CN112884497A - Method and device for determining user type, electronic equipment and storage medium - Google Patents

Method and device for determining user type, electronic equipment and storage medium Download PDF

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CN112884497A
CN112884497A CN201911200962.0A CN201911200962A CN112884497A CN 112884497 A CN112884497 A CN 112884497A CN 201911200962 A CN201911200962 A CN 201911200962A CN 112884497 A CN112884497 A CN 112884497A
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user
login
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variance
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党俊峰
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Beijing Jingdong Century Trading Co Ltd
Beijing Jingdong Shangke Information Technology Co Ltd
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Beijing Jingdong Century Trading Co Ltd
Beijing Jingdong Shangke Information Technology Co Ltd
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    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0201—Market modelling; Market analysis; Collecting market data
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/06—Buying, selling or leasing transactions
    • G06Q30/0601—Electronic shopping [e-shopping]
    • G06Q30/0631—Item recommendations

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Abstract

The invention discloses a method, a device, electronic equipment and a storage medium for determining a user type, and relates to the technical field of computers. One embodiment of the method comprises: screening out multiple groups of target position data in a preset time period according to multiple groups of position data of a user login application program, wherein each group of position data comprises time information and position information corresponding to the time information; analyzing the login distribution state of the user in a preset time period according to the multiple groups of target position data; and determining the type of the user based on the login distribution state of the user in a preset time period. The implementation mode can avoid passively acquiring the relevant information of the user, so that the accurate type of the user is difficult to determine.

Description

Method and device for determining user type, electronic equipment and storage medium
Technical Field
The present invention relates to the field of computer technologies, and in particular, to a method and an apparatus for determining a user type, an electronic device, and a storage medium.
Background
In order to better serve users, e-commerce usually recommends different items for users according to the needs of the users, so that the users can conveniently find out the needed items based on the recommendations. To ensure the accuracy of recommending items, it is often necessary to classify users. The user classification is usually performed by classifying users according to the information about the user, such as the information about the items collected or purchased by the user, the data filled by the user, and the user data collected by the customer service.
In the process of implementing the invention, the inventor finds that at least the following problems exist in the prior art:
in the existing mode of classifying users, the related information of the users is passively acquired, so that the user classification has limitation and passivity, and the accurate type of the users is difficult to determine.
Disclosure of Invention
In view of this, embodiments of the present invention provide a method, an apparatus, a system, and a storage medium for determining a user type, which can avoid a problem that it is difficult to determine an accurate type of a user due to passively acquiring relevant information of the user.
To achieve the above object, according to an aspect of an embodiment of the present invention, there is provided a method of determining a user type.
The method for determining the user type in the embodiment of the invention comprises the following steps: screening out multiple groups of target position data in a preset time period according to multiple groups of position data of a user login application program, wherein each group of position data comprises time information and position information corresponding to the time information; analyzing the login distribution state of the user in the preset time period according to the multiple groups of target position data; and determining the type of the user based on the login distribution state of the user in the preset time period.
In one embodiment, the analyzing the login distribution state of the user in the preset time period according to the plurality of sets of target location data includes:
analyzing the login position distribution state of the user in the preset time period according to the position information in the multiple groups of target position data; and/or analyzing the login time distribution state of the user in the preset time period according to the time information in the multiple groups of target position data.
In yet another embodiment, the location information includes a longitude parameter and a latitude parameter;
analyzing the login position distribution state of the user in the preset time period according to the position information in the multiple groups of target position data, wherein the method comprises the following steps: according to longitude parameters in the multiple groups of target position data, calculating the longitude position variance of the user in the preset time period; calculating latitude position variance of the user in the preset time period according to latitude parameters in the multiple groups of target position data; and analyzing the login position distribution state of the user in the preset time period according to the longitude position variance and the latitude position variance.
In yet another embodiment, the determining the type of the user based on the location information in the plurality of sets of target location data and the login distribution status of the user includes:
if the longitude variance is smaller than a longitude variance threshold and the latitude variance is smaller than a latitude variance threshold, the login location distribution state is that the login location of the user is concentrated in a fixed area within the preset time period, and the longitude and latitude parameters of the fixed area comprise the longitude parameter and the latitude parameter.
