CN112215667A - User behavior prediction method and device - Google Patents

User behavior prediction method and device Download PDF

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CN112215667A
CN112215667A CN202011294083.1A CN202011294083A CN112215667A CN 112215667 A CN112215667 A CN 112215667A CN 202011294083 A CN202011294083 A CN 202011294083A CN 112215667 A CN112215667 A CN 112215667A
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user behavior
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CN112215667B (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 the prediction result of the user behavior prediction method provided by the application. The method comprises the following steps: the user behavior prediction device determines a historical user behavior pattern sequence of a target user under the condition of receiving a prediction request which is sent by a terminal and comprises an identity mark of the target user. 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 and the data traffic use condition 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 the 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, people increasingly rely on the internet for daily life and work. The method can predict the user behavior based on the increasingly large data of the mobile users, and can provide personalized services for the users according to the prediction result, wherein the personalized services comprise services of recommending consumption 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 users 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 an urgent problem to be solved.
Disclosure of Invention
The application provides a user behavior prediction method and device, and reasonable personalized service can be provided for users based on the prediction result of the user behavior prediction method provided by the application.
In order to achieve the purpose, the technical scheme is as follows:
in a first aspect, the present application provides a user behavior prediction method, including: the user behavior prediction device determines a historical user behavior pattern sequence of a target user under the condition of receiving a prediction request which is sent by a terminal and comprises an identity mark of the target user. The historical user behavior pattern sequence is used for representing the mobility intensity degree and the data traffic use condition 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 and the data traffic use condition 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 the 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 target user behavior pattern sequence can represent the mobility intensity degree and the data traffic use condition of the target user in the first preset time period, so that the mobility and the traffic use condition of the target user behavior 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 the prediction results of the user position information, the mobility and the traffic usage of the target user are obtained, personalized services (such as services of recommending consumption places, parking lots and recommending mobile packages) can be provided for the target user according to the prediction results.
Optionally, in a possible design, 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 a user behavior parameter 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 a 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: the method comprises the steps of user position information, user access base station information, user access wireless network information and user data flow information in a preset historical time period.
Correspondingly, the "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 of the target user and the mapping relation.
Optionally, in another possible design, the "obtaining the user behavior parameter of each user in the target user group" may include: based on a first preset rule, acquiring user position information of each user in a preset historical time period; based on a second preset rule, acquiring user access base station information and user data traffic 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, in another possible design, the "determining a 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 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 within a first preset time period according to user position information of the target user within the first preset time period; and determining user data traffic information of the target user within a first preset time period.
Optionally, in another possible design manner, the "determining a target user behavior pattern sequence according to a historical user behavior pattern sequence" may include: establishing a hidden Markov model according to the historical user behavior pattern sequence; a sequence of target user behavior patterns is determined according to a hidden markov model.
In a second aspect, the present application provides a user behavior prediction apparatus, including: the device comprises a first determination module, a second determination module and a third determination module.
The system comprises a first determining module, a second determining module and a third determining module, wherein the first determining module is used for determining the historical user behavior pattern sequence of a target user under the condition of receiving a prediction request sent by a terminal; the prediction request comprises an identity of the target user; the historical user behavior pattern sequence is used for representing the mobility intensity and the data traffic use condition 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 data traffic use condition 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 mode sequence determined by the second determining module; the user behavior parameters include at least: the method comprises the steps of user position information, user access base station information, user access wireless network information and user data flow information in a preset historical time period.
In a third aspect, the present application provides a user behavior prediction apparatus, including a processor, where the processor is configured to be coupled with 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 apparatus may further include a transceiver, and the transceiver is configured to perform the steps of transceiving data, signaling or information under the control of the processor of the user behavior prediction apparatus, for example, obtaining the user behavior parameters of each user in the target user group.
Alternatively, the user behavior prediction device may be a server, or may be a part of a device in the server, for example, a chip system in the server. The system-on-chip is configured to enable the user behavior prediction apparatus to implement the functions referred to in the first aspect, for example, to receive, transmit or process data and/or information referred to in the 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, in which instructions are stored, and when the instructions are executed by a computer, the method for predicting user behavior as provided in the first aspect is implemented.
