CN112215667B - User behavior prediction method and device - Google Patents

User behavior prediction method and device Download PDF

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CN112215667B
CN112215667B CN202011294083.1A CN202011294083A CN112215667B CN 112215667 B CN112215667 B CN 112215667B CN 202011294083 A CN202011294083 A CN 202011294083A CN 112215667 B CN112215667 B CN 112215667B
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CN112215667A (en
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邓煜
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China United Network Communications Group Co Ltd
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Abstract

The application provides a user behavior prediction method and device, relates to the technical field of Internet, and can provide reasonable personalized service for users based on a prediction result of the user behavior prediction method. The method comprises the following steps: and the user behavior prediction device determines a historical user behavior pattern sequence of the target user under the condition of receiving a prediction request comprising the identity of the target user sent by the terminal. And then, the user behavior prediction device determines a target user behavior pattern sequence according to the historical user behavior pattern sequence, wherein the target user behavior pattern sequence is used for representing the mobility intensity degree and the use condition of the data flow of the target user in a first preset time period. Then, the user behavior prediction device determines a user behavior prediction result for the target user according to the target user behavior pattern sequence and the user position information of the target user in a preset historical time period.

Description

User behavior prediction method and device
Technical Field
The present application relates to the field of internet technologies, and in particular, to a method and an apparatus for predicting user behavior.
Background
At present, daily life and work of people are increasingly dependent on the internet. User behavior prediction can be performed based on the big data of increasingly mobile users, and personalized services can be provided for the users according to the prediction results, including the services of recommending consumer places, parking lots, recommending mobile packages and the like for the users.
However, the existing user behavior prediction method generally performs user behavior prediction directly based on user data from an operator, and personalized services provided for a user according to the prediction result often do not meet the user requirements. Therefore, how to provide a user behavior prediction method, which can provide reasonable personalized services for users, is a problem to be solved.
Disclosure of Invention
The application provides a user behavior prediction method and device, and based on the prediction result of the user behavior prediction method, reasonable personalized service can be provided for a user.
In order to achieve the above purpose, the present application adopts the following technical scheme:
in a first aspect, the present application provides a method for predicting user behavior, including: and the user behavior prediction device determines a historical user behavior pattern sequence of the target user under the condition of receiving a prediction request comprising the identity of the target user sent by the terminal. The historical user behavior pattern sequence is used for representing the mobility intensity degree and the service condition of data flow of the target user in a preset historical time period. And then, the user behavior prediction device determines a target user behavior pattern sequence according to the historical user behavior pattern sequence, wherein the target user behavior pattern sequence is used for representing the mobility intensity degree and the use condition of the data flow of the target user in a first preset time period. Then, the user behavior prediction device determines a user behavior prediction result for the target user according to the target user behavior pattern sequence and the user position information of the target user in a preset historical time period.
In the user behavior prediction method provided by the application, the user behavior prediction device can determine the target user behavior pattern sequence according to the historical user behavior pattern sequence of the target user. The mobility degree of the target user in the first preset time period and the service condition of the data flow can be represented by the target user behavior pattern sequence, so that the mobility of the target user behavior and the service condition of the flow can be predicted. And then, combining the user position information of the target user in the preset historical time period, and predicting the user position information of the target user in the first preset time period. After obtaining the prediction result of the user location information, mobility, and traffic usage of the target user, personalized services (such as recommending consumer location, parking lot, and recommending mobile packages) may be provided for the target user with respect to the prediction result.
Optionally, in one possible design manner, before the determining the historical user behavior pattern sequence of the target user, the user behavior prediction method provided in the first aspect of the present application may further include: acquiring user behavior parameters of each user in a target user group; the target user group comprises target users; determining a user behavior mode sequence of each user in a preset historical time period according to the user behavior parameters of each user; and generating a unique identity for each user, and storing the mapping relation between the user behavior pattern sequence of each user in a preset historical time period and the identity of each user.
Wherein, the user behavior parameters at least comprise: user location information, user access base station information, user access wireless network information, and user data traffic information within a preset history period.
Correspondingly, the above-mentioned "determining the historical user behavior pattern sequence of the target user" may include: and determining a historical user behavior pattern sequence of the target user according to the identity mark and the mapping relation of the target user.