In another embodiment, the analyzing the login time distribution state of the user within the preset time period according to the time information in the plurality of sets of target location data includes: calculating the login variance of the user in the preset time period according to the time information in the multiple groups of target position data; and analyzing the login time distribution state of the user in the preset time period according to the login variance.
In another embodiment, said analyzing the login time distribution state of the user within the preset time period according to the login variance comprises: if the login variance is smaller than a login variance threshold, the login position distribution state of the user in the preset time period is that the login time of the user in the preset time period is centralized.
In another embodiment, the determining the type of the user based on the login distribution status of the user includes: and if the login distribution state of the user indicates that the login position of the user is concentrated in the target area within the preset time and the login time is not concentrated, determining that the type of the user is the target type.
To achieve the above object, according to another aspect of the present invention, there is provided an apparatus for determining a user type.
The device for determining the user type comprises the following components: the screening unit is used for screening multiple groups of target position data in a preset time period according to multiple groups of position data of a user login application program, wherein each group of position data comprises time information and position information corresponding to the time information; the analysis unit is used for analyzing the login distribution state of the user in the preset time period according to the multiple groups of target position data; and the determining unit is used for determining the type of the user based on the login distribution state of the user.
In an embodiment, the analysis unit is specifically configured to:
analyzing the login position distribution state of the user in the preset time period according to the position information in the multiple groups of target position data; and/or analyzing the login time distribution state of the user in the preset time period according to the time information in the multiple groups of target position data.
In yet another embodiment, the location information includes a longitude parameter and a latitude parameter; the analysis unit is specifically configured to:
according to longitude parameters in the multiple groups of target position data, calculating the longitude position variance of the user in the preset time period; calculating latitude position variance of the user in the preset time period according to latitude parameters in the multiple groups of target position data; and analyzing the login position distribution state of the user in the preset time period according to the longitude position variance and the latitude position variance.
In a further embodiment, the analysis unit is specifically configured to:
if the longitude variance is smaller than a longitude variance threshold and the latitude variance is smaller than a latitude variance threshold, the login location distribution state is that the login location of the user is concentrated in a fixed area within the preset time period, and the longitude and latitude parameters of the fixed area comprise the longitude parameter and the latitude parameter.
In a further embodiment, the analysis unit is specifically configured to:
calculating the login variance of the user in the preset time period according to the time information in the multiple groups of target position data; and analyzing the login time distribution state of the user in the preset time period according to the login variance.
In a further embodiment, the analysis unit is specifically configured to:
if the login variance is smaller than a login variance threshold, the login position distribution state of the user in the preset time period is that the login time of the user in the preset time period is centralized.
In another embodiment, the determining unit is specifically configured to:
and if the login distribution state of the user indicates that the login position of the user is concentrated in the target area within the preset time and the login time is not concentrated, determining that the type of the user is the target type.
To achieve the above object, according to still another aspect of an embodiment of the present invention, there is provided an electronic apparatus.
An electronic device of an embodiment of the present invention includes: one or more processors; the storage device is used for storing one or more programs, and when the one or more programs are executed by the one or more processors, the one or more processors implement the method for determining the user type provided by the embodiment of the invention.
To achieve the above object, according to still another aspect of an embodiment of the present invention, there is provided a computer-readable medium.
A computer-readable medium of an embodiment of the present invention stores thereon a computer program, which when executed by a processor implements the method for determining a user type provided by an embodiment of the present invention.
One embodiment of the above invention has the following advantages or benefits: in the embodiment of the invention, after a plurality of groups of target position data in the preset time period are screened out, the distribution state of the user logged in the preset time period can be analyzed, and the type of the user can be determined according to the distribution state. According to the embodiment of the invention, the login rule of the user in the preset time period can be obtained by analyzing the login distribution state of the user in the preset time period, so that the type of the user can be accurately determined, and the problem that the accurate type of the user is difficult to determine by passively acquiring the relevant information of the user for classification is solved.
Further effects of the above-mentioned non-conventional alternatives will be described below in connection with the embodiments.
Drawings
The drawings are included to provide a better understanding of the invention and are not to be construed as unduly limiting the invention. Wherein:
FIG. 1 is a schematic diagram of one major flow of a method of determining a user type according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of yet another major flow of a method of determining a user type according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of yet another major flow of a method of determining a user type according to an embodiment of the present invention;
FIG. 4 is a schematic diagram illustrating determining user data for campus users according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of the main elements of an apparatus for determining a user type according to an embodiment of the present invention;
FIG. 6 is a diagram of yet another exemplary system architecture to which embodiments of the present invention may be applied;
FIG. 7 is a schematic block diagram of a computer system suitable for use in implementing embodiments of the present invention.