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 all or part of the computer instructions may be stored on the computer readable storage medium. The computer-readable storage medium may be packaged with the processor of the user behavior prediction apparatus, or may be packaged separately from the processor of the user behavior prediction apparatus, which is not limited in this application.
For the descriptions of the second, third, fourth and fifth aspects in this application, reference may be made to the detailed description of the first aspect; in addition, for the beneficial effects described in the second aspect, the third aspect, the fourth aspect and the fifth aspect, reference may be made to the beneficial effect analysis of the first aspect, and details are not repeated here.
In the present application, the names of the user behavior prediction devices do not limit the devices or the function modules themselves, and in practical implementations, the devices or the function modules may be presented by other names. Insofar as the functions of the respective devices or functional blocks are similar to those of the present invention, they are within the scope of the claims of the present application and their equivalents.
These and other aspects of the present application will be more readily apparent from the following description.
Drawings
Fig. 1 is a schematic diagram illustrating an architecture of a user behavior prediction system according to an embodiment of the present disclosure;
fig. 2 is a schematic flowchart of a user behavior prediction method according to an embodiment of the present disclosure;
fig. 3 is a schematic flowchart of another user behavior prediction method according to an embodiment of the present application;
fig. 4 is a schematic flowchart of another user behavior prediction method according to an embodiment of the present application;
fig. 5 is a schematic flowchart of another user behavior prediction method according to an embodiment of the present application;
fig. 6 is a schematic flowchart of another user behavior prediction method according to an embodiment of the present application;
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 in detail a user behavior prediction method and apparatus provided in the embodiments of the present application with reference to the accompanying drawings.
The term "and/or" herein is merely an association describing an associated object, meaning that three relationships may exist, e.g., a and/or B, may mean: a exists alone, A and B exist simultaneously, and B exists alone.
The terms "first" and "second" and the like in the description and drawings of the present application are used for distinguishing different objects or for distinguishing different processes for the same object, and are not used for describing a specific order of the objects.
Furthermore, the terms "including" and "having," and any variations thereof, as referred to in the description of the present application, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements but may alternatively include other steps or elements not expressly 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 "for example" are used to mean serving as examples, illustrations or descriptions. Any embodiment or design described herein as "exemplary" or "e.g.," is not necessarily to be construed as preferred or advantageous over other embodiments or designs. Rather, use of the word "exemplary" or "such as" is intended to present concepts related in a concrete fashion.
In the description of the present application, the meaning of "a plurality" means two or more unless otherwise specified.
At present, people increasingly rely on the internet for daily life and work. The method can predict the user behavior based on the increasingly large data of the mobile users, and can provide personalized services for the users according to the prediction result, wherein the personalized services comprise services of recommending consumption places and parking lots for the users, recommending mobile packages for the users and the like.
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 users 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 an urgent problem to be solved.
In view of the above problems in the prior art, an embodiment of the present application provides a user behavior prediction method. The method can firstly determine a target user behavior pattern sequence according to the historical user behavior pattern sequence, and then determine a user behavior prediction result for the target user by combining 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 consumption places, parking lots and recommending mobile packages) can be provided for the target user.
The user behavior prediction method provided by the embodiment of the application can be applied to the 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 a server cluster composed of a plurality of servers, which is not limited in this embodiment of the present application.
Server 01 may illustratively consist of two servers, one being a database server and the other being an application server.
In one possible implementation, the 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 obtain user location information of each user within a preset historical time period based on a first preset rule, and then send the obtained user location information of each user within the preset historical time 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 based on the sequence of historical user behavior patterns. The movement 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 location 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 (PDA), a wearable electronic device, a virtual reality device, or other different types of terminals connected to the server 01.
In a possible implementation manner, a user behavior prediction Application (APP) may be installed on the terminal 02. Illustratively, a user may perform a trigger operation (e.g., a click operation, a sliding operation, or a long-press operation) on an APP interface of the terminal 02, and the trigger terminal 02 sends a prediction request to the server 01.