Alternatively, in another possible design manner, the "obtaining the user behavior parameter of each user in the target user group" may include: acquiring user position information of each user in a preset historical time period based on a first preset rule; based on a second preset rule, acquiring user access base station information and user data flow information of each user in a preset historical time period; and acquiring the user access wireless network information of each user in a preset historical time period based on a third preset rule.
Alternatively, in another possible design manner, the determining the user behavior prediction result for the target user according to the target user behavior pattern sequence and the user behavior parameter of the target user may include: determining the user position information of the target user in a first preset time period according to the target user behavior pattern sequence and the user position information of the target user in the preset historical time period; determining user access base station information and user access wireless network information of a target user in a first preset time period according to the user position information of the target user in the first preset time period; and determining the user data flow information of the target user in a first preset time period.
Alternatively, in another possible design manner, the determining the target user behavior pattern sequence according to the historical user behavior pattern sequence may include: establishing a hidden Markov model according to the historical user behavior pattern sequence; determining a target user behavior pattern sequence according to the hidden Markov model.
In a second aspect, the present application provides a user behavior prediction apparatus, including: the device comprises a first determining module, a second determining module and a third determining module.
The first determining module is used for determining a historical user behavior pattern sequence of the target user under the condition that a prediction request sent by the terminal is received; the prediction request comprises the identity of the target user; the historical user behavior pattern sequence is used for representing the mobility intensity degree and the service condition of data flow of the target user in a preset historical time period.
The second determining module is used for determining a target user behavior pattern sequence according to the historical user behavior pattern sequence determined by the first determining module; the target user behavior pattern sequence is used for representing the mobility intensity degree and the service condition of data flow of the target user in a first preset time period.
The third determining module is used for determining a user behavior prediction result for the target user according to the user behavior parameters of the target user and the target user behavior pattern sequence determined by the second determining module; the user behavior parameters include at least: user location information, user access base station information, user access wireless network information, and user data traffic information within a preset history period.
In a third aspect, the present application provides a user behavior prediction apparatus, including a processor, where the processor is configured to couple to a memory, and read and execute instructions in the memory, so as to implement the user behavior prediction method provided in the first aspect.
Optionally, the user behavior prediction device may further comprise a memory for storing program instructions and data of the user behavior prediction device. Further optionally, the user behavior prediction device may further comprise a transceiver for performing the step of transceiving data, signaling or information, e.g. obtaining user behavior parameters of each user in the target user group, under control of a processor of the user behavior prediction device.
Alternatively, the user behavior prediction device may be a server, or may be a part of a device in the server, for example, may be a chip system in the server. The chip system is configured to support the user behavior prediction means to implement the functions involved in the first aspect, e.g. to receive, transmit or process data and/or information involved in the above-mentioned user behavior prediction method. The chip system includes a chip, and may also include other discrete devices or circuit structures.
In a fourth aspect, the present application provides a computer readable storage medium having instructions stored therein which, when executed by a computer, implement the user behavior prediction method as provided in the first aspect.
In a fifth aspect, the present application provides a computer program product comprising computer instructions which, when run on a computer, cause the computer to perform the user behavior prediction method according to the first aspect.
It should be noted that the above-mentioned computer instructions may be stored in whole or in part on a computer-readable storage medium. The computer readable storage medium may be packaged together with the processor of the user behavior prediction device or may be packaged separately from the processor of the user behavior prediction device, which is not limited in this application.
The description of the second, third, fourth and fifth aspects of the present application may refer to the detailed description of the first aspect; also, the advantageous effects described in the second aspect, the third aspect, the fourth aspect, and the fifth aspect may refer to the advantageous effect analysis of the first aspect, and are not described herein.
In the present application, the names of the above-mentioned user behavior prediction apparatuses do not constitute limitations on the devices or function modules themselves, and in actual implementations, these devices or function modules may appear under other names. Insofar as the function of each device or function module is similar to the present invention, it is within the scope of the claims of the present application and the equivalents thereof.
These and other aspects of the present application will be more readily apparent from the following description.