Detailed Description
Exemplary embodiments of the present invention are described below with reference to the accompanying drawings, in which various details of embodiments of the invention are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the invention. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
It should be noted that the embodiments and features of the embodiments may be combined with each other without conflict.
An embodiment of the present invention provides a method for determining a user type, which may be performed by a computer device, as shown in fig. 1, and includes the following steps.
S101: and screening out multiple groups of target position data in a preset time period according to multiple groups of position data of the user login application program.
Each group of position data comprises time information and position information corresponding to the time information. In the embodiment of the invention, the type of the user can be obtained by carrying out the richness according to the position information and the time information of the user in the preset time period.
After a user logs in an application program through equipment such as a terminal, the equipment such as the terminal can periodically collect the position information logged in by the user, so that a plurality of groups of position data can be obtained. Each set of position data comprises time information and position information corresponding to the time information. Since the device such as the terminal periodically collects the position information, the position information collected each time is marked with the corresponding timestamp, i.e., the time information, so as to obtain a set of position data, and the time of the user logging in the application program is also included in the plurality of sets of position numerical data. After collecting multiple sets of position data, the terminal and other devices can send the position data to the computer device executing the embodiment of the invention, so that the computer device can execute the step conveniently. Or, the terminal and other devices collect multiple sets of position data and then store the collected data, and when the embodiment of the invention is executed, the terminal and other devices obtain multiple sets of position data collected by the terminal and other devices.
Each set of position data comprises time information, so that the position data of the user in a preset time period can be screened out based on the time information in the position data, namely the position data is target position data. Since the time information is included in the target location data, the time at which the user logs into the application can be derived based on the time information.
The determination of the preset time period may be determined according to the rule of logging in the application program by each type of user. For example, if it is determined whether the user is a campus user, the preset time period may be selected to be a period of time during school, such as a week, a month, or several months, etc. Because the number of times and time of logging in the application program by the user each day are variable, the number of the position data collected by the device such as the terminal each day is also variable, and if the number of the position data is small, the characteristic analysis cannot be performed, so the preset time period is usually selected to be a longer time, such as a week, a month, a quarter, and the like. The location information in the location data may be specifically longitude and latitude parameters, that is, longitude parameters and latitude parameters.
It should be noted that, there are various ways for the device such as the terminal to acquire the location information, for example, acquiring the location information according to a system GPS module, or calculating the location information according to the location of the operator base station, or calculating the longitude and latitude according to the location of the surrounding WI-FI router, or a combination of the above ways. By the above manner, a plurality of sets of position data of the user can be obtained.
S102: and analyzing the login distribution state of the user in a preset time period according to the multiple groups of target position data.
The multiple sets of target position data are position data of the user logging in the application program in the preset time period, and include time information of the user logging in the application program in the preset time period and position information corresponding to the time information, so that the rule of the user logging in the application program in the preset time period, namely when and at which position the user logs in the application program, can be determined based on the time information in the multiple sets of target position data, namely the distribution state of the user logging in the application program in the preset time period.
In an implementation manner of the embodiment of the present invention, the login distribution state may include a login time distribution state and/or a login location distribution state.
The login time distribution state indicates whether the time for the user to login the application program in the preset time period is concentrated or not. If the user logs in the application program at a certain time point in the preset time, the time for the user to log in the application program is relatively fixed; if the user logs in the application program in a non-centralized manner within the preset time period, namely the time for logging in the application program is dispersed, the fact that the time for logging in the application program by the user is not fixed is indicated. The login position distribution state represents whether the positions of the user for logging in the application program are concentrated within a preset time period and a concentrated area when the positions are concentrated. If the user logs in the application program in certain areas within a preset time period, the user logs in the application program at a stable position; if the positions of the users for logging in the application program are not concentrated within the preset time period, namely the places for logging in the application program are scattered, the situation that the positions of the users for logging in the application program are not fixed is shown.