Based on the system architecture, as shown in fig. 2, an embodiment of the present application provides a user behavior prediction method, which may be applied to a user behavior prediction apparatus, where the user behavior prediction apparatus may be a server 01 in the system architecture shown in fig. 1. The method comprises S101-S103:
s101, under the condition that a user behavior prediction device receives a prediction request sent by a terminal, determining a historical user behavior pattern sequence of a target user.
The prediction request comprises the identity of the target user, and the identity 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 and the data traffic use condition of the target user in a preset historical time period.
The preset historical time period may be a time period before the prediction request is received, which is set in advance by a human.
Optionally, as shown in fig. 3, before step S101, the user behavior prediction method provided in the embodiment of the present application may further include S201 to S203:
s201, the user behavior prediction device obtains 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 position information, user access base station information, user access wireless network (WIFI) information and user data traffic information within a preset historical time period.
Alternatively, as shown in fig. 4, step S201 may include S2011-S2013:
s2011, the user behavior prediction apparatus obtains user location information of each user in a preset historical time period based on a first preset rule.
In a possible implementation manner, the terminal carries an APP that can obtain the user location information in real time, and the user behavior prediction apparatus can obtain the user location information of the user within a preset historical time period from the APP.
Of course, in practical application, the user behavior prediction apparatus may also obtain the user location information of each user in a preset historical time period according to other manners, which is not limited in this embodiment of the application.
And S2012, the user behavior prediction device acquires user access base station information and user data traffic information of each user in a preset historical time period based on a second preset rule.
In a possible implementation manner, a data transmission interface is provided between the user behavior prediction apparatus and a server of an operator, and the user behavior prediction apparatus may obtain, based on the data transmission interface, user access base station information and user data traffic information of a user within a preset historical time period.
In another possible implementation manner, 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 apparatus. In addition, the data traffic used by the terminal access base station is also updated to the user behavior prediction device in real time. The user behavior prediction device can determine the user access base station information and the user data traffic information of the user in a preset historical time period according to the information reported by the terminal.
Of course, in practical application, the user behavior prediction apparatus may also obtain, according to other manners, user access base station information and user data traffic information of each user in a preset historical time period, which is not limited in this embodiment of the present application.
S2013, the user behavior prediction device obtains user access wireless network information of each user in a preset historical time period based on a third preset rule.
In a possible implementation manner, 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 apparatus. The user behavior prediction device can determine the user access wireless network information of the user in the preset historical time period according to the access wireless network information reported by the terminal.
Certainly, in practical application, the user behavior prediction apparatus may also obtain the user access wireless network information of each user in a preset historical time period according to other manners, which is not limited in this embodiment of the application.
It can be understood that, in the embodiment of the present application, the sequence of step S2011 to step S2013 is not limited, and the three steps of 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 user position information, the user access base station information, the user access wireless network information and the user data traffic information of the target user in a preset historical time period.
In one possible implementation, the preset historical time period may be divided into a plurality of historical time periods. Wherein the second preset time period is any one of a plurality of historical time periods. If the user position moving distance of the target user in the second preset time period exceeds the preset distance, or the switching times of the target user accessing the base station in the second preset time period exceeds the first preset times, or the switching times of the target user accessing the wireless network in the second preset time period exceeds the second preset times, the user behavior prediction device may determine that the target user has stronger mobility in the second preset time period. If the user data traffic of the target user used in the second preset time period exceeds the preset traffic, the user behavior prediction device may 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 manually.
If the letter A indicates that the mobility of the user is strong, the letter a indicates that the mobility of the user is weak, the letter B indicates that the user uses more data traffic, and the letter B indicates that the user uses less data traffic. If the preset history time period is divided into 5 history time periods, for example, the user behavior pattern sequence of the target user in the preset history time period may be Ab, Ab. It can be seen that the target user has strong mobility and uses less data traffic within a preset historical time period. For example, the user behavior pattern sequence of the target user within the preset historical time period may be Ab, Ab. It can be seen that the mobility of the target user is changed from strong to weak within the preset historical time period, and the used data traffic is less.
