CN110264262B - Data processing method and device based on user behavior and electronic equipment - Google Patents
Data processing method and device based on user behavior and electronic equipment Download PDFInfo
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Abstract
The embodiment of the disclosure provides a data processing method and device based on user behaviors and electronic equipment. The method comprises the following steps: acquiring historical behavior information of a target user aiming at a target application; analyzing the historical behavior information to determine the behavior characteristic information of the target user; and determining target data corresponding to various operation behaviors of the target user according to the behavior characteristic information, and issuing the target data according to the operation behaviors executed by the target user aiming at the target application. The embodiment of the disclosure ensures that different target data are issued to users with different behavior characteristics, thereby stimulating the user to increase the retention time in the target application, improving the user retention rate of the target application, and solving the problems of poor user experience and easy user loss caused by the fact that part of users cannot complete the operation conditions corresponding to the target data due to the adoption of an undifferentiated target data determination mode in the prior art.
Description
Technical Field
The present disclosure relates to the field of data processing technologies, and in particular, to a data processing method and apparatus based on user behavior, and an electronic device.
Background
The user retention rate refers to the ratio of the retained user to the newly added user of the application, wherein the retained user refers to a user who starts to use the application within a certain period of time and continues to use the application after a period of time. In order to improve the user retention rate of the application, the enthusiasm of the user for using the application and the user retention rate of the application are generally improved by issuing data such as a virtual red envelope, a virtual gift, an experience value and the like to the user.
However, the operation conditions for issuing the data such as the virtual red envelope, the virtual gift, the experience value, and the like to the user are fixed, and the usage habits of different users for the application are different, for example, some users have a long usage time and some users have a short usage time, and therefore, there may be a case where the data issued by the application cannot be acquired by the user with a short usage time. Therefore, the existing mode for improving the user retention rate has the problems that part of users are poor in use experience and the users are easy to lose.
Disclosure of Invention
The application provides a data processing method and device based on user behaviors, an electronic device and a computer readable medium, which can solve the technical problems. The technical scheme is as follows.
In a first aspect, a data processing method based on user behavior is provided, and the method includes:
acquiring historical behavior information of a target user aiming at a target application;
analyzing the historical behavior information to determine the behavior characteristic information of the target user;
and determining target data corresponding to various operation behaviors of the target user according to the behavior characteristic information, and issuing the target data according to the operation behaviors executed by the target user aiming at the target application.
In a second aspect, a data processing apparatus based on user behavior is provided, the apparatus comprising:
the data acquisition module is used for acquiring historical behavior information of a target user aiming at a target application;
the characteristic analysis module is used for analyzing the historical behavior information and determining the behavior characteristic information of the target user;
and the data determining module is used for determining target data corresponding to various operation behaviors of the target user according to the behavior characteristic information so as to issue the target data according to the operation behaviors executed by the target user aiming at the target application.
In a third aspect, an electronic device is provided, which includes:
one or more processors;
a memory;
one or more application programs, wherein the one or more application programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs configured to: and executing the data processing method based on the user behaviors.
In a fourth aspect, a computer-readable medium is provided, on which a computer program is stored, which program, when being executed by a processor, is adapted to carry out the above-mentioned data processing method based on user behavior.
The technical scheme provided by the embodiment of the disclosure has the following beneficial effects: the method comprises the steps of obtaining historical behavior information of a target user aiming at a target application, analyzing the historical behavior information, determining behavior feature information of the target user, determining target data corresponding to various operation behaviors of the target user according to the behavior feature information, playing a role of analyzing the behavior feature information of the user according to the historical behavior information of the user, namely determining the behavior feature of the user according to the historical behavior of the user, taking the behavior feature of the user as a condition for determining the target data, issuing the target data according to the operation behaviors of the target user aiming at the target application, and achieving the purpose of issuing different target data to users with different behavior features, so that the user is stimulated to increase the retention time of the target application, the user retention rate of the target application is improved, and the problem that the prior art adopts a non-differential target data determination mode is solved, the problems that part of users cannot complete the operation conditions corresponding to the target data, so that the user experience is poor and the users are easy to lose are solved.