This step can be specifically performed as: analyzing the login position distribution state of the user in a preset time period according to the position information in the multiple groups of target position data; and/or analyzing the login time distribution state of the user in a preset time period according to the time information in the multiple groups of target position data.
In the embodiment of the invention, the login variance of the user in the preset time period can be calculated according to the time information in the multiple groups of target position data, and the login time distribution state of the user in the preset time period is further analyzed according to the login variance of the user in the preset time period.
The location information in the location data may include longitude parameters and latitude parameters, so embodiments of the present invention may respectively calculate precision location variances and latitude location variances of the user within a preset time period through the longitude parameters and the latitude parameters in the sets of target location data, and analyze a login location distribution state of the user within the preset time period based on the precision location variances and the latitude location variances.
S103: and determining the type of the user based on the login distribution state of the user in a preset time period.
After the login distribution state of the user in the preset time period is obtained through analysis in step S102, the rule that the user logs in the application program in the preset time period is determined, so that the type of the user is determined according to the rule that the user logs in the application program in the preset time period. Specifically, the embodiment of the present invention may preset rules for each type of user to log in the application program within a certain time period, and further may set the certain time period corresponding to a certain type of user as a preset time period, and further analyze a login distribution state of the user within the preset time period, and then compare the login distribution state with the rules of the certain type of user to determine whether the login distribution state of the user within the preset time period can be matched with the login distribution state, thereby determining whether the user is the type of user.
In another implementation manner of the embodiment of the present invention, the login distribution state of the target type user in the preset time period may be set as follows: the location of the login application is concentrated on the target area and the time of the login application is not concentrated. This step may be specifically performed as: and if the login distribution state of the user indicates that the login position of the user is concentrated in the target area within the preset time and the login time is not concentrated, determining the type of the user as the target type.
In the embodiment of the invention, after a plurality of groups of target position data in the preset time period are screened out, the distribution state of the user logged in the preset time period can be analyzed, and the type of the user can be determined according to the distribution state. According to the embodiment of the invention, the login rule of the user in the preset time period can be obtained by analyzing the login distribution state of the user in the preset time period, so that the type of the user can be accurately determined, and the problem that the accurate type of the user is difficult to determine by passively acquiring the relevant information of the user for classification is solved.
In the embodiment of the present invention, after a plurality of sets of target location data within a preset time period are screened out, a login distribution state of a user within the preset time period may be analyzed, and the embodiment of the present invention takes a login distribution state including a login time distribution state and a login location distribution state as an example, and specifically describes the method in step S102 in the embodiment shown in fig. 1, as shown in fig. 2, step S102 may include the following steps.
S201: and calculating the login variance of the user in the preset time period based on the time information in the multiple groups of target position data.
The time information in the multiple sets of target location data is the time when the location information is collected, so the time information records the login time of the user logging in the application program each time within a preset time period, and the login variance of the user within the preset time period can be calculated based on the time information in the multiple sets of target location data.
Specifically, in this step, the login variance of the user in the preset time period may be calculated according to formula 1.
Figure BDA0002295860920000091
In equation 1, σ2The login variance of the user in the preset time period is represented, X represents the login time of the user in each login in the preset time period, mu represents the average value of the login times of the user in the preset time period, and N represents the login times of the user in the preset time period.
S202: and calculating the longitude position variance of the user in a preset time period according to the longitude parameters in the multiple groups of target position data.
The position information in the multiple groups of target position data is the acquired login position of the user in a preset time period, and the position information can be represented by latitude and longitude parameters, namely the position information comprises precision parameters and latitude parameters. The variance of the longitude position of the user within the preset time period can be calculated based on the longitude parameters of the position information in the plurality of sets of target position data, and the variance of the longitude position of the user within the preset time period can be calculated based on the longitude parameters of the position information in the plurality of sets of target position data.
Specifically, in this step, the variance of the longitude position of the user in the preset time period may be calculated according to formula 2.
Figure BDA0002295860920000092
In equation 2, δ2The longitude parameter setting method includes the steps that the longitude position variance of a user in a preset time period is represented, Y represents longitude parameters of each group of target position data of the user in the preset time period, epsilon represents the average value of the longitude parameters of multiple groups of target position data of the user in the preset time period, and M represents the number of the longitude parameters of the multiple groups of target position data of the user in the preset time period.