Drawings
Fig. 1 is a schematic architecture diagram of a user behavior prediction system according to an embodiment of the present application;
fig. 2 is a flow chart of a user behavior prediction method provided in an embodiment of the present application;
FIG. 3 is a flowchart illustrating another method for predicting user behavior according to an embodiment of the present disclosure;
FIG. 4 is a flowchart illustrating a method for predicting user behavior according to an embodiment of the present disclosure;
FIG. 5 is a flowchart illustrating a method for predicting user behavior according to an embodiment of the present disclosure;
FIG. 6 is a flowchart illustrating a method for predicting user behavior according to an embodiment of the present disclosure;
fig. 7 is a schematic structural diagram of a user behavior prediction apparatus according to an embodiment of the present application;
fig. 8 is a schematic structural diagram of another user behavior prediction apparatus according to an embodiment of the present application.
Detailed Description
The following describes a user behavior prediction method and device provided by the embodiment of the application in detail with reference to the accompanying drawings.
The term "and/or" is herein merely an association relationship describing an associated object, meaning that there may be three relationships, e.g., a and/or B, may represent: a exists alone, A and B exist together, and B exists alone.
The terms "first" and "second" and the like in the description and in the drawings are used for distinguishing between different objects or for distinguishing between different processes of the same object and not for describing a particular sequential order of objects.
Furthermore, references to the terms "comprising" and "having" and any variations thereof in the description of the present application are intended to cover a non-exclusive inclusion. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those listed but may optionally include other steps or elements not listed or inherent to such process, method, article, or apparatus.
It should be noted that, in the embodiments of the present application, words such as "exemplary" or "such as" are used to mean serving as an example, instance, or illustration. Any embodiment or design described herein as "exemplary" or "for example" should not be construed as preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary" or "such as" is intended to present related concepts in a concrete fashion.
In the description of the present application, unless otherwise indicated, the meaning of "a plurality" means two or more.
At present, daily life and work of people are increasingly dependent on the internet. User behavior prediction can be performed based on the big data of increasingly mobile users, and personalized services can be provided for the users according to the prediction results, including the services of recommending consumption places, parking lots, mobile packages and the like for the users.
However, the existing user behavior prediction method generally performs user behavior prediction directly based on user data from an operator, and personalized services provided for a user according to the prediction result often do not meet the user requirements. Therefore, how to provide a user behavior prediction method, which can provide reasonable personalized services for users, is a problem to be solved.
Aiming at the problems in the prior art, the embodiment of the application provides a user behavior prediction method. The method can firstly determine a target user behavior pattern sequence according to a historical user behavior pattern sequence, and then determine a user behavior prediction result for the target user by combining the user position information of the target user in a preset historical time period. Based on the prediction result, personalized services (such as services of recommending a consumption place, a parking lot, recommending a mobile package, and the like) can be provided for the target user.
The user behavior prediction method provided by the embodiment of the present application may be applied to a system architecture shown in fig. 1, where the system architecture includes a server 01 and a terminal 02.
The server 01 may be one server or may be a server cluster formed by a plurality of servers, which is not limited in the embodiment of the present application.
By way of example, server 01 may be comprised of two servers, one being a database server and the other being an application server.
In one possible implementation, a database server may include a data access module and a data storage module. The data access module is used for acquiring data; and the data storage module is used for storing the data acquired by the data access module. For example, the data access module may acquire user location information of each user in a preset history period based on a first preset rule, and then send the acquired user location information of each user in the preset history period to the data storage module, where the user location information is stored by the data storage module.
In one possible implementation, the application server may include a behavior modeling algorithm module and a movement prediction algorithm module. Illustratively, the behavior modeling algorithm module may be configured to build a hidden Markov model from a sequence of historical user behavior patterns. The mobile prediction algorithm module may be configured to determine a user behavior prediction result for the target user according to the target user behavior pattern sequence and user position information of the target user within a preset historical time period.
The terminal 02 may be a mobile phone, a tablet computer, a desktop computer, a laptop computer, a notebook computer, an ultra-mobile personal computer (UMPC), a handheld computer, a netbook, a personal digital assistant (personal digital assistant, PDA), a wearable electronic device, a virtual reality device, or other different types of terminals connected to the server 01.
In one possible implementation, a user behavior prediction Application (APP) may be installed on the terminal 02. For example, the user may perform a triggering operation (such as a clicking operation, a sliding operation, or a long press operation) on the APP interface of the terminal 02, and trigger the terminal 02 to send a prediction request to the server 01.
Based on the above system architecture, as shown in fig. 2, the embodiment of the present application provides a method for predicting user behavior, where the method may be applied to a user behavior prediction device, and the user behavior prediction device may be a server 01 in the system architecture shown in fig. 1. The method comprises S101-S103:
s101, the user behavior prediction device determines a historical user behavior pattern sequence of a target user under the condition that a prediction request sent by a terminal is received.
