CN113656275B - User activity prediction method, device, electronic equipment and storage medium - Google Patents
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Abstract
The invention relates to the technical field of data analysis, and provides a user activity prediction method, a device, electronic equipment and a storage medium. Obtaining an initial active value of a user to be tested according to behavior characteristics of the user to be tested in a preset time length; then determining the initial activity level of the user to be tested from a plurality of preset activity levels according to the initial activity value of the user to be tested; and predicting the probability value of the user to be tested, which is converted from the initial activity level to each preset activity level, according to the initial activity level of the user to be tested and the preset estimation model. The method and the device realize the prediction of the possibility that the user is in various activity levels, and improve the efficiency and accuracy of the prediction of the user activity.
Description
Technical Field
The present invention relates to the field of data analysis technologies, and in particular, to a user activity prediction method, a device, an electronic apparatus, and a storage medium.
Background
With the popularization of intelligent devices, users are gradually used to perform activities such as learning, entertainment, social contact and the like by using the intelligent devices. According to the using habit of the user, the activity of the user can be obtained. In the prior art, the prediction mode of the user activity is single, and the prediction is inaccurate.
Disclosure of Invention
In view of the above, the present invention aims to provide a user activity prediction method, a device, an electronic apparatus and a storage medium.
In order to achieve the above object, the technical scheme adopted by the embodiment of the invention is as follows:
in a first aspect, the present invention provides a method for predicting user activity, the method comprising:
obtaining an initial activity value of a user to be tested according to behavior characteristics of the user to be tested in a preset time length;
determining the initial activity level of the user to be tested from a plurality of preset activity levels according to the initial activity value of the user to be tested;
and predicting that the user to be detected is converted from the initial activity level to the probability value of each preset activity level according to the initial activity level of the user to be detected and a preset estimation model.
In an alternative embodiment, each preset activity level has a corresponding preset range;
the step of determining the initial activity level of the user to be detected from a plurality of preset activity levels according to the initial activity value of the user to be detected comprises the following steps:
determining a target preset range to which the initial active value belongs from a plurality of preset ranges according to the initial active value of the user to be tested;
And taking the target preset active level corresponding to the target preset range as the initial active level of the user to be tested.
In an alternative embodiment, the preset estimation model is obtained as follows:
according to the behavior characteristics of each test user in a first preset period, a first activity level of each test user is obtained;
obtaining a plurality of second activity levels of each test user according to the behavior characteristics of each test user in a second preset period; the second preset period comprises a plurality of time sequences, and one time sequence corresponds to one second activity level;
and constructing the preset estimation model according to the first activity level of each test user and all the second activity levels of each test user.
In an alternative embodiment, the step of constructing the preset estimation model according to the first activity level of each test user and the second activity level of each test user includes:
aiming at each target time sequence, obtaining a sub-estimation model corresponding to the target time sequence according to the first activity level of each test user and the target second activity level of each test user; the target time sequence is any one of the plurality of time sequences, and the target second activity level corresponds to the target time sequence;
Traversing each time sequence to obtain a plurality of sub estimation models; the preset estimation model comprises all sub estimation models.
In an alternative embodiment, the step of obtaining the first activity level of each test user according to the behavior feature of each test user in the first preset period includes:
according to the behavior characteristics of each test user in a first preset period, a first activity value of each test user is obtained;
dividing a plurality of initial intervals according to the first activity values of all the test users;
smoothing each initial interval to obtain a plurality of active ranges;
and obtaining the first activity level of each test user according to the first activity value and the plurality of activity ranges of each test user.
In a second aspect, the present invention provides a user activity prediction apparatus, the apparatus comprising:
the computing module is used for obtaining an initial activity value of the user to be tested according to the behavior characteristics of the user to be tested in the preset time length;
the determining module is used for determining the initial activity level of the user to be detected from a plurality of preset activity levels according to the initial activity value of the user to be detected;
And the prediction module is used for predicting the probability value of the user to be detected, which is converted from the initial activity level to each preset activity level, according to the initial activity level of the user to be detected and a preset estimation model.