S203: and calculating latitude position variance of the user in the preset time period according to the latitude parameters in the multiple groups of target position data.
The latitude position variance of the user in the preset time period can be calculated based on the latitude parameters of the position information in the multiple sets of target position data, and the latitude position variance of the user in the preset time period can be calculated based on the latitude parameters of the position information in the multiple sets of target position data.
In this step, the latitude position variance of the user in the preset time period may be calculated according to formula 3.
Figure BDA0002295860920000101
In the formula 3,. pi.2The latitude position variance of the user in a preset time period is represented, Z represents the latitude parameter of each group of target position data of the user in the preset time period, theta represents the average value of the latitude parameters of a plurality of groups of target position data of the user in the preset time period, and L represents the number of the latitude parameters of the plurality of groups of target position data of the user in the preset time period.
It should be noted that, because the longitude and latitude differ every 0.01 degrees by about 1000 meters in actual distance, the difference between the longitude parameter and the latitude parameter between the positions close to each other is not large, so for convenience of calculation, in the embodiment of the present invention, weighting processing may be performed on the longitude parameter and the latitude parameter before calculation, for example, the longitude parameter and the latitude parameter are amplified by 100 times, and then step S202 and step S203 are performed to facilitate calculation.
S204: and analyzing the login distribution state of the user in the preset time period according to the longitude position variance, the latitude position variance and the login variance.
The login distribution state comprises a login time distribution state and a login position distribution state. The login time distribution state indicates whether the time of logging in the application program by the user in the preset time period is concentrated or not, and the login position distribution state indicates whether the positions of logging in the application program by the user in the preset time period are concentrated in a certain area or not. In the embodiment of the invention, a longitude variance threshold, a latitude variance threshold and a login variance threshold can be preset. And analyzing the login distribution state of the user in the preset time period through the set longitude variance threshold, latitude variance threshold and login variance threshold, and the longitude position variance, latitude position variance and login variance. The login distribution state of the user can be obtained through analysis, and the type of the user can be determined.
The specific mode may be that, if the longitude variance of the location is greater than or equal to the longitude variance threshold, it indicates that the longitude of the login location of the user is dispersed within a preset time period; if the longitude variance of the location is less than the longitude variance threshold, it indicates that the user is in the longitude set of the location for a preset time period. If the latitude position variance is larger than or equal to the latitude variance threshold value, the latitude dispersion of the login position of the user in a preset time period is shown; and if the latitude position variance is smaller than the latitude variance threshold value, indicating that the user logs in the latitude set of the position within the preset time period. Through the judgment of the longitude position variance and the latitude position variance, if the longitude of the login position of the user is concentrated in the preset time period and the latitude of the login position of the user is concentrated in the preset time period, the distribution state of the login position of the user in the preset time period is that the position of the login application program of the user in the preset time period is concentrated in a certain area, and the area is the area to which the position information in the target position data belongs. If the login variance is larger than or equal to the login variance threshold, the login time distribution state is that the time for the user to login the application program in the preset time period is not concentrated; and if the login variance is smaller than the login variance threshold, the login time distribution state is the time set of the user logging in the application program within the preset time period.
With reference to the embodiments shown in fig. 1 and fig. 2, the embodiment of the present invention takes the determination that the user type is a campus user as an example, and the embodiment of the present invention is described. The rule of the campus user logging in the application program is that the logging position is in or near a campus area every day, namely the logging position is concentrated in a target area, the target area is the campus area or near the campus area, and meanwhile, the time of logging in the application program every day is dispersed and not concentrated. Therefore, after the rule of the campus user for logging in the application program is known, whether the user is the campus user can be determined through the embodiment of the invention. As shown in fig. 3, the following steps are included.
S301: and screening out multiple groups of target position data within a preset time period according to multiple groups of position data of the user login application program.
The campus users typically analyze data of the users all day, so the preset time period may be selected to be a longer time period, such as a week. In the step, a plurality of groups of target position data of the user in a preset time period can be screened out. Each set of target location data includes time information and location information corresponding to the time, the location information including longitude parameters and latitude parameters.
The calculation process in this step is the same as that in step S101, and is not described herein again.
S302: and calculating the login variance of the user in a preset time period based on the time information in the plurality of groups of target position data.
The calculation process in this step is the same as that in step S201, and is not described herein again.