Drawings
The above and other features, advantages and aspects of various embodiments of the present disclosure will become more apparent by referring to the following detailed description when taken in conjunction with the accompanying drawings. Throughout the drawings, the same or similar reference numbers refer to the same or similar elements. It should be understood that the drawings are schematic and that elements and features are not necessarily drawn to scale.
Fig. 1 is a schematic flowchart of a data processing method based on user behavior according to an embodiment of the present disclosure;
fig. 2 is a schematic structural diagram of a data processing apparatus based on user behavior according to an embodiment of the present disclosure;
fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure.
Detailed Description
Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present disclosure are shown in the drawings, it is to be understood that the present disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein, but rather are provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the disclosure are for illustration purposes only and are not intended to limit the scope of the disclosure.
It should be understood that the various steps recited in the method embodiments of the present disclosure may be performed in a different order, and/or performed in parallel. Moreover, method embodiments may include additional steps and/or omit performing the illustrated steps. The scope of the present disclosure is not limited in this respect.
The term "include" and variations thereof as used herein are open-ended, i.e., "including but not limited to". The term "based on" is "based, at least in part, on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Relevant definitions for other terms will be given in the following description.
It should be noted that the terms "first", "second", and the like in the present disclosure are only used for distinguishing the devices, modules or units, and are not used for limiting the devices, modules or units to be different devices, modules or units, and also for limiting the sequence or interdependence relationship of the functions executed by the devices, modules or units.
It is noted that references to "a", "an", and "the" modifications in this disclosure are intended to be illustrative rather than limiting, and that those skilled in the art will recognize that "one or more" may be used unless the context clearly dictates otherwise. The names of messages or information exchanged between devices in the embodiments of the present disclosure are for illustrative purposes only, and are not intended to limit the scope of the messages or information.
Example one
An embodiment of the present disclosure provides a data processing method based on user behavior, as shown in fig. 1, the method includes: step S101, step S102, and step S103.
And step S101, acquiring historical behavior information of the target user aiming at the target application.
In the embodiment of the disclosure, the historical behavior information is used for representing user behavior information of a target user on a target application in a past preset time period, specifically, the operation behavior and operation behavior related information of a general user of the user behavior information, and specifically, the operation behavior includes praise, collection, attention, giving a virtual gift, comment, sharing link and the like; the operation behavior related information may include the time of execution of the operation behavior, and the object pointed to by the operation behavior, for example, the object pointed to by the operation is the small video X. For example, assume that the historical behavior information is as follows:
x months X days X, X minutes and X seconds in X year, and video A is praised;
x months X days X hours X minutes X seconds in X year, and paying attention to the video author A;
clicking to play the live broadcast of the anchor B in X years, X months, X days, X minutes and X seconds;
and giving the virtual gift X of the anchor B in X years, X months, X days, X minutes and X seconds.
More specifically, a target user is generally determined by a user ID, that is, corresponding historical behavior information is obtained according to the ID of the target user, and in a specific application, the user ID may be set as information that uniquely identifies the user, such as an account number (e.g., a mobile phone number, a mailbox number, etc.), a certificate number (e.g., an identification number, a school number), and the like of the user in the target application; or the target user is determined by the terminal device ID, that is, historical behavior information corresponding to the terminal device ID is acquired.
In the embodiment of the present disclosure, the target application may be any type of application, such as a video-type application, a news-information-type application, a novel-type application, and the like, which are not listed here.
And S102, analyzing the historical behavior information and determining the behavior characteristic information of the target user.
In the embodiment of the disclosure, the behavior feature information is used for representing behavior information related to the target application by the user, such as operation habits, interest objects, retention time aiming at the interest objects, and the like of the target user on the target application in the past preset time.
Step S103, determining target data corresponding to various operation behaviors of the target user according to the behavior feature information, and issuing the target data according to the operation behaviors executed by the target user aiming at the target application.