In one possible implementation, the user behavior patterns may include three categories of patterns, a continuous movement pattern, a long-term stationary pattern, and a random pattern. The continuous moving mode may include a driving mode and a passenger mode, the long-term stationary mode may include a working mode and an entertainment mode, and the random mode may include a busy mode and an idle mode, among others. And the driving mode is used for representing that the mobility of the user is strong and the using data flow is less. The passenger mode is used for representing that the mobility of the user is strong and the use data flow is large. The working mode is used for representing that the mobility of the user is weak and the used data flow is less. The entertainment mode is used for representing that the mobility of the user is weak and the use data flow is large. The busy mode is used for representing that the mobility of the user is unstable and the used data traffic is less. The idle mode is used for representing that the mobility of the user is unstable and the used data flow is more.
For example, if the user behavior pattern sequence of the target user within the preset history period is Ab, the user behavior prediction means may determine that the target user belongs to the driving pattern within 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 the 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 the historical user behavior mode 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 data traffic use condition of the target user in a first preset time period.
In one possible implementation, the user behavior prediction apparatus may establish a hidden markov model according to the historical user behavior pattern sequence, and then determine a target user behavior pattern sequence according to the hidden markov model. The hidden Markov model can be input as a historical user behavior pattern sequence and output as a target user behavior pattern sequence. For a specific method for establishing the hidden markov model, reference may be made to the related description in the prior art, and details thereof are not repeated here.
S103, the user behavior prediction device determines a user behavior prediction result of the target user according to the target user behavior mode sequence and the user position information of the target user in the preset historical time period.
Alternatively, as shown in fig. 6, step S103 may be replaced with S1031 to S1033:
and 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 a possible implementation manner, the user behavior prediction apparatus may establish a linear prediction model for the user position information of the target user within a 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 within a first preset time period. Illustratively, the linear equation in the linear prediction model may be: x is k1 t + b1, and y is k2 t + b 2. Wherein x represents the longitude of the location of the user, y represents the latitude of the location of the user, and t represents time. And calculating the error between the calculated x and y values and the actual value based on the linear equation, minimizing the error value, and solving four parameters of k1, b1, k2 and b 2. 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 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.
In a possible implementation manner, the user behavior prediction apparatus 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 within a first preset time period.
S1033, the user behavior prediction device determines user data traffic information of the target user in a first preset time period.
After 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 and the user position information of the target user in the first preset time period, the user data flow information of the target user in the first preset time period can be predicted.
After the user behavior prediction device determines the user behavior prediction result of the target user, personalized service can be provided for the target user according to the prediction result. For example, after the user behavior prediction apparatus determines the user data traffic information of the target user within a first preset time period, the mobile package may be recommended to the target user. For another example, the user behavior prediction apparatus determines that the user behavior pattern sequence of the target user in the first preset time period is as follows: ab. Ab, and Ab, the user behavior prediction apparatus may determine that the target user belongs to the driving pattern within a preset historical period of time, 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 target user behavior pattern sequence can represent the mobility intensity degree and the data traffic use condition of the target user in the first preset time period, so that the mobility and the traffic use condition of the target user behavior 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 results 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 according to the prediction results.
As shown in fig. 7, an embodiment of the present application further provides a user behavior prediction apparatus, where the user behavior prediction apparatus may be a server in the user behavior prediction system shown in fig. 1, and the user behavior prediction apparatus includes: a first determination module 31, a second determination module 32 and a third determination module 33.
The first determining module 31 performs S101 in the above method embodiment, the second determining module 32 performs S102 in the above method embodiment, and the third determining module 33 performs S103 in the above method embodiment.
Specifically, the first determining module 31 is configured to determine the historical user behavior pattern sequence of the target user in the case of receiving a prediction request sent by the terminal. The prediction request comprises an identity of a target user, and the historical user behavior pattern sequence is used for representing the mobility intensity and the data traffic use condition of the target user in a preset historical time period.