In an alternative embodiment, each preset activity level has a corresponding preset range; the determining module is specifically configured to:
determining a target preset range to which the initial active value belongs from a plurality of preset ranges according to the initial active value of the user to be tested;
and taking the target preset active level corresponding to the target preset range as the initial active level of the user to be tested.
In an alternative embodiment, the prediction module is further configured to:
according to the behavior characteristics of each test user in a first preset period, a first activity level of each test user is obtained;
obtaining a plurality of second activity levels of each test user according to the behavior characteristics of each test user in a second preset period; the second preset period comprises a plurality of time sequences, and one time sequence corresponds to one second activity level;
and constructing the preset estimation model according to the first activity level of each test user and all the second activity levels of each test user.
In a third aspect, the invention provides an electronic device comprising a processor and a memory, the memory storing a computer program, the processor implementing the method of any of the preceding embodiments when executing the computer program.
In a fourth aspect, the present invention provides a storage medium having stored thereon a computer program which, when executed by a processor, implements a method according to any of the preceding embodiments.
The embodiment of the invention provides a user activity prediction method, a device, electronic equipment and a storage medium. Obtaining an initial active value of a user to be tested according to behavior characteristics of the user to be tested in a preset time length; then determining the initial activity level of the user to be tested from a plurality of preset activity levels according to the initial activity value of the user to be tested; and predicting the probability value of the user to be tested, which is converted from the initial activity level to each preset activity level, according to the initial activity level of the user to be tested and the preset estimation model. The method and the device realize the prediction of the possibility that the user is in various activity levels, and improve the efficiency and accuracy of the prediction of the user activity.
In order to make the above objects, features and advantages of the present invention more comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 shows a schematic view of a scenario provided by an embodiment of the present invention;
fig. 2 is a schematic block diagram of an electronic device according to an embodiment of the present invention;
fig. 3 is a schematic flow chart of a user activity prediction method according to an embodiment of the present invention;
fig. 4 is a schematic flow chart of a user activity prediction method according to an embodiment of the present invention;
fig. 5 is a schematic flow chart of a user activity prediction method according to an embodiment of the present invention;
fig. 6 is a schematic flow chart of a user activity prediction method according to an embodiment of the present invention;
fig. 7 is a schematic flow chart of a user activity prediction method according to an embodiment of the present invention;
fig. 8 is a functional block diagram of a user activity prediction apparatus according to an embodiment of the present invention.
Icon: 100-server; 102-terminal equipment; a 120-processor; 130-memory; 170-a communication interface; 300-user activity prediction means; 310-a computing module; 330-a determination module; 350-a prediction module.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. The components of the embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the invention, as presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be made by a person skilled in the art without making any inventive effort, are intended to be within the scope of the present invention.
It is noted that relational terms such as "first" and "second", and the like, are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
Fig. 1 is a schematic view of a scenario provided in an embodiment of the present invention. The system comprises a server 100 and a plurality of terminal devices 102, wherein the server 100 is in communication connection with the plurality of terminal devices 102 to realize data interaction.
The server 100 may be a stand-alone server or a server cluster composed of a plurality of servers.
The terminal device 102 may be a smart phone, personal computer, tablet, wearable device, notebook, ultra-mobile personal computer (UMPC), netbook, personal Digital Assistant (PDA), etc. The embodiment of the present invention is not limited in any way.
Optionally, the scene graph may be used to provide a variety of possible services including, but not limited to: multimedia streaming services, cloud gaming, distributed storage, etc. Taking live video as an example, the server 100 may be a server providing live video streaming, and the terminal device 102 may install live video related Application (APP).
The server 100 may collect and analyze data related to the live video application in the terminal device 102 for different analysis purposes. The terminal device 102 may obtain relevant data of the user when using the live video application, and report the relevant data to the server 100.
Fig. 2 is a block diagram of an electronic device according to an embodiment of the invention. The structure of which may be used to implement the server 100 or the terminal device 102 of fig. 1 described above. The electronic device includes a processor 120, a memory 130, and a communication interface 170.
The processor 120, the memory 130, and the communication interface 170 are electrically connected directly or indirectly to each other to realize data transmission or interaction. For example, the components may be electrically connected to each other via one or more communication buses or signal lines. The user activity prediction apparatus 300 includes at least one software function module that may be stored in the memory 130 in the form of software or firmware (firmware) or cured in an Operating System (OS) of the server 100. The processor 120 is configured to execute executable modules stored in the memory 130, such as software functional modules or computer programs included in the user activity prediction apparatus 300.