The prediction request comprises an identity mark of the target user, wherein the identity mark is used for uniquely indicating the identity information of the target user.
The historical user behavior pattern sequence is used for representing the mobility intensity degree and the service condition of data flow of the target user in a preset historical time period.
The preset history period may be a period set in advance by a person before the prediction request is received.
Optionally, as shown in fig. 3, before step S101, the method for predicting user behavior provided in the embodiment of the present application may further include S201 to S203:
s201, the user behavior prediction device acquires the user behavior parameters of each user in the target user group.
The target user group comprises target users, and the user behavior parameters at least comprise: user location information, user access base station information, user access wireless network (WIFI) information, and user data traffic information within a preset historical period of time.
Optionally, as shown in fig. 4, step S201 may include S2011-S2013:
s2011, the user behavior prediction device acquires user position information of each user in a preset historical time period based on a first preset rule.
In one possible implementation manner, the terminal carries an APP capable of acquiring user location information in real time, and the user behavior prediction device may acquire user location information of the user in a preset historical period from the APP.
Of course, in practical applications, the user behavior prediction device may also obtain the user position information of each user in the preset historical time period according to other manners, which is not limited in the embodiment of the present application.
S2012, the user behavior prediction device acquires user access base station information and user data flow information of each user in a preset historical time period based on a second preset rule.
In one possible implementation manner, a data transmission interface is provided between the user behavior prediction device and the server of the operator, and the user behavior prediction device may acquire user access base station information and user data traffic information of the user in a preset historical time period based on the data transmission interface.
In another possible implementation, when the terminal accesses the base station, or when the base station accessed by the terminal changes, the terminal may report the accessed base station information to the user behavior prediction device. In addition, the data traffic used by the terminal access base station is updated to the user behavior prediction device in real time. The user behavior prediction device can determine user access base station information and user data flow information of a user in a preset historical time period according to information reported by the terminal.
Of course, in practical application, the user behavior prediction device may further obtain the user access base station information and the user data traffic information of each user in the preset historical time period according to other modes, which is not limited in the embodiment of the present application.
S2013, the user behavior prediction device acquires the user access wireless network information of each user in a preset historical time period based on a third preset rule.
In one possible implementation, when the terminal accesses the wireless network, or when the wireless network accessed by the terminal changes, the terminal may report the accessed wireless network information to the user behavior prediction device. The user behavior prediction device can determine the user access wireless network information of the user in a preset historical time period according to the access wireless network information reported by the terminal.
Of course, in practical application, the user behavior prediction device may also obtain the user access wireless network information of each user in the preset historical time period according to other modes, which is not limited in the embodiment of the present application.
It can be appreciated that the sequence of steps S2011 to S2013 is not limited in the embodiment of the present application, and three steps from step S2011 to step S2013 may be performed simultaneously.
S202, the user behavior prediction device determines a user behavior mode sequence of each user in a preset historical time period according to the user behavior parameters of each user.
The user behavior prediction device can analyze the obtained user position information, user access base station information, user access wireless network information and user data flow information of the target user in a preset historical time period.
In one possible implementation, the preset history period may be divided into a plurality of history periods. Wherein the second preset time period is any one of a plurality of history time periods. If the moving distance of the user position of the target user in the second preset time period exceeds the preset distance, or the switching frequency of the target user in the second preset time period when the user accesses the base station exceeds the first preset frequency, or the switching frequency of the target user in the second preset time period when the user accesses the wireless network exceeds the second preset frequency, the user behavior prediction device can determine that the mobility of the target user in the second preset time period is stronger. If the user data traffic of the target user in the second preset time period exceeds the preset traffic, the user behavior prediction device can determine that the target user uses more data traffic in the second preset time period.
The second preset time period, the preset distance, the first preset times, the second preset times and the preset flow are all parameters determined in advance.
If the mobility of the user is strong, the mobility of the user is weak, the data flow used by the user is more, and the data flow used by the user is less. If the preset history period is divided into 5 history periods, for example, the sequence of user behavior patterns of the target user in the preset history period may be Ab, ab, ab, ab, ab. It can be seen that the target user moves strongly and uses less data traffic during the preset history period. For example, the sequence of user behavior patterns of the target user over the preset historical period may be Ab, ab, ab, ab, ab. It can be seen that the target user moves from strong to weak within the preset history period, and the data traffic used is less.