S303: according to the longitude parameters in the multiple groups of target position data, longitude position variance of the user in a preset time period is calculated, and according to the latitude parameters in the multiple groups of target position data, latitude position variance of the user in the preset time period is calculated.
The calculation process in this step is the same as steps S202 and S203, and will not be described herein again.
S304: and analyzing the login distribution state of the user in the preset time period according to the longitude position variance, the latitude position variance and the login variance.
In this step, a longitude variance threshold, a latitude variance threshold, and a login variance threshold corresponding to the campus user may be preset, so as to analyze the login distribution state of the user in the preset time period by combining the longitude location variance, the latitude location variance, and the login variance.
If the longitude variance is smaller than the longitude variance threshold and the latitude variance is smaller than the latitude variance threshold, it indicates that the user logs in the location set within the preset time period. And if the login variance is smaller than the login variance threshold, the user is indicated to be in the concentrated login time within the preset time period. Meanwhile, when the fact that the user logs in the centralized location in the preset time period is analyzed, whether the location where the user logs in the centralized location belongs to a target area or not can be judged, and the target area comprises a campus area and an area near the campus. The area near the campus may be set to an area within a preset range from the campus, for example, an area within a range of five kilometers from the campus.
S305: and determining the type of the user based on the login distribution state of the user in a preset time period.
The login distribution state obtained in step S302 can determine whether the login distribution state of the user meets a preset login rule of the campus user, that is, a rule that the login location is concentrated in the campus area or near the campus area and the time for logging in the application program is not concentrated. If the login distribution state of the user meets the preset login rule of the campus user, determining the type of the user as the campus user; and if the login distribution state of the user does not meet the preset login rule of the campus user, determining that the type of the user is not the campus user.
For example, as shown in fig. 4, by analyzing the user 1, the user 2, the user 3, and the user 4 by the method of the embodiment of the present invention, the longitude location variance, the latitude location variance, the login variance, and the area to which the login location belongs are obtained within the corresponding preset time period of each user. Further, according to the data in fig. 4, and steps S303 and S304, the login distribution status and types of user 1, user 2, user 3, and user 4 in the preset time period can be analyzed.
According to the embodiment of the invention, the login rule of the user in the preset time period can be obtained by analyzing the login distribution state of the user in the preset time period, so that whether the user is a campus user can be accurately determined, and the problems that the relevant information of the user is passively acquired for classification and the type of the user is determined to be inaccurate are avoided.
Since some types of users need to analyze data at different times, the preset time period may be divided into two or more parts, and the parts included in the preset time period may be continuous or discontinuous. For example, for a user of the type of a working family, the working time period and the working time period of the user need to be analyzed respectively, so the preset time period in the embodiment of the present invention includes two time period portions, namely, a first preset time period and a second preset time period, where the first preset time period may be from 9 o 'clock to 6 o' clock later, and the second preset time period may be from 6 o 'clock to 12 o' clock later or from 6 o 'clock later to 9 o' clock earlier on the second day.
When determining whether the user is a working clan or not according to the embodiment of the present invention, in step S101, a plurality of sets of target location data within a first preset time period and a second preset time period need to be screened out respectively. And by executing step S102, analyzing the login distribution state of the user in the first preset time period according to the multiple sets of target position data in the first preset time period, and analyzing the login distribution state of the user in the second preset time period according to the multiple sets of target position data in the second preset time period, where a specific calculation manner may be as shown in the embodiment shown in fig. 2.
After the login distribution state of the user in the first preset time period and the login distribution state in the second preset time period are obtained, the type of the user can be determined.
The rule for the office worker to log in the application is usually: the login positions are in the centralized state and the time of logging in the application program is centralized in the working time period, and the login positions in the working time period are different from the login positions in the working time period. Therefore, after the login distribution state of the user in the first preset time period and the login distribution state of the user in the second preset time period are obtained through the step S102, whether the login distribution state meets the rule that the user logs in the application program of the office worker is judged, and whether the user is of the office worker type is further judged.
