regard and, in the present document, a readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.

The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

A computer readable signal medium may include a propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable signal medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.

Program code embodied on a readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

Program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., through the internet using an internet service provider).

Furthermore, the above-described figures are merely schematic illustrations of processes involved in methods according to exemplary embodiments of the invention, and are not intended to be limiting. It will be readily understood that the processes shown in the above figures are not intended to indicate or limit the chronological order of the processes. In addition, it is also readily understood that these processes may be performed synchronously or asynchronously, e.g., in multiple modules.

Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.

It will be understood that the present disclosure 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 present disclosure is to be limited only by the terms of the appended claims.

Claims

1. A label management method based on life cycle idea is characterized by comprising the following steps:

2. The method of claim 1, wherein the data acquisition step further comprises:

3. The method of claim 1, wherein the data acquisition step further comprises:

4. The method of claim 1, wherein the machine learning algorithm defining labels step further comprises:

5. The method of claim 1, wherein the tag lifecycle in the tag lifecycle presetting step further comprises:

6. The method of claim 1, wherein the tag lifecycle management step further comprises:

and if the output tags are successfully matched with the extinction tags, deleting the off-line of the tags, checking the newly appeared tags, and introducing social interaction to influence the user tags if the tags are fewer.

7. The method of claim 1, wherein the method further comprises:

8. A label management device based on a life cycle concept, the device comprising:

9. An electronic device, comprising

A processor; and

a memory having computer readable instructions stored thereon which, when executed by the processor, implement the method of any of claims 1 to 7.

10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1 to 7.