In one possible implementation, the user behavior patterns may include three categories of patterns, a continuous moving pattern, a long-term stationary pattern, and a random pattern, respectively. The continuous moving mode may include a driving mode and a passenger mode, the long-term stationary mode may include an operating mode and an entertainment mode, and the random mode may include a busy mode and an idle mode. And the driving mode is used for representing that the mobility of the user is strong and the used data flow is less. The passenger mode is used for representing that the mobility of the user is strong and the used data traffic is more. The operation mode is used for representing that the mobility of the user is weak and the used data traffic is small. Entertainment mode is used to characterize the user's mobility as weak, using more data traffic. The busy mode is used to characterize the mobility instability of the user with less data traffic. The idle mode is used for representing unstable mobility of the user and more data traffic is used.
For example, if the sequence of the user behavior patterns of the target user in the preset history period is Ab, ab, ab, ab, ab, the user behavior prediction apparatus may determine that the target user belongs to the driving pattern in the preset history period.
S203, the user behavior prediction device generates a unique identity for each user, and stores the mapping relation between the user behavior pattern sequence of each user in a preset historical time period and the identity of each user.
Alternatively, as shown in fig. 5, step S101 may be replaced with S1011:
s1011, the user behavior prediction device determines a historical user behavior pattern sequence of the target user according to the identity of the target user and the mapping relation.
S102, the user behavior prediction device determines a target user behavior pattern sequence according to the historical user behavior pattern sequence.
The target user behavior pattern sequence is used for representing the mobility intensity degree and the service condition of data flow of a target user in a first preset time period.
In one possible implementation, the user behavior prediction means may build a hidden markov model from the historical user behavior pattern sequence and then determine the target user behavior pattern sequence from the hidden markov model. The input of the hidden Markov model can be a historical user behavior pattern sequence, and the output of the hidden Markov model is a target user behavior pattern sequence. The method for specifically building the hidden markov model may refer to the related description in the prior art, and will not be described herein.
S103, the user behavior prediction device determines a user behavior prediction result for the target user according to the target user behavior pattern sequence and the user position information of the target user in a preset historical time period.
Alternatively, as shown in fig. 6, step S103 may be replaced with S1031-S1033:
s1031, the user behavior prediction device determines the user position information of the target user in a first preset time period according to the target user behavior pattern sequence and the user position information of the target user in the preset historical time period.
In one possible implementation manner, the user behavior prediction device may establish a linear prediction model for the user position information of the target user in the preset historical time period by using a least square method according to the target user behavior pattern sequence, so as to obtain the user position information of the target user in the first preset time period. Illustratively, the linear equation in the linear prediction model may be: x=k1×t+b1, and y=k2×t+b2. Where x represents the longitude of the location where the user is located, y represents the latitude of the location where the user is located, and t represents time. And calculating errors between the calculated values of x and y and the actual values based on the linear equation, minimizing the error values, and solving four parameters of k1, b1, k2 and b2. Then, the user position information of the target user within the first preset time period can be predicted based on the linear equation.
S1032, the user behavior prediction device determines the user access base station information and the user access wireless network information of the target user in the first preset time period according to the user position information of the target user in the first preset time period.
In one possible implementation manner, the user behavior prediction device may determine, according to the base station information table and the wireless network information table, user access base station information and user access wireless network information of the target user in a first preset time period.
S1033, the user behavior prediction device determines user data flow information of the target user in a first preset time period.
The user behavior prediction device determines the user access base station information and the user access wireless network information of the target user in a first preset time period, and the user data flow information of the target user in the first preset time period after the user position information of the target user in the first preset time period is determined.
After the user behavior prediction device determines the user behavior prediction result for the target user, personalized service can be provided for the target user according to the prediction result. For example, the user behavior prediction device may recommend a mobile package to the target user after determining the user data traffic information of the target user within the first preset time period. For another example, the user behavior prediction device determines that the sequence of the user behavior patterns of the target user in the first preset time period is: ab. Ab, the user behavior prediction apparatus may determine that the target user belongs to the driving mode within the preset history period, and may recommend a parking lot or a gas station, etc. to the target user.