It should be noted that, for users of the types such as office workers, since the login locations of the users in the multiple time periods included in the preset time period belong to different areas, the locations of the users of the types that log in the application program in the preset time period are not concentrated, that is, the login location variances of the users of the types in the preset time period are generally large. Therefore, when determining the types of users, the embodiment of the present invention may first analyze whether the locations where the user logs in the application program within the preset time period are not concentrated, and if the locations where the user logs in the application program within the preset time period are not concentrated, it indicates that the user may belong to the types, and may continue to execute step S102 to analyze the login distribution states within different time periods; if the user logs into the location set of the application within a preset time period, it is indicated that the user may not be of these types and the subsequent steps may not be performed.
In the embodiment of the invention, the rules of logging in the application program by the users of the types of going out, going on business, going abroad and going to study and the like can be analyzed, so that the type of the user can be determined. For example, the login distribution state of an outgoing user is typically: after a long time, the user logs in the place A, logs in the place B for a while, and then returns to the former place A to log in. And whether the user is an outgoing user can be analyzed and determined based on the rule. As another example, the login distribution status for a user who is left to study abroad is typically: and logging in a foreign place B within a period of time after logging in a place A in China all the time, wherein the logging position of the place B belongs to the school scope, and the user can be analyzed and determined whether to go abroad and leave for study or not based on the rule.
In order to solve the problems in the prior art, an embodiment of the present invention provides an apparatus 500 for determining a user type, as shown in fig. 5, where the apparatus 500 includes:
the screening unit 501 is configured to screen out multiple sets of target location data within a preset time period according to multiple sets of location data of a user logging in an application program, where each set of location data includes time information and location information corresponding to the time information;
an analyzing unit 502, configured to analyze a login distribution state of the user in the preset time period according to the multiple sets of target location data;
a determining unit 503, configured to determine the type of the user based on the login distribution status of the user.
It should be understood that the manner of implementing the embodiment of the present invention is the same as the manner of implementing the embodiment shown in fig. 1, and the description thereof is omitted.
In an implementation manner of the embodiment of the present invention, the analysis unit 502 is specifically configured to:
analyzing the login position distribution state of the user in the preset time period according to the position information in the multiple groups of target position data;
and/or the presence of a gas in the gas,
and analyzing the login time distribution state of the user in the preset time period according to the time information in the multiple groups of target position data.
In an implementation manner of the embodiment of the present invention, the location information includes a longitude parameter and a latitude parameter;
the analysis unit 502 is specifically configured to:
according to longitude parameters in the multiple groups of target position data, calculating the longitude position variance of the user in the preset time period;
calculating latitude position variance of the user in the preset time period according to latitude parameters in the multiple groups of target position data;
and analyzing the login position distribution state of the user in the preset time period according to the longitude position variance and the latitude position variance.
In an implementation manner of the embodiment of the present invention, the analysis unit 502 is specifically configured to:
if the longitude variance is smaller than a longitude variance threshold and the latitude variance is smaller than a latitude variance threshold, the login location distribution state is that the login location of the user is concentrated in a fixed area within the preset time period, and the longitude and latitude parameters of the fixed area comprise the longitude parameter and the latitude parameter.
In an implementation manner of the embodiment of the present invention, the analysis unit 502 is specifically configured to:
calculating the login variance of the user in the preset time period according to the time information in the multiple groups of target position data;
and analyzing the login time distribution state of the user in the preset time period according to the login variance.
In an implementation manner of the embodiment of the present invention, the analysis unit 502 is specifically configured to:
if the login variance is smaller than a login variance threshold, the login position distribution state of the user in the preset time period is that the login time of the user in the preset time period is centralized.
In an implementation manner of the embodiment of the present invention, the determining unit 502 is specifically configured to:
and if the login distribution state of the user indicates that the login position of the user is concentrated in the target area within the preset time and the login time is not concentrated, determining that the type of the user is the target type.
It should be understood that the embodiment of the present invention is implemented in the same manner as the embodiment shown in fig. 2 or fig. 3, and is not repeated herein.
In the embodiment of the invention, after a plurality of groups of target position data in the preset time period are screened out, the distribution state of the user logged in the preset time period can be analyzed, and the type of the user can be determined according to the distribution state. According to the embodiment of the invention, the login rule of the user in the preset time period can be obtained by analyzing the login distribution state of the user in the preset time period, so that the type of the user can be accurately determined, and the problem that the accurate type of the user is difficult to determine by passively acquiring the relevant information of the user for classification is solved.
The invention also provides an electronic device and a readable storage medium according to the embodiment of the invention.