The processor 120 may be an integrated circuit chip with signal processing capability. The processor 120 may be a general-purpose processor, including a central processing unit (Central Processing Unit, CPU for short), a network processor (Network Processor, NP for short), etc.; but may also be a Digital Signal Processor (DSP), application Specific Integrated Circuit (ASIC), an off-the-shelf programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components. The disclosed methods, steps, and logic blocks in the embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The Memory 130 may be, but is not limited to, a random access Memory 130 (Random Access Memory, RAM), a Read Only Memory 130 (ROM), a programmable Read Only Memory 130 (Programmable Read-Only Memory, PROM), an erasable Read Only Memory 130 (Erasable Programmable Read-Only Memory, EPROM), an electrically erasable Read Only Memory 130 (Electric Erasable Programmable Read-Only Memory, EEPROM), etc. The memory 130 is configured to store a program, and the processor 120 executes the program after receiving an execution instruction, and the method executed by the server 100 according to the process definition disclosed in any embodiment of the present invention may be applied to the processor 120 or implemented by the processor 120.
The communication interface 170 may be used for communication of signaling or data with other node devices.
It should be noted that the structure shown in fig. 2 is only a schematic structural diagram of the electronic device, and the electronic device may further include more or fewer components than those shown in fig. 2, or have a different configuration from that shown in fig. 2. The components shown in fig. 2 may be implemented in hardware, software, or a combination thereof.
It will be appreciated that the electronic device may also comprise other modules, for example, for implementing the respective functions: radio frequency circuitry, I/O interfaces, batteries, touch screens, microphones/speakers, etc. And are not limited herein.
The steps in the methods provided in the embodiments of the present invention are executed with the server 100 as an execution body, and corresponding technical effects are achieved.
Referring to fig. 3, fig. 3 is a flowchart illustrating a method for predicting user activity according to an embodiment of the present invention.
Step S202, obtaining an initial activity value of a user to be tested according to behavior characteristics of the user to be tested in a preset time length;
it can be understood that, in the process of using the application program, the terminal device can obtain the use condition of the user, namely, the behavior feature. The behavioral characteristics may be used to characterize the user's liveness, such as any one or combination of a variety of data including days of liveness, number of liveness, duration of liveness, and the like.
The preset duration may be a length of time of one week elapsed with the current date as the node. It can be understood that the preset time length can be set according to actual requirements, and the embodiment of the invention is not limited.
Optionally, the activity value of the user may be calculated according to the behavior characteristics of the user in the preset duration and the preset formula. For example, according to the behavior characteristics of the user to be tested in the past week, namely the number of active days, the number of active times, the active duration, and a preset formula, the initial active value of the user to be tested is calculated.
The preset formula is H=w1, active days+w2, active times+w3, active time length, wherein H represents an initial active value of a user to be tested; w1, w2 and w3 are preset parameters, optionally w1 is 0.5, w2 is 0.3 and w3 is 0.2. The preset parameters can be understood as weight values of different behavior characteristic data, compared with the number of activities and the active duration, the number of active days can more obviously reflect the activity of the user, and the weight value corresponding to the number of active days, namely the preset parameter w1, can be set to be the maximum. It can be understood that the preset parameters can be set according to actual requirements, and the embodiment of the invention is not limited.
Step S204, determining the initial activity level of the user to be tested from a plurality of preset activity levels according to the initial activity value of the user to be tested;
the preset activity levels may be set based on a large amount of data of application program usage by a test user collected by the terminal device.
For example, three active levels, namely, a high active level, a medium active level, and a low active level may be set. It can be appreciated that the preset activity level may be set according to actual requirements, and the embodiment of the present invention is not limited.
The activity value of the user has a corresponding relation with a preset activity level.
Optionally, according to an initial activity value of the user to be tested and a corresponding relation between the activity value and a preset activity level, the preset activity level corresponding to the initial activity value, that is, the initial activity level of the user to be tested, may be determined from a plurality of preset activity levels.