The embodiment of the disclosure acquires the historical behavior information of the target user aiming at the target application, analyzes the historical behavior information, determines the behavior feature information of the target user, determines the target data corresponding to various operation behaviors of the target user according to the behavior feature information, plays a role in analyzing the behavior feature information of the user according to the historical behavior information of the user, namely realizes the purpose of determining the behavior feature of the user according to the historical behavior of the user, takes the behavior feature of the user as a condition for determining the target data, issues the target data according to the operation behavior executed by the target user aiming at the target application, realizes the purpose of issuing different target data to users with different behavior features, thereby stimulating the user to increase the retention time in the target application, improving the user retention rate of the target application, and solving the problem that the prior art adopts a non-differential target data determination mode, the problems that part of users cannot complete the operation conditions corresponding to the target data, so that the user experience is poor and the users are easy to lose are solved.
In one implementation, the behavior feature information includes at least one of:
an object of interest of a target user;
a retention time for the object of interest;
the operation habit of the target user.
In the embodiment of the disclosure, the operation habit is used for representing a frequent behavior manner of the target user on the target application, and specifically may be a certain operation behavior, such as operations of praise, giving a virtual gift, sharing a link, and the like; the method can also be used in a certain operation behavior and application scene thereof, for example, habits such as giving a virtual gift while watching a live broadcast, or adding comments while playing a video; the interest object may be typed data or specific data, for example, the interest object is a variety program, military news, science fiction movie, fantasy novel, a certain type of live broadcast (e.g. eating broadcast), etc.; or a certain drama, a certain book, a certain article, a certain fiction, a certain author, a certain anchor, etc.; the duration of the object of interest generally refers to the duration of the target user on a specific type of data, and specifically, the specific type of data is generally data with viewable content such as a video, a movie, a live broadcast, an article, and the like.
In another implementation, as shown in fig. 1, the analyzing the historical behavior information to determine the operation habits of the target user includes at least one of the following:
if the operation times of any operation behavior in the historical behavior information are larger than a preset operation time threshold, determining the operation habit of a target user according to the operation behavior;
if the operation frequency of any operation behavior in the historical behavior information is larger than a preset operation frequency threshold, determining the operation habit of a target user according to the operation behavior;
and if the operation time of any operation behavior in the historical behavior information is within a preset operation time period, determining the operation habit of the target user according to the operation behavior.
In specific application, if the operation frequency of the praise operation of the target user in the historical behavior information is 100 times, the operation habit of the target user can be determined to be praise; or if the number of operations of the virtual gift-giving operation of the target user in the historical behavior information is 100, determining that the operation habit of the target user is to give the virtual gift while watching the live program according to the virtual gift-giving operation.
In specific application, if the operation frequency of the target user for performing the attention operation in the historical behavior information is 10 times/30 min, the operation habit of the target user can be determined to be the attention operation; if the number of times that the target user executes the sharing link in the historical behavior information is 30, and the operation time for executing the sharing link is within the time period of 20:00-21:00, it may be determined that the operation habit of the target user is to execute the sharing link in the predetermined time period.
In yet another implementation, as shown in fig. 1, analyzing the historical behavior information to determine an object of interest of the target user includes:
step S1024 (not shown in the figure), determining the target object respectively aimed at by each operation behavior of the target user as the interest object of the target user.
In specific application, a target object is generally displayed by the target application through an interactive interface, and various controls (such as a complimentary control, a comment control, a focus-added control, a link-sharing control, and the like) are arranged on the interactive interface, so that each control operation of a user and the target object for which each operation behavior is respectively directed can be determined according to the received operation of the user on the control, and an interest object of the user is determined. For example, if the object that the user likes is the movie "guess of lie X", then the object of interest is determined to be the movie "guess of lie X".
In another implementation manner, the step S1024 determines, as the object of interest of the target user, a target object respectively targeted by each operation behavior of the target user, including:
if the target object respectively aimed at by each operation behavior of the target user is the preset type data, determining the retention time of the target user on the preset type data, and judging whether the retention time is greater than the preset retention time;
and if the retention time is longer than the preset retention time, determining the target object respectively aimed at by each operation behavior of the target user as the interest object of the target user.
In the embodiment of the present disclosure, the predetermined type of data refers to data having viewable content or operable functions, such as movies, videos, articles, and the like. When the method is applied specifically, if the time that the user often watches the variety programs when the user opens the target application every time is greater than a preset time threshold, namely the retention time of the target user on the variety programs is greater than a preset retention time, the user interest object is the variety program.