And a second determining module 32, 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 data traffic use condition of the 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 this 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 traffic information in a preset historical time period;
the fourth determining module is used for determining a user behavior mode sequence of each user in a preset historical time period according to the user behavior parameters of each user acquired by the acquiring module;
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 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:
based on a first preset rule, acquiring user position information of each user in a preset historical time period;
based on a second preset rule, acquiring user access base station information and user data traffic 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 user position information of the target user within 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 within 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 within a first preset time period according to user position information of the target user within the first preset time period;
and determining user data traffic information of the target user within 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;
a sequence of target user behavior patterns is determined according to a hidden markov model.
Optionally, the storage module is further configured to store program code of the user behavior prediction apparatus, and the like.
As shown in fig. 8, an embodiment of the present application further provides a user behavior prediction apparatus, which includes a memory 41, a processor 42, a bus 43, and a communication interface 44; the memory 41 is used for storing computer execution instructions, and the processor 42 is connected with the memory 41 through a bus 43; when the user behavior prediction apparatus is operating, 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 provided in the above-described embodiments.
In particular implementations, processor 42(42-1 and 42-2) may include one or more Central Processing Units (CPUs), such as CPU0 and CPU1 shown in FIG. 8, as one example. And as an example, the user behavior prediction means may comprise a plurality of processors 42, such as processor 42-1 and processor 42-2 shown in fig. 8. Each of the processors 42 may be a single-Core Processor (CPU) or a multi-Core Processor (CPU). Processor 42 may refer herein to one or more devices, circuits, and/or processing cores that process 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 (RAM) or other type of dynamic storage device that can store information and instructions, an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) 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 self-contained and coupled to the processor 42 via a bus 43. The memory 41 may also be integrated with the processor 42.
In a specific implementation, the memory 41 is used for storing data in the present application and computer-executable instructions corresponding to software programs for executing the present application. The processor 42 may predict various functions of the device by running or executing software programs stored in the memory 41, as well as invoking data stored in the memory 41.
The communication interface 44 is any device, such as a transceiver, for communicating with other devices or communication networks, such as a control system, a Radio Access Network (RAN), a Wireless Local Area Network (WLAN), and the like. The communication interface 44 may include a receiving unit implementing a receiving function and a transmitting unit implementing a transmitting function.
The bus 43 may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an extended ISA (enhanced industry standard architecture) bus, or the like. The bus 43 may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is shown in FIG. 8, but this is not intended to represent only one bus or type of bus.
As an example, in conjunction with fig. 7, the function implemented by the acquisition module in the user behavior prediction apparatus is the same as the function implemented by the receiving unit in fig. 8, the function implemented by the processing module in the user behavior prediction apparatus is the same as the function implemented by the processor in fig. 8, and the function implemented by the storage module in the user behavior prediction apparatus is the same as the function implemented by the memory in fig. 8.
For the explanation of the related contents in this embodiment, reference may be made to the above method embodiments, which are not described herein again.
Through the above description of the embodiments, it is clear to those skilled in the art that, for convenience and simplicity of description, the foregoing division of the functional modules is merely used as an example, and in practical applications, the above function distribution may be completed by different functional modules according to needs, that is, the internal structure of the device may be divided into different functional modules to complete all or part of the above described functions. For the specific working processes of the system, the apparatus and the unit described above, reference may be made to the corresponding processes in the foregoing method embodiments, and details are not described here again.