Step S206, predicting the probability value of the user to be tested from the initial activity level to each preset activity level according to the initial activity level of the user to be tested and the preset estimation model;
the preset estimation model may be a model constructed based on data of a large number of test users using an application program, and may predict a probability of a change in an activity level of the user. The input of the preset estimation model is the current activity level of the user, and the output is the probability value of the user transitioning from the current activity level to each preset activity level.
Optionally, after obtaining an initial activity level of the user to be tested, taking the initial activity level as input of a preset estimation model, and obtaining output as a plurality of probability values. A probability value indicates the probability that the user under test has transitioned from the initial activity level to a predetermined activity level.
For easy understanding, the above steps will be described below by taking the user a to be tested as an example, with reference to the schematic view of the scenario shown in fig. 1.
As shown in fig. 1, the terminal device 102 collects behavior characteristics of the user a to be tested in a predicted period, such as the past week, for example, the number of active days, the number of active times, the active period of the application program used by the user a to be tested, specifically, the number of times of logging in the application program, the online period, the number of times of sending interactive information, and the like. The terminal device 102 sends the collected behavior characteristics of the user a to be tested to the server 100.
After receiving the behavior characteristics of the user A to be tested, the server 100 calculates an initial activity value H1 of the user A to be tested according to the behavior characteristics and a preset formula; and then determining that the initial activity level of the user A to be detected is the middle activity level from a plurality of preset activity levels such as three preset activity levels (high activity level, middle activity level and low activity level) according to the initial activity value H1.
The server 100 obtains a plurality of probability values, such as G1, G2, and G3, according to the initial activity level of the user a to be tested and the preset estimation model, where one probability value indicates a probability that the user a to be tested is converted from the initial activity level, i.e. the middle level, to a preset activity level. If the probability value G1 represents the probability that the user A to be tested changes from the medium activity level to the high activity level; the probability value G1 represents the probability that the user A to be tested is converted from the middle active level to the middle active level; the probability value G1 represents the probability that the user a to be measured transitions from the medium active level to the low active level. Based on a plurality of probability values, the probability prediction of the user for a plurality of liveness is achieved.
Based on the steps, obtaining an initial activity value of the user to be tested according to the behavior characteristics of the user to be tested in a preset time length; then determining the initial activity level of the user to be tested from a plurality of preset activity levels according to the initial activity value of the user to be tested; and predicting the probability value of the user to be tested, which is converted from the initial activity level to each preset activity level, according to the initial activity level of the user to be tested and the preset estimation model. Compared with a single prediction mode in the prior art, the method and the device realize the prediction of the possibility that the user is in multiple active levels, thereby improving the efficiency and the accuracy of the prediction of the user activity, and being beneficial to providing a targeted strategy for the user according to the predicted activity so as to adjust the activity of the user.
Optionally, to further improve the accuracy of predicting user activity. For the step S204, a possible implementation manner is provided in the embodiment of the present invention. Please refer to fig. 4. Step S204 may further include the following steps:
step S204-1, determining a target preset range to which the initial active value belongs from a plurality of preset ranges according to the initial active value of the user to be tested;
It will be appreciated that each preset activity level has a corresponding preset range.
The plurality of preset ranges are a plurality of active values obtained according to the application program used by a plurality of test users, and the ranges are divided based on the active values.
Optionally, after obtaining the initial activity value of the user to be tested, a target preset range may be determined from a plurality of preset ranges according to the preset range to which the initial activity value belongs.
Step S204-3, taking a target preset active level corresponding to the target preset range as an initial active level of the user to be tested;
optionally, the preset activity level has a correspondence with a preset range. The target preset activity level can be determined from a plurality of preset activity levels based on the preset activity level corresponding to the target preset range, and the initial activity level of the user to be detected is obtained.
Through the steps, based on the initial activity value of the user to be detected, a target preset range to which the initial activity value belongs is determined from a plurality of preset ranges, and the target preset activity level corresponding to the target preset range is used as the initial activity level of the user to be detected. Therefore, the activity value is converted into the activity level, the subsequent prediction of the change of the activity level of the user is facilitated, and the accuracy of the prediction is improved.