According to the embodiment of the disclosure, when the target object respectively aimed at by each operation behavior of the target user is the predetermined type of data, the retention time on the target object respectively aimed at by each operation behavior is compared with the preset retention time, so as to provide a data basis for determining the interest object, and solve the problem that the determined interest object is inaccurate because the retention time is not considered in the prior art. For example, assuming that the target application is a video-class application and a plurality of classification data such as variety, movie, tv show, documentary, etc. are provided in the video-class application, assuming that the user clicks and previews all classification data in the video-class application, but only selects and completely views or views most of the content in the variety program (i.e. the condition that the retention time is longer than the preset retention time is satisfied), it may be determined that the variety program is an interest object and other classification data are not interest objects. For another example, if the target object respectively targeted by each operation behavior of the target user is not the predetermined type of data, for example, the target object respectively targeted by each operation behavior of the target user is a certain author, then the author is directly determined to be the interest object of the target user.
It should be noted that there are many ways to determine the interest object, for example, when the interest object is a certain author, the interest object may be determined according to a certain operation behavior of the user, for example, if the user focuses on the author of a certain video in a video application, the interest object of the user is determined to be the author. Or, if the user pays attention to 3 public numbers with the same content, and the contents of the three public numbers are all related to fitness knowledge, determining that the interest object is fitness content. In practical application, the determination mode of the object of interest can be selected according to needs, which is not listed here.
In still another implementation manner, as shown in fig. 1, step S103 determines target data corresponding to various operation behaviors of the target user according to the behavior feature information, where the target data includes at least one of the following:
determining different target data respectively corresponding to different retention durations of the target user when the target user executes various operation behaviors aiming at the interest object according to the retention duration of the target user aiming at the interest object;
according to the operation habit of the target user, determining different target data corresponding to different operation times when the target user executes the operation behavior corresponding to the operation habit;
and determining different target data respectively corresponding to different operation frequencies when the target user executes the operation behavior corresponding to the operation habit according to the operation habit of the target user.
For example, assuming that the target user's object of interest is a singing-type program and the retention time for the singing-type program is 30 minutes, it may be determined that the target data when the user watches the singing-type program for 10 minutes is data 1; the target data when the length of time for viewing the singing-type program is 20 minutes is data 2; the target data when the duration of watching the singing type program is 30 minutes is data 3, that is, different target data are provided according to different retention durations of the user on the interested object, so as to encourage the user to increase the retention duration, thereby improving the user retention rate of the target application.
For another example, because the operation habit of the target user may be determined according to the operation frequency of the same operation behavior, or may be determined according to the operation frequency of the same operation behavior, the embodiments of the present disclosure provide different target data for different operation frequencies, thereby encouraging the target user to increase the frequency or frequency of the operation behavior corresponding to the operation habit, thereby improving the activity of the target user in the target application, and further improving the user retention rate of the target application.
For example, assuming that the number of times of approval of the user on the target application reaches 10 times, 3 virtual coins are issued to the user (i.e., target data, hereinafter, the target data are all represented by the virtual coins); if the number of praise times reaches 30 times, 15 virtual coins are issued to the user; if the number of praise reaches 50, 35 virtual coins are issued to the user, and the user needs to increase the number of praise in order to acquire more virtual coins when the application is used, so that the retention time of the user in the target application is increased.
In the foregoing embodiments and corresponding implementations, the target data includes at least one of:
virtual currency; a virtual gift; virtual transaction discount information; application usage rights; a user rating.
In specific application, the type of the target data can be determined according to actual needs, for example, the target data corresponding to the operation behavior of giving the virtual gift is the virtual gift; the target data corresponding to the praise operation is virtual currency, which is not listed here.
Example two
The embodiment of the present disclosure provides a data processing apparatus based on user behavior, and as shown in fig. 2, the data processing apparatus 30 based on user behavior may include: a data acquisition module 301, a feature analysis module 302, and a data determination module 303, wherein,
the data acquisition module 301 is configured to acquire historical behavior information of a target user for a target application;
the characteristic analysis module 302 is configured to analyze the historical behavior information and determine behavior characteristic information of the target user;
the data determining module 303 is configured to determine target data corresponding to various operation behaviors of the target user according to the behavior feature information, so as to issue the target data according to the operation behavior executed by the target user for the target application.