The embodiment of the present application further provides a computer-readable storage medium, where instructions are stored in the computer-readable storage medium, and when the instructions are executed by a computer, the computer is enabled to execute the user behavior prediction method provided by the foregoing 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 any combination thereof. 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 (EPROM), a register, a hard disk, an optical fiber, a CD-ROM, an optical storage device, a magnetic storage device, 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. Of course, the storage medium may also be integral to the processor. The processor and the storage medium may reside in an Application Specific Integrated Circuit (ASIC). In embodiments 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 above description is only an embodiment of the present application, but the 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 by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims (12)

1. A method for predicting user behavior, comprising:
determining a historical user behavior pattern sequence of a target user under the condition of receiving a prediction request sent by a terminal; the prediction request comprises an identity of the target user; the historical user behavior pattern sequence is used for representing the mobility intensity and the data traffic use condition 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; the target user behavior pattern sequence is used for representing the mobility intensity and the data traffic use condition of the target user in a first preset time period;
and 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.
2. The method of claim 1, wherein prior to determining the sequence of historical user behavior patterns for the target user, the method further comprises:
acquiring a user behavior parameter of each user in a target user group; the target user group comprises the target user; the user behavior parameters include at least: the user position information, the user access base station information, the user access wireless network information and the user data traffic information in the preset historical time period;
determining a user behavior pattern sequence of each user in the preset historical time period according to the user behavior parameters of each user;
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 of the historical user behavior pattern sequence of the target user comprises: and determining the historical user behavior pattern sequence of the target user according to the identity of the target user and the mapping relation.
3. The method according to claim 2, wherein the obtaining the user behavior parameters of each user in the target user group comprises:
based on a first preset rule, acquiring user position information of each user in the preset historical time period;
based on a second preset rule, acquiring user access base station information and user data traffic information of each user in the preset historical time period;
and acquiring user access wireless network information of each user in the preset historical time period based on a third preset rule.
4. The method according to claim 3, wherein the determining a user behavior prediction result for the target user according to the target user behavior pattern sequence and user location information of the target user within a preset historical time period comprises:
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 user data traffic information of the target user within the first preset time period.
5. The method according to any one of claims 1 to 4, wherein the determining a target user behavior pattern sequence from the historical user behavior pattern sequence comprises:
establishing a hidden Markov model according to the historical user behavior pattern sequence;
determining the sequence of target user behavior patterns according to the hidden Markov model.
6. A user behavior prediction apparatus, comprising:
the system comprises a first determining module, a second determining module and a third determining module, wherein the first determining module is used for determining a historical user behavior pattern sequence of a target user under the condition of receiving a prediction request sent by a terminal; the prediction request comprises an identity of the target user; the historical user behavior pattern sequence is used for representing the mobility intensity and the data traffic use condition 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 and the data traffic use condition of the target user in a first preset time period;
and the third determining module is used for determining a user behavior prediction result for the target user according to the user position information of the target user in a preset historical time period and the target user behavior pattern sequence determined by the second determining module.
7. The apparatus of claim 6, wherein the apparatus further comprises:
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 user; the user behavior parameters include at least: the user position information, the user access base station information, the user access wireless network information and the user data traffic information in the preset historical time period;
a fourth determining module, configured to determine, according to the user behavior parameter of each user acquired by the acquiring module, a user behavior pattern sequence of each user in the preset historical time period;
the processing module is used for generating a unique identity for each user;
a storage module, configured to store a mapping relationship 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 is specifically configured to: and determining the historical user behavior pattern sequence of the target user according to the identity of the target user and the mapping relation stored in the storage module.
8. The user behavior prediction device according to claim 7, wherein the obtaining module is specifically configured to:
based on a first preset rule, acquiring user position information of each user in the preset historical time period;
based on a second preset rule, acquiring user access base station information and user data traffic information of each user in the preset historical time period;
and acquiring user access wireless network information of each user in the preset historical time period based on a third preset rule.
9. The user behavior prediction apparatus according to claim 8, wherein the third determining module is specifically configured to:
determining user position information of the target user within 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 within the preset historical time period, which is 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;
and determining user data traffic information of the target user within the first preset time period.
10. The user behavior prediction apparatus according to any one of claims 6 to 9, wherein 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 determining module;
determining the sequence of target user behavior patterns according to the hidden Markov model.
11. A user behavior prediction device 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 in operation, 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 1-5.
12. A computer-readable storage medium having stored therein instructions, which when executed by a computer, cause the computer to perform a method of user behavior prediction according to any one of claims 1-5.