Optionally, the preset estimation model has an important influence on the prediction of the user activity, which is a key factor of the prediction accuracy, and further, the embodiment of the present invention provides a possible implementation manner of obtaining the preset estimation model, please refer to fig. 5, which is another flow chart provided by the embodiment of the present invention.
Step S212, according to the behavior characteristics of each test user in a first preset period, obtaining a first activity level of each test user;
optionally, a set number of test users may be obtained, and for each test user, the behavior characteristics of the test user in the first set period, such as the number of active days, the number of active times, and the active duration of the past week, are collected, so as to obtain the first activity level of each test user.
Step S214, obtaining a plurality of second activity levels of each test user according to the behavior characteristics of each test user in a second preset period;
the second preset period comprises a plurality of time sequences, and one time sequence corresponds to one second activity level;
alternatively, the second preset period may be set to 24 weeks, and the second preset period may include 24 time sequences, for example, the 1 st time sequence is the first week, the 2 nd time sequence is the first two weeks, the 3 rd time sequence is the first three weeks, and the 24 time sequences are sequentially obtained.
Optionally, according to the behavior characteristics of the test user in a time sequence, a second activity level corresponding to the time sequence can be obtained. Based on the plurality of time sequences, a plurality of second activity levels of the test user may be obtained.
It can be understood that the first preset period and the second preset period can be set according to actual requirements based on a plurality of time sequences, and the embodiment of the invention is not limited.
Step S216, a preset estimation model is constructed according to the first activity level of each test user and all the second activity levels of each test user;
optionally, the change condition of the activity level of each test user based on the time dimension can be obtained according to the first activity level of each test user and all the second activity levels of each test user. Based on the change conditions of the activity levels of a plurality of test users, a preset estimation model can be constructed.
For the above step S212, a possible implementation manner is provided in the embodiment of the present invention. Referring to fig. 6, step S212 may further include the following steps:
step S212-1, obtaining a first activity value of each test user according to the behavior characteristics of each test user in a first preset period;
Optionally, the first preset period is a past week, and the first activity value of each test user may be calculated according to the behavior characteristics of each test user in the past week, such as the number of active days, the number of active times, the active duration, and the preset formula h=w1×active days+w2×active times+w3×active duration.
Step S212-3, dividing a plurality of initial intervals according to the first activity values of all the test users;
optionally, the first active values for all the tests are arranged according to a preset sequence, and then a plurality of initial intervals are divided based on a plurality of preset percentages and quantiles, wherein the quantiles can be the largest first active values.
For example, the first activity values of all the test users are arranged in order from small to large, and based on a plurality of preset percentages such as 25%, 75% and quantiles, 25% of the quantiles are used as a first initial section, 25% to 75% of the quantiles are used as a second initial section, and 75% to 100% of the quantiles are used as a third initial section.
Taking the example that all the first activity values are 1,2,3,4,5,6, …, and the quantile is the largest first activity value, namely 100, the first initial interval is [0,25 ], the second initial interval is [25,75 ], and the third initial interval is [75,100].
Step S212-5, smoothing each initial interval to obtain a plurality of active ranges;
alternatively, calculating an average of all the first active values belonging to the first initial interval, which may be denoted by E (H low activity); calculating an average value of all the first activity values belonging to the second interval, which can be represented by E (activity in H); the average of all the first activity values belonging to the third distinction is calculated and can be represented by E (H high activity).
Each initial interval may be smoothed according to the plurality of averages to obtain a plurality of active ranges. Less than E (H low activity) is a low active range; a middle active range between E (H low activity) and E (H medium activity); a higher than E (H high activity) is a high active range.
It is understood that the preset ranges in step S204 may be set in this way, and the preset ranges and the active ranges may be consistent.
Step S212-7, obtaining a first activity level of each test user according to the first activity value and the plurality of activity ranges of each test user;
optionally, if the first activity value of the test user belongs to the low activity range, the first activity level of the test user is the low activity level.
If the first activity value of the test user belongs to the middle activity range, the first activity level of the test user is the middle activity level.
If the first activity value of the test user belongs to the high activity range, the first activity level of the test user is the high activity level.