The embodiment of the disclosure acquires the historical behavior information of the target user aiming at the target application, analyzes the historical behavior information, determines the behavior feature information of the target user, determines the target data corresponding to various operation behaviors of the target user according to the behavior feature information, plays a role in analyzing the behavior feature information of the user according to the historical behavior information of the user, namely realizes the purpose of determining the behavior feature of the user according to the historical behavior of the user, takes the behavior feature of the user as a condition for determining the target data, issues the target data according to the operation behavior executed by the target user aiming at the target application, realizes the purpose of issuing different target data to users with different behavior features, thereby stimulating the user to increase the retention time in the target application, improving the user retention rate of the target application, and solving the problem that the prior art adopts a non-differential target data determination mode, the problems that part of users cannot complete the operation conditions corresponding to the target data, so that the user experience is poor and the users are easy to lose are solved.
Further, the behavior feature information includes at least one of:
an object of interest of a target user;
the retention time of the target user for the interest object;
the operation habit of the target user.
Further, the feature analysis module comprises at least one of:
if the operation times of any operation behavior in the historical behavior information are larger than a preset operation time threshold, determining the operation habit of a target user according to the operation behavior;
if the operation frequency of any operation behavior in the historical behavior information is larger than a preset operation frequency threshold, determining the operation habit of a target user according to the operation behavior;
and if the operation time of any operation behavior in the historical behavior information is within a preset operation time period, determining the operation habit of the target user according to the operation behavior.
Further, the feature analysis module is to:
and determining the target object respectively aimed at by each operation behavior of the target user as the interest object of the target user.
Further, the feature analysis module is to:
if the target object respectively aimed at by each operation behavior of the target user is the preset type data, determining the retention time of the target user on the preset type data, and judging whether the retention time is greater than the preset retention time;
and if the retention time is longer than the preset retention time, determining the target object respectively aimed at by each operation behavior of the target user as the interest object of the target user.
Further, the data determination module comprises at least one of:
according to the interest object of the target user and the retention time length aiming at the interest object, different target data respectively corresponding to different retention time lengths of the target user when the target user executes various operation behaviors aiming at the interest object are determined;
according to the operation habit of the target user, determining different target data corresponding to different operation times when the target user executes the operation behavior corresponding to the operation habit;
and determining different target data respectively corresponding to different operation frequencies when the target user executes the operation behavior corresponding to the operation habit according to the operation habit of the target user.
Further, the target data includes at least one of:
virtual currency; a virtual gift; virtual transaction discount information; application usage rights; a user rating.
The data processing apparatus based on user behavior according to this embodiment can execute the data processing method based on user behavior according to the first embodiment of the present disclosure, and the implementation principles thereof are similar, and are not described herein again.
EXAMPLE III
Referring now to fig. 3, a schematic diagram of an electronic device (e.g., a terminal device or server of fig. 3) 400 suitable for implementing embodiments of the present disclosure is shown. The electronic devices in the embodiments of the present disclosure may include, but are not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., car navigation terminals), and the like, and fixed terminals such as digital TVs, desktop computers, and the like. The electronic device 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 disclosure.
The electronic device includes: a memory and a processor, wherein the processor may be referred to as a processing device 401 described below, and the memory may include at least one of a Read Only Memory (ROM)402, a Random Access Memory (RAM)403, and a storage device 408, which are described below:
as shown in fig. 3, electronic device 400 may include a processing device (e.g., central processing unit, graphics processor, etc.) 401 that may perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM)402 or a program loaded from a storage device 408 into a Random Access Memory (RAM) 403. In the RAM 403, various programs and data necessary for the operation of the electronic apparatus 400 are also stored. The processing device 401, the ROM 402, and the RAM 403 are connected to each other via a bus 404. An input/output (I/O) interface 405 is also connected to bus 404.