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2022221296A1 (en) * 2021-04-13 2022-10-20 Dotson Michael Rocky System and method for managing social needs of a client

Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101651568A (en) * 2009-07-01 2010-02-17 青岛农业大学 Method for predicting network flow and detecting abnormality
CN102369689A (en) * 2011-08-11 2012-03-07 华为技术有限公司 Long-term forecasting method and device of network flow
CN103685072A (en) * 2013-11-27 2014-03-26 中国电子科技集团公司第三十研究所 Method for quickly distributing network flow
CN105072638A (en) * 2015-07-28 2015-11-18 北京邮电大学 Traffic load sharing method for delay-tolerant heterogeneous wireless mobile network
CN105101475A (en) * 2014-05-12 2015-11-25 中国移动通信集团湖南有限公司 Method and apparatus for identifying WLAN (Wireless Local Area Network) potential users
CN107016126A (en) * 2017-05-12 2017-08-04 西南交通大学 A kind of multi-user's model movement pattern method based on sequential mode mining
CN109768869A (en) * 2017-11-06 2019-05-17 中国移动通信有限公司研究院 A kind of traffic forecast method, system and computer storage medium
CN109862585A (en) * 2019-01-31 2019-06-07 湖北工业大学 A kind of dynamic heterogeneous network flow prediction method based on depth Space-time Neural Network
CN111915104A (en) * 2020-08-28 2020-11-10 山东省国土测绘院 Method and device for predicting outgoing position

Patent Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101651568A (en) * 2009-07-01 2010-02-17 青岛农业大学 Method for predicting network flow and detecting abnormality
CN102369689A (en) * 2011-08-11 2012-03-07 华为技术有限公司 Long-term forecasting method and device of network flow
CN103685072A (en) * 2013-11-27 2014-03-26 中国电子科技集团公司第三十研究所 Method for quickly distributing network flow
CN105101475A (en) * 2014-05-12 2015-11-25 中国移动通信集团湖南有限公司 Method and apparatus for identifying WLAN (Wireless Local Area Network) potential users
CN105072638A (en) * 2015-07-28 2015-11-18 北京邮电大学 Traffic load sharing method for delay-tolerant heterogeneous wireless mobile network
CN107016126A (en) * 2017-05-12 2017-08-04 西南交通大学 A kind of multi-user's model movement pattern method based on sequential mode mining
CN109768869A (en) * 2017-11-06 2019-05-17 中国移动通信有限公司研究院 A kind of traffic forecast method, system and computer storage medium
CN109862585A (en) * 2019-01-31 2019-06-07 湖北工业大学 A kind of dynamic heterogeneous network flow prediction method based on depth Space-time Neural Network
CN111915104A (en) * 2020-08-28 2020-11-10 山东省国土测绘院 Method and device for predicting outgoing position

Non-Patent Citations (5)

* Cited by examiner, † Cited by third party
Title
HYEBIN PARK等: ""A Markov-Based Prediction Algorithm for User Mobility at Heterogeneous Cloud Radio Access Network"", 《2019 IEEE INTERNATIONAL CONFERENCE ON BIG DATA AND SMART COMPUTING (BIGCOMP)》, pages 1 - 5 *
方志祥等: ""顾及上网行为特征的手机用户停留行为预测方法"", 《地球信息科学学报》, vol. 22, no. 01, pages 136 - 143 *
穆桃等: ""基于多层网络流量分析的用户分类方法"", 《计算机应用》, no. 03, pages 705 - 710 *
薛超: ""基于位置数据的价值发现与行为预测"", 《中国优秀硕士学位论文全文数据库信息科技辑》, pages 138 - 2257 *
陈曦等: ""一种基于朴素贝叶斯分类的3G 用户流量预测技术"", 《计算机与现代化》, no. 4, pages 153 - 157 *

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2022221296A1 (en) * 2021-04-13 2022-10-20 Dotson Michael Rocky System and method for managing social needs of a client

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