It will be appreciated that, for the step S214, a plurality of second activity values may also be calculated by using the behavior characteristics and the preset formulas of each test user in the second preset period, where each second activity value corresponds to a time sequence. Based on the plurality of second activity values, a plurality of second activity levels for each test user are obtained. I.e. a time sequence corresponds to a second activity value, a second activity value corresponding to a second activity level.
Through the steps, the activity level of the test user in different preset periods is obtained based on the behavior characteristics of the test user in different preset periods, so that the change condition of the activity level of the test user is obtained, a preset estimation model is built based on the change condition of the activity level of a large number of test users, and the prediction accuracy is improved.
For the step S216, a possible implementation manner is provided in the embodiment of the present invention. Referring to fig. 7, step S216 may further include the following steps:
step S216-1, aiming at each target time sequence, obtaining a sub-estimation model corresponding to the target time sequence according to the first activity level of each test user and the target second activity level of each test user;
The target time sequence is any one of a plurality of time sequences, and the target second activity level corresponds to the target time sequence.
It may be appreciated that the first activity level is an activity level corresponding to a first preset period, and the second activity level is an activity level corresponding to a time sequence in a second preset period, where the first preset period and the second preset period are consecutive in time.
The first activity level of the test user may be denoted by Ei and the second activity level of the test user may be denoted by Ej. The change in activity level of the test user in the time dimension may be derived from the first activity level and the second activity level, and may be represented by P (Ej/Ei).
Optionally, for each target time sequence, a sub-estimation model corresponding to the target time sequence is obtained according to the first activity level of all the test users and the second activity level corresponding to the target time sequence of all the test users, namely, the target second activity level.
For example, the first week, which is the 1 st time series, is described as an example of the target time series. Setting a number S of test users, and acquiring the behavior characteristics of the S test users in the past week by using the current date as a node to obtain a first activity level of each test user. And then, the behavior characteristics of the S test users in the first week after the current date are acquired, and a second activity level of each test user is obtained.
And respectively calculating the change situations of the activity levels of the test user groups with the first activity level being the low activity level, the medium activity level and the high activity level.
For a test user population with a first activity level of low activity level, this may be denoted by E1:
calculating the proportion probability of the second active level in the test user group to be the low active level, which can be represented by P (E11/E1), wherein the probability of the test user with the low active level being converted into the low active level in a first preset period and a first time sequence can be understood;
calculating the proportion probability that the second active level in the test user group is the middle active level, which can be represented by P (E12/E1), wherein the probability that the test user with the low active level is converted into the middle active level in a first preset period and a first time sequence can be understood;
the probability of the proportion of the second active level to the high active level in the test user group is calculated, which can be represented by P (E13/E1), and the probability of the test user with the low active level to be converted to the high active level in the first preset period and the first time sequence can be understood.
For a test user population with a first activity level being a medium activity level, this may be denoted by E2:
Calculating the proportion probability that the second active level in the test user group is the low active level, which can be represented by P (E21/E2), wherein the probability that the test user with the medium active level is converted into the low active level in a first preset period and a first time sequence can be understood;
calculating the proportion probability that the second active level in the test user group is the middle active level, which can be expressed by P (E22/E2), wherein the probability that the test user with the middle active level is converted into the middle active level in a first preset period and a first time sequence can be understood;
the proportion probability that the second active level in the test user group is the high active level is calculated, which can be represented by P (E23/E2), and the probability that the test user with the medium active level is converted into the high active level in the first preset period and the first time sequence can be understood.
For a test user population with a first activity level of high activity level, this can be denoted by E3:
calculating the proportion probability that the second active level in the test user group is the low active level, which can be represented by P (E31/E3), wherein the probability that the test user with the high active level is converted into the low active level in a first preset period and a first time sequence can be understood;
Calculating the proportion probability that the second active level in the test user group is the middle active level, which can be expressed by P (E32/E3), wherein the probability that the test user with high active level is converted into the middle active level in a first preset period and a first time sequence can be understood;
the probability of the proportion of the second active level to the high active level in the test user group is calculated, which can be represented by P (E33/E3), and the probability that the test user with the high active level is converted into the high active level in the first preset period and the first time sequence can be understood.