Generally, the following devices may be connected to the I/O interface 405: input devices 406 including, for example, a touch screen, touch pad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; an output device 407 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; storage 408 including, for example, tape, hard disk, etc.; and a communication device 409. The communication means 409 may allow the electronic device 400 to communicate wirelessly or by wire with other devices to exchange data. While fig. 3 illustrates an electronic device 400 having various means, it is to be understood that not all illustrated means are required to be implemented or provided. More or fewer devices may alternatively be implemented or provided.
In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program carried on a non-transitory computer readable medium, the computer program containing program code for performing the method illustrated by the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication device 409, or from the storage device 408, or from the ROM 402. The computer program performs the above-described functions defined in the methods of the embodiments of the present disclosure when executed by the processing device 401.
It should be noted that the computer readable medium in the present disclosure can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer 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 of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, 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. In the present disclosure, a computer 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. In contrast, in the present disclosure, a computer readable signal medium may comprise a propagated data signal with computer readable program code embodied therein, either 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 computer readable signal medium may also be any computer readable medium that is not a computer 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 computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
In some embodiments, the clients, servers may communicate using any currently known or future developed network Protocol, such as HTTP (HyperText Transfer Protocol), and may interconnect with any form or medium of digital data communication (e.g., a communications network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), the Internet (e.g., the Internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed network.
The computer readable medium may be embodied in the electronic device; or may exist separately without being assembled into the electronic device.
The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: acquiring historical behavior information of a target user aiming at a target application; analyzing the historical behavior information to determine the behavior characteristic information of the target user; and determining target data corresponding to various operation behaviors of the target user according to the behavior characteristic information, and issuing the target data according to the operation behaviors executed by the target user aiming at the target application.
Computer program code for carrying out operations for the present disclosure may be written in any combination of one or more programming languages, including but not limited to an object oriented programming language such as Java, Smalltalk, C + +, 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 computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The modules or units described in the embodiments of the present disclosure may be implemented by software or hardware. The name of the module or unit does not in some cases constitute a limitation on the unit itself, and for example, the first obtaining unit may also be described as a "unit that obtains historical behavior information of the target user for the target application".
The functions described herein above may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), systems on a chip (SOCs), Complex Programmable Logic Devices (CPLDs), and the like.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable medium would include an electrical connection based on one or more wires, a portable computer diskette, 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.
Example a provides a method of data processing based on user behavior, in accordance with one or more embodiments of the present disclosure, comprising:
acquiring historical behavior information of a target user aiming at a target application;
analyzing the historical behavior information to determine behavior feature information of the target user;
and determining target data corresponding to various operation behaviors of the target user according to the behavior feature information, and issuing the target data according to the operation behaviors executed by the target user aiming at the target application.
In accordance with one or more embodiments of the present disclosure, the behavior feature information in example a includes at least one of:
an object of interest of a target user;
the retention time of the target user for the interest object;
the operation habit of the target user.
Example a, according to one or more embodiments of the present disclosure, analyzes historical behavior information to determine an operating habit of a target user, including at least one of:
if the operation times of any operation behavior in the historical behavior information are larger than a preset operation time threshold, determining the operation habit of a target user according to the operation behavior;
if the operation frequency of any operation behavior in the historical behavior information is larger than a preset operation frequency threshold, determining the operation habit of a target user according to the operation behavior;
and if the operation time of any operation behavior in the historical behavior information is within a preset operation time period, determining the operation habit of the target user according to the operation behavior.
Example a, according to one or more embodiments of the present disclosure, analyzing historical behavior information to determine an object of interest of a target user, includes:
and determining the target object respectively aimed at by each operation behavior of the target user as the interest object of the target user.
According to one or more embodiments of the present disclosure, example a determines, as an object of interest of a target user, a target object to which respective operation behaviors of the target user are respectively directed, includes:
if the target object respectively aimed at by each operation behavior of the target user is the preset type data, determining the retention time of the target user on the preset type data, and judging whether the retention time is greater than the preset retention time;
and if the retention time is longer than the preset retention time, determining the target object respectively aimed at by each operation behavior of the target user as the interest object of the target user.