In the user behavior prediction method provided by the embodiment of the application, the user behavior prediction device can determine the target user behavior pattern sequence according to the historical user behavior pattern sequence of the target user. The mobility degree of the target user in the first preset time period and the service condition of the data flow can be represented by the target user behavior pattern sequence, so that the mobility of the target user behavior and the service condition of the flow can be predicted. And then, combining the user position information of the target user in the preset historical time period, and predicting the user position information of the target user in the first preset time period. After obtaining the prediction result of the user location information, mobility, and traffic usage of the target user, the user behavior prediction apparatus may provide personalized services (such as services of recommending a consumption place, a parking lot, and recommending a mobile package) for the target user with respect to the prediction result.
As shown in fig. 7, the embodiment of the present application further provides a user behavior prediction apparatus, which may be a server in the user behavior prediction system shown in fig. 1, where the user behavior prediction apparatus includes: the first determination module 31, the second determination module 32, and the third determination module 33.
Wherein the first determining module 31 performs S101 in the above-described method embodiment, the second determining module 32 performs S102 in the above-described method embodiment, and the third determining module 33 performs S103 in the above-described method embodiment.
Specifically, the first determining module 31 is configured to determine, when a prediction request sent by the terminal is received, a historical user behavior pattern sequence of the target user. The prediction request comprises an identity of a target user, and the historical user behavior pattern sequence is used for representing the mobility intensity degree and the use condition of data flow of the target user in a preset historical time period.
The second determining module 32 is configured to determine a target user behavior pattern sequence according to the historical user behavior pattern sequence determined by the first determining module 31. The target user behavior pattern sequence is used for representing the mobility intensity degree and the service condition of data flow of a target user in a first preset time period.
And a third determining module 33, configured to determine a user behavior prediction result for the target user according to the user location information of the target user in the preset historical time period and the target user behavior pattern sequence determined by the second determining module 32.
Optionally, the user behavior prediction apparatus provided in the embodiment of the present application may further include:
the acquisition module is used for acquiring the user behavior parameters of each user in the target user group; the target user group comprises target users; the user behavior parameters include at least: user position information, user access base station information, user access wireless network information and user data flow information in a preset historical time period;
a fourth determining module, configured to determine, according to the user behavior parameters of each user acquired by the acquiring module, a user behavior pattern sequence of each user in a preset historical time period;
the processing module is used for generating a unique identity for each user;
the storage module is used for storing the mapping relation between the user behavior pattern sequence of each user in the preset historical time period, which is determined by the fourth determination module, and the identity of each user generated by the processing module;
the first determining module 31 is specifically configured to: and determining a historical user behavior pattern sequence of the target user according to the identity of the target user and the mapping relation stored by the storage module.
Optionally, the obtaining module is specifically configured to:
acquiring user position information of each user in a preset historical time period based on a first preset rule;
Based on a second preset rule, acquiring user access base station information and user data flow information of each user in a preset historical time period;
and acquiring the user access wireless network information of each user in a preset historical time period based on a third preset rule.
Optionally, the third determining module 33 is specifically configured to:
determining the user position information of the target user in a first preset time period according to the target user behavior pattern sequence determined by the second determining module 32 and the user position information of the target user in the preset historical time period acquired by the acquiring module;
determining user access base station information and user access wireless network information of a target user in a first preset time period according to the user position information of the target user in the first preset time period;
and determining the user data flow information of the target user in a first preset time period.
Optionally, the second determining module 32 is specifically configured to:
establishing a hidden Markov model according to the historical user behavior pattern sequence determined by the first determining module 31;
determining a target user behavior pattern sequence according to the hidden Markov model.
Optionally, the storage module is further configured to store program codes of the user behavior prediction device, and the like.
As shown in fig. 8, the embodiment of the present application further provides a user behavior prediction apparatus, including a memory 41, a processor 42, a bus 43, and a communication interface 44; the memory 41 is used for storing computer-executable instructions, and the processor 42 is connected with the memory 41 through the bus 43; when the user behavior prediction apparatus is operated, the processor 42 executes computer-executable instructions stored in the memory 41 to cause the user behavior prediction apparatus to perform the user behavior prediction method as provided in the above-described embodiment.