By calculating the change conditions of the activity levels of all the test users in a first preset period and a first time sequence, a sub-estimation model corresponding to the first time sequence can be obtained, namely:
step S216-2, traversing each time sequence to obtain a plurality of sub estimation models;
optionally, for each time sequence, a second activity level corresponding to each test user in the time sequence may be calculated, so as to obtain a sub-estimation model corresponding to each time sequence. And obtaining a preset estimation model according to a plurality of sub-estimation models corresponding to the plurality of time sequences, wherein the preset estimation model comprises all sub-estimation models.
Through the steps, based on the first activity level and the second activity level of a large number of test users, the change condition of the activity level of the test user group with different activity levels can be obtained, and the probability of the user changing into different activity levels is obtained, so that various possibility predictions of the user activity level are realized, a targeted service strategy is convenient for the user, and the user activity level can be regulated and controlled.
In order to perform the corresponding steps in the above embodiments and in each possible way, an implementation of the user activity prediction device is given below. Referring to fig. 8, fig. 8 is a functional block diagram of a user activity prediction apparatus 300 according to an embodiment of the present invention. It should be noted that, the basic principle and the technical effects of the user activity prediction apparatus 300 provided in this embodiment are the same as those of the foregoing embodiments, and for brevity, reference may be made to the corresponding contents of the foregoing embodiments. The user activity prediction apparatus 300 includes:
a calculation module 310, configured to obtain an initial activity value of the user to be tested according to the behavior characteristics of the user to be tested in the preset duration;
a determining module 330, configured to determine an initial activity level of the user to be tested from a plurality of preset activity levels according to the initial activity value of the user to be tested;
The prediction module 350 is configured to predict a probability value of the user to be tested from the initial activity level to each preset activity level according to the initial activity level of the user to be tested and the preset estimation model.
Optionally, each preset activity level has a corresponding preset range, and the determining module 330 is specifically configured to: determining a target preset range to which the initial active value belongs from a plurality of preset ranges according to the initial active value of the user to be tested; and taking the target preset activity level corresponding to the target preset range as the initial activity level of the user to be tested.
Optionally, the prediction module 350 is further configured to: according to the behavior characteristics of each test user in a first preset period, a first activity level of each test user is obtained;
obtaining a plurality of second activity levels of each test user according to the behavior characteristics of each test user in a second preset period; the second preset period comprises a plurality of time sequences, and one time sequence corresponds to one second activity level;
and constructing a preset estimation model according to the first activity level of each test user and all the second activity levels of each test user.
Optionally, the prediction module 350 is further configured to: aiming at each target time sequence, obtaining a sub-estimation model corresponding to the target time sequence according to the first activity level of each test user and the target second activity level of each test user; the target time sequence is any one of a plurality of time sequences, and the target second activity level corresponds to the target time sequence;
Traversing each time sequence to obtain a plurality of sub estimation models; the preset estimation model includes all sub-estimation models.
Optionally, the prediction module 350 is further configured to: according to the behavior characteristics of each test user in a first preset period, a first activity value of each test user is obtained;
dividing a plurality of initial intervals according to the first activity values of all the test users;
smoothing each initial interval to obtain a plurality of active ranges;
and obtaining the first activity level of each test user according to the first activity value and the plurality of activity ranges of each test user.
The embodiment of the invention also provides an electronic device, which comprises a processor 120 and a memory 130, wherein the memory 130 stores a computer program, and when the processor executes the computer program, the user activity prediction method disclosed in the above embodiment is realized.
The embodiment of the invention also provides a storage medium, on which a computer program is stored, which when executed by the processor 120 implements the user activity prediction method disclosed in the embodiment of the invention.