According to one or more embodiments of the disclosure, example a determines target data corresponding to various operation behaviors of a target user according to the behavior feature information, including at least one of:
determining different target data respectively corresponding to different persistence durations of the target user when the target user executes various operation behaviors aiming at the interest object according to the persistence duration of the target user aiming at the interest object;
according to the operation habit of the target user, determining different target data corresponding to different operation times when the target user executes the operation behavior corresponding to the operation habit;
and determining different target data respectively corresponding to different operation frequencies when the target user executes the operation behavior corresponding to the operation habit according to the operation habit of the target user.
In accordance with one or more embodiments of the present disclosure, the target data in example a includes at least one of:
virtual currency; a virtual gift; virtual transaction discount information; application usage rights; a user rating.
Example B provides, in accordance with one or more embodiments of the present disclosure, a data processing apparatus based on user behavior, comprising:
the data acquisition module is used for acquiring historical behavior information of a target user aiming at a target application;
the characteristic analysis module is used for analyzing the historical behavior information and determining the behavior characteristic information of the target user;
and the data determining module is used for determining target data corresponding to various operation behaviors of the target user according to the behavior characteristic information so as to release the target data according to the operation behaviors executed by the target user aiming at the target application.
In accordance with one or more embodiments of the present disclosure, the behavior feature information in example B includes at least one of:
an object of interest of a target user;
the retention time of the target user for the interest object;
the operation habit of the target user.
In accordance with one or more embodiments of the present disclosure, the feature analysis module in example B includes at least one of:
if the operation times of any operation behavior in the historical behavior information are larger than a preset operation time threshold, determining the operation habit of a target user according to the operation behavior;
if the operation frequency of any operation behavior in the historical behavior information is larger than a preset operation frequency threshold, determining the operation habit of a target user according to the operation behavior;
and if the operation time of any operation behavior in the historical behavior information is within a preset operation time period, determining the operation habit of the target user according to the operation behavior.
In accordance with one or more embodiments of the present disclosure, the feature analysis module in example B is to:
and determining the target object respectively aimed at by each operation behavior of the target user as the interest object of the target user.
In accordance with one or more embodiments of the present disclosure, the feature analysis module in example B is to:
if the target object respectively aimed at by each operation behavior of the target user is the preset type data, determining the retention time of the target user on the preset type data, and judging whether the retention time is greater than the preset retention time;
and if the retention time is longer than the preset retention time, determining the target object respectively aimed at by each operation behavior of the target user as the interest object of the target user.
In accordance with one or more embodiments of the present disclosure, the data determination module in example B comprises at least one of:
according to the interest object of the target user and the retention time length aiming at the interest object, different target data respectively corresponding to different retention time lengths of the target user when the target user executes various operation behaviors aiming at the interest object are determined;
according to the operation habit of the target user, determining different target data corresponding to different operation times when the target user executes the operation behavior corresponding to the operation habit;
and determining different target data respectively corresponding to different operation frequencies when the target user executes the operation behavior corresponding to the operation habit according to the operation habit of the target user.
In accordance with one or more embodiments of the present disclosure, the target data in example B includes at least one of:
virtual currency; a virtual gift; virtual transaction discount information; application usage rights; a user rating.
The foregoing description is only exemplary of the preferred embodiments of the disclosure and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the disclosure herein is not limited to the particular combination of features described above, but also encompasses other embodiments in which any combination of the features described above or their equivalents does not depart from the spirit of the disclosure. For example, the above features and (but not limited to) the features disclosed in this disclosure having similar functions are replaced with each other to form the technical solution.
Further, while operations are depicted in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order. Under certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while several specific implementation details are included in the above discussion, these should not be construed as limitations on the scope of the disclosure. Certain features that are described in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable subcombination.
Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims.
Claims (9)
1. A data processing method based on user behavior is characterized by comprising the following steps:
acquiring historical behavior information of a target user aiming at a target application;
analyzing the historical behavior information to determine behavior feature information of the target user;
determining target data corresponding to various operation behaviors of the target user according to the behavior feature information, and issuing the target data according to the operation behaviors executed by the target user aiming at the target application;
the behavior characteristic information comprises the operation habits of the target user;
the determining target data corresponding to various operation behaviors of the target user according to the behavior feature information includes: according to the operation habit of the target user, determining different target data corresponding to different operation times when the target user executes the operation behavior corresponding to the operation habit;
the target data includes at least one of:
virtual currency; a virtual gift; virtual transaction discount information; application usage rights; a user rating.