In a particular implementation, as one embodiment, the processors 42 (42-1 and 42-2) may include one or more central processing units (central processing unit, CPU), such as CPU0 and CPU1 shown in FIG. 8. And as one example, the user behavior prediction apparatus may include a plurality of processors 42, such as processor 42-1 and processor 42-2 shown in fig. 8. Each of these processors 42 may be a single-core processor (single-CPU) or a multi-core processor (multi-CPU). The processor 42 herein may refer to one or more devices, circuits, and/or processing cores for processing data (e.g., computer program instructions).
The memory 41 may be, but is not limited to, a read-only memory 41 (ROM) or other type of static storage device that can store static information and instructions, a random access memory (random access memory, RAM) or other type of dynamic storage device that can store information and instructions, or an electrically erasable programmable read-only memory (electrically erasable programmable read-only memory, EEPROM), a compact disc read-only memory (compact disc read-only memory) or other optical disc storage, optical disc storage (including compact disc, laser disc, optical disc, digital versatile disc, blu-ray disc, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer. The memory 41 may be stand alone and be coupled to the processor 42 via a bus 43. Memory 41 may also be integrated with processor 42.
In a specific implementation, the memory 41 is used for storing data in the application and computer-executable instructions corresponding to executing a software program of the application. The processor 42 may predict various functions of the apparatus by running or executing a software program stored in the memory 41 and invoking data stored in the memory 41.
The communication interface 44 uses any transceiver-like device for communicating with other devices or communication networks, such as a control system, a radio access network (radio access network, RAN), a wireless local area network (wireless local area networks, WLAN), etc. The communication interface 44 may include a receiving unit to implement a receiving function and a transmitting unit to implement a transmitting function.
Bus 43 may be an industry standard architecture (industry standard architecture, ISA) bus, an external device interconnect (peripheral component interconnect, PCI) bus, or an extended industry standard architecture (extended industry standard architecture, EISA) bus, among others. The bus 43 may be classified into an address bus, a data bus, a control bus, and the like. For ease of illustration, only one thick line is shown in fig. 8, but not only one bus or one type of bus.
As an example, in connection with fig. 7, the acquisition module in the user behavior prediction apparatus performs the same function as the receiving unit in fig. 8, the processing module in the user behavior prediction apparatus performs the same function as the processor in fig. 8, and the memory module in the user behavior prediction apparatus performs the same function as the memory in fig. 8.
The explanation of the related content in this embodiment may refer to the above method embodiment, and will not be repeated here.
From the foregoing description of the embodiments, it will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of functional modules is illustrated, and in practical application, the above-described functional allocation may be implemented by different functional modules according to needs, i.e. the internal structure of the apparatus is divided into different functional modules to implement all or part of the functions described above. The specific working processes of the above-described systems, devices and units may refer to the corresponding processes in the foregoing method embodiments, which are not described herein.
The embodiment of the application also provides a computer readable storage medium, in which instructions are stored, which when executed by a computer, cause the computer to execute the user behavior prediction method provided in the above embodiment.
The 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 a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a RAM, a ROM, an erasable programmable read-only memory (erasable programmable read only memory, EPROM), a register, a hard disk, an optical fiber, a CD-ROM, an optical storage device, a magnetic storage device, or any suitable combination of the foregoing, or any other form of computer readable storage medium known in the art. An exemplary storage medium is coupled to the processor such the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an application specific integrated circuit (application specific integrated circuit, ASIC). In the context of the present application, 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.
The foregoing is merely a specific embodiment of the present application, but the protection scope of the present application is not limited thereto, and any changes or substitutions within the technical scope of the present disclosure should be covered in the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims (8)

1. A method for predicting user behavior, comprising:
acquiring user behavior parameters of each user in a target user group; the target user group comprises the target users; the user behavior parameters at least comprise: user position information, user access base station information, user access wireless network information and user data flow information in a preset historical time period;
determining a user behavior mode sequence of each user in the preset historical time period according to the user behavior parameters of each user;
under the condition that a prediction request sent by a terminal is received, determining a historical user behavior pattern sequence of a target user; the prediction request comprises the identity of the target user; the historical user behavior pattern sequence is used for representing the mobility intensity degree and the service condition of data flow of the target user in a preset historical time period;
Determining a target user behavior pattern sequence according to the historical user behavior pattern sequence, wherein the target user behavior pattern sequence comprises the following steps: establishing a hidden Markov model according to the historical user behavior pattern sequence; determining the target user behavior pattern sequence according to the hidden Markov model; the target user behavior pattern sequence is used for representing the mobility intensity degree and the service condition of data flow of the target user in a first preset time period;
determining a user behavior prediction result for the target user according to the target user behavior pattern sequence and the user position information of the target user in a preset historical time period, wherein the method comprises the following steps:
determining user position information of the target user in the first preset time period according to the target user behavior pattern sequence and the user position information of the target user in the preset historical time period;
determining user access base station information and user access wireless network information of the target user in the first preset time period according to the user position information of the target user in the first preset time period;
and determining the user data flow information of the target user in the first preset time period.