In the several embodiments provided in the present invention, it should be understood that the disclosed apparatus and method may be implemented in other manners. The apparatus embodiments described above are merely illustrative, for example, of the flowcharts and block diagrams in the figures that illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, functional modules in the embodiments of the present invention may be integrated together to form a single part, or each module may exist alone, or two or more modules may be integrated to form a single part.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (8)
1. A method for predicting user activity, the method comprising:
obtaining an initial activity value of a user to be tested according to behavior characteristics of the user to be tested in a preset time length;
determining the initial activity level of the user to be tested from a plurality of preset activity levels according to the initial activity value of the user to be tested;
according to the initial activity level of the user to be detected and a preset estimation model, predicting that the user to be detected is converted from the initial activity level to a probability value of each preset activity level;
the preset estimation model is obtained in the following way:
according to the behavior characteristics of each test user in a first preset period, a first activity level of each test user is obtained; the behavior characteristics comprise the number of active days, the number of active times and the active duration;
obtaining a plurality of second activity levels of each test user according to the behavior characteristics of each test user in a second preset period; the second preset period comprises a plurality of time sequences, and one time sequence corresponds to one second activity level;
and determining the change condition of the activity level of each test user based on the time dimension according to the first activity level of each test user and all the second activity levels of each test user, and constructing the preset estimation model.
2. The method of claim 1, wherein each of the predetermined activity levels has a corresponding predetermined range;
the step of determining the initial activity level of the user to be detected from a plurality of preset activity levels according to the initial activity value of the user to be detected comprises the following steps:
determining a target preset range to which the initial active value belongs from a plurality of preset ranges according to the initial active value of the user to be tested;
and taking the target preset active level corresponding to the target preset range as the initial active level of the user to be tested.
3. The method of claim 1, wherein the step of constructing the preset estimation model according to the first activity level of each of the test users and the total second activity level of each of the test users comprises:
aiming at each target time sequence, obtaining a sub-estimation model corresponding to the target time sequence according to the first activity level of each test user and the target second activity level of each test user; the target time sequence is any one of the plurality of time sequences, and the target second activity level corresponds to the target time sequence;
Traversing each time sequence to obtain a plurality of sub estimation models; the preset estimation model comprises all sub estimation models.
4. The method of claim 1, wherein the step of obtaining a first activity level for each test user based on the behavior characteristics of each test user over a first predetermined period comprises:
according to the behavior characteristics of each test user in a first preset period, a first activity value of each test user is obtained;
dividing a plurality of initial intervals according to the first activity values of all the test users;
smoothing each initial interval to obtain a plurality of active ranges;
and obtaining the first activity level of each test user according to the first activity value and the plurality of activity ranges of each test user.
5. A user activity prediction apparatus, the apparatus comprising:
the computing module is used for obtaining an initial activity value of the user to be tested according to the behavior characteristics of the user to be tested in the preset time length;
the determining module is used for determining the initial activity level of the user to be detected from a plurality of preset activity levels according to the initial activity value of the user to be detected;
The prediction module is used for predicting the probability value of the user to be detected from the initial activity level to each preset activity level according to the initial activity level of the user to be detected and a preset estimation model;
the prediction module is further configured to: according to the behavior characteristics of each test user in a first preset period, a first activity level of each test user is obtained; the behavior characteristics comprise the number of active days, the number of active times and the active duration; obtaining a plurality of second activity levels of each test user according to the behavior characteristics of each test user in a second preset period; the second preset period comprises a plurality of time sequences, and one time sequence corresponds to one second activity level; and determining the change condition of the activity level of each test user based on the time dimension according to the first activity level of each test user and all the second activity levels of each test user, and constructing the preset estimation model.
6. The apparatus of claim 5, wherein each of the predetermined activity levels has a corresponding predetermined range; the determining module is specifically configured to:
Determining a target preset range to which the initial active value belongs from a plurality of preset ranges according to the initial active value of the user to be tested;
and taking the target preset active level corresponding to the target preset range as the initial active level of the user to be tested.
7. An electronic device comprising a processor and a memory, the memory storing a computer program, the processor implementing the method of any one of claims 1 to 4 when executing the computer program.
8. A storage medium having stored thereon a computer program which, when executed by a processor, implements the method of any of claims 1 to 4.
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| CN111047338A (en) * | 2018-10-12 | 2020-04-21 | 北大方正集团有限公司 | User activity prediction method, prediction system and medium |
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| CN105631538A (en) * | 2015-12-23 | 2016-06-01 | 北京奇虎科技有限公司 | User activity prediction method and device, and application method and system thereof |
| CN111047338A (en) * | 2018-10-12 | 2020-04-21 | 北大方正集团有限公司 | User activity prediction method, prediction system and medium |
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