2. The method of claim 1, wherein the behavior feature information further comprises at least one of:
an object of interest of the target user;
a retention time duration for the target user for the object of interest.
3. The method of claim 2, wherein analyzing the historical behavior information to determine the operating habits of the target user comprises at least one of:
if the operation times of any operation behavior in the historical behavior information are larger than a preset operation time threshold, determining the operation habit of the target user according to the operation behavior;
if the operation frequency of any operation behavior in the historical behavior information is larger than a preset operation frequency threshold, determining the operation habit of the target user according to the operation behavior;
and if the operation time of any operation behavior in the historical behavior information is within a preset operation time period, determining the operation habit of the target user according to the operation behavior.
4. The method of claim 2, wherein analyzing the historical behavior information to determine the object of interest of the target user comprises:
and determining the target object respectively aimed at by each operation behavior of the target user as the interest object of the target user.
5. The method according to claim 4, wherein determining the target object respectively targeted by the operation behaviors of the target user as the interest object of the target user comprises:
if the target object respectively aimed at by each operation behavior of the target user is the preset type data, determining the retention time of the target user on the preset type data, and judging whether the retention time is greater than the preset retention time;
and if the retention time is longer than the preset retention time, determining the target object respectively aimed at by each operation behavior of the target user as the interest object of the target user.
6. The method according to claim 2, wherein the determining target data corresponding to various operation behaviors of the target user according to the behavior feature information comprises at least one of the following:
determining different target data respectively corresponding to different persistence durations of the target user when the target user executes various operation behaviors aiming at the interest object according to the persistence duration of the target user aiming at the interest object;
and determining different target data respectively corresponding to different operation frequencies when the target user executes the operation behavior corresponding to the operation habit according to the operation habit of the target user.
7. A data processing apparatus based on user behavior, comprising:
the data acquisition module is used for acquiring historical behavior information of a target user aiming at a target application;
the characteristic analysis module is used for analyzing the historical behavior information and determining the behavior characteristic information of the target user;
the data determining module is used for determining target data corresponding to various operation behaviors of the target user according to the behavior characteristic information so as to issue the target data according to the operation behaviors executed by the target user aiming at the target application;
the behavior characteristic information comprises the operation habits of the target user;
the determining target data corresponding to various operation behaviors of the target user according to the behavior feature information includes: according to the operation habit of the target user, determining different target data corresponding to different operation times when the target user executes the operation behavior corresponding to the operation habit;
the target data includes at least one of:
virtual currency; a virtual gift; virtual transaction discount information; application usage rights; a user rating.
8. An electronic device, comprising:
one or more processors;
a memory;
one or more applications, wherein the one or more applications are stored in the memory and configured to be executed by the one or more processors, the one or more programs configured to: -executing the user behavior based data processing method according to any of claims 1-6.
9. A computer-readable medium, on which a computer program is stored, which program, when being executed by a processor, is adapted to carry out the method for user behavior-based data processing according to any one of claims 1 to 6.
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| CN110827087B (en) * | 2019-11-07 | 2022-09-30 | 北京字节跳动网络技术有限公司 | Virtual article distribution method and device, electronic equipment and storage medium |
| CN111401969B (en) * | 2020-04-02 | 2024-02-20 | 北京达佳互联信息技术有限公司 | Method, device, server and storage medium for improving user retention |
| CN111709785B (en) * | 2020-06-18 | 2023-08-22 | 抖音视界有限公司 | Method, apparatus, device and medium for determining user retention time |
| CN112712391A (en) * | 2020-12-31 | 2021-04-27 | 北京嘀嘀无限科技发展有限公司 | Service pushing method and device, electronic equipment and storage medium |
| CN112559898B (en) * | 2021-02-24 | 2021-07-27 | 北京每日优鲜电子商务有限公司 | Item information sending method, item information sending device, electronic equipment and computer readable medium |
| CN113094377A (en) * | 2021-03-02 | 2021-07-09 | 游艺星际(北京)科技有限公司 | Content list updating method and device |
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