2. The method of claim 1, wherein after said determining a sequence of user behavior patterns for each user over said predetermined historical period of time, the method further comprises:
generating a unique identity for each user, and storing a mapping relation between a user behavior pattern sequence of each user in the preset historical time period and the identity of each user;
the determining the historical user behavior pattern sequence of the target user comprises the following steps: and determining a historical user behavior pattern sequence of the target user according to the identity of the target user and the mapping relation.
3. The method for predicting user behavior according to claim 2, wherein the obtaining the user behavior parameters of each user in the target user group includes:
acquiring user position information of each user in the preset historical time period based on a first preset rule;
acquiring user access base station information and user data flow information of each user in the preset historical time period based on a second preset rule;
and acquiring the user access wireless network information of each user in the preset historical time period based on a third preset rule.
4. A user behavior prediction apparatus, comprising:
the acquisition module is used for acquiring the user behavior parameters of each user in the target user group; the target user group comprises the target users; the user behavior parameters at least comprise: user position information, user access base station information, user access wireless network information and user data flow information in a preset historical time period;
a fourth determining module, configured to determine a user behavior pattern sequence of each user in the preset historical time period according to the user behavior parameters of each user acquired by the acquiring module;
the first determining module is used for determining a historical user behavior pattern sequence of the target user under the condition that a prediction request sent by the terminal is received; the prediction request comprises the identity of the target user; the historical user behavior pattern sequence is used for representing the mobility intensity degree and the service condition of data flow of the target user in a preset historical time period;
the second determining module is used for determining a target user behavior pattern sequence according to the historical user behavior pattern sequence determined by the first determining module; the target user behavior pattern sequence is used for representing the mobility intensity degree and the service condition of data flow of the target user in a first preset time period;
A third determining module, configured to determine a user behavior prediction result for the target user according to the user location information of the target user in the preset historical time period and the target user behavior pattern sequence determined by the second determining module;
the third determining module is specifically configured to:
determining user position information of the target user in the first preset time period according to the target user behavior pattern sequence determined by the second determining module and the user position information of the target user in the preset historical time period acquired by the acquiring module;
determining user access base station information and user access wireless network information of the target user in the first preset time period according to the user position information of the target user in the first preset time period;
determining user data flow information of the target user in the first preset time period;
the second determining module is specifically configured to:
establishing a hidden Markov model according to the historical user behavior pattern sequence determined by the first determination module;
and determining the target user behavior pattern sequence according to the hidden Markov model.
5. The user behavior prediction apparatus according to claim 4, characterized in that the apparatus further comprises:
the processing module is used for generating a unique identity for each user;
the storage module is used for storing the mapping relation between the user behavior pattern sequence of each user in the preset historical time period, which is determined by the fourth determination module, and the identity of each user, which is generated by the processing module;
the first determining module is specifically configured to: and determining a historical user behavior pattern sequence of the target user according to the identity of the target user and the mapping relation stored by the storage module.
6. The user behavior prediction apparatus according to claim 5, wherein the obtaining module is specifically configured to:
acquiring user position information of each user in the preset historical time period based on a first preset rule;
acquiring user access base station information and user data flow information of each user in the preset historical time period based on a second preset rule;
and acquiring the user access wireless network information of each user in the preset historical time period based on a third preset rule.
7. A user behavior prediction device, which is characterized by comprising a memory, a processor, a bus and a communication interface; the memory is used for storing computer execution instructions, and the processor is connected with the memory through the bus;
when the user behavior prediction apparatus is running, a processor executes the computer-executable instructions stored in the memory to cause the user behavior prediction apparatus to perform the user behavior prediction method of any one of claims 1-3.
8. A computer readable storage medium having instructions stored therein, which when executed by a computer, cause the computer to perform the user behavior prediction method of any one of claims 1-3.
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