The electronic device of the present invention includes: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the processor, and the instructions are executed by the at least one processor to cause the at least one processor to perform the method for determining a user type provided by the embodiment of the present invention.
Fig. 6 illustrates an exemplary system architecture 600 of a method of determining a user type or an apparatus for determining a user type to which embodiments of the present invention may be applied.
As shown in fig. 6, the system architecture 600 may include terminal devices 601, 602, 603, a network 604, and a server 605. The network 604 serves to provide a medium for communication links between the terminal devices 601, 602, 603 and the server 605. Network 604 may include various types of connections, such as wire, wireless communication links, or fiber optic cables, to name a few.
A user may use the terminal devices 601, 602, 603 to interact with the server 605 via the network 604 to receive or send messages or the like. The terminal devices 601, 602, 603 may have installed thereon various communication client applications, such as shopping applications, web browser applications, search applications, instant messaging tools, mailbox clients, social platform software, etc. (by way of example only).
The terminal devices 601, 602, 603 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop portable computers, desktop computers, and the like.
The server 605 may be a server providing various services, such as a background management server (for example only) providing support for shopping websites browsed by users using the terminal devices 601, 602, 603. The backend management server may analyze and perform other processing on the received data such as the product information query request, and feed back a processing result (for example, product information — just an example) to the terminal device.
It should be noted that the method for determining the user type provided by the embodiment of the present invention is generally executed by the server 605, and accordingly, the device for determining the user type is generally disposed in the server 605. The method for determining the user type provided by the embodiment of the present invention is generally executed by the terminal device 601, 602, 603, and accordingly, the apparatus for determining the user type is generally disposed in the terminal device 601, 602, 603.
It should be understood that the number of terminal devices, networks, and servers in fig. 6 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
Referring now to FIG. 7, a block diagram of a computer system 700 suitable for use in implementing embodiments of the present invention is shown. The computer system illustrated in FIG. 7 is only an example and should not impose any limitations on the scope of use or functionality of embodiments of the invention.
As shown in fig. 7, the computer system 700 includes a Central Processing Unit (CPU)701, which can perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM)702 or a program loaded from a storage section 708 into a Random Access Memory (RAM) 703. In the RAM 703, various programs and data necessary for the operation of the system 700 are also stored. The CPU 701, the ROM 702, and the RAM 703 are connected to each other via a bus 704. An input/output (I/O) interface 705 is also connected to bus 704.
The following components are connected to the I/O interface 705: an input portion 706 including a keyboard, a mouse, and the like; an output section 707 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage section 708 including a hard disk and the like; and a communication section 709 including a network interface card such as a LAN card, a modem, or the like. The communication section 709 performs communication processing via a network such as the internet. A drive 710 is also connected to the I/O interface 705 as needed. A removable medium 711 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 710 as necessary, so that a computer program read out therefrom is mounted into the storage section 708 as necessary.
In particular, according to the embodiments of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program can be downloaded and installed from a network through the communication section 709, and/or installed from the removable medium 711. The computer program performs the above-described functions defined in the system of the present invention when executed by the Central Processing Unit (CPU) 701.
It should be noted that the computer readable medium shown in the present invention can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present invention, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present invention, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a unit, 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 or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in the embodiments of the present invention may be implemented by software or hardware. The described units may also be provided in a processor, and may be described as: a processor includes a screening unit, an analysis unit, and a determination unit. Where the names of these elements do not in some cases constitute a limitation on the elements themselves, for example, a screening element may also be described as a "functional element of a screening element".
As another aspect, the present invention also provides a computer-readable medium that may be contained in the apparatus described in the above embodiments; or may be separate and not incorporated into the device. The computer readable medium carries one or more programs which, when executed by a device, cause the device to perform the method of determining a user type provided by the present invention.
The above-described embodiments should not be construed as limiting the scope of the invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions can occur, depending on design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (10)

1. A method of determining a user type, comprising:
screening out multiple groups of target position data in a preset time period according to multiple groups of position data of a user login application program, wherein each group of position data comprises time information and position information corresponding to the time information;
analyzing the login distribution state of the user in the preset time period according to the multiple groups of target position data;
and determining the type of the user based on the login distribution state of the user in the preset time period.
2. The method according to claim 1, wherein the analyzing the login distribution status of the user in the preset time period according to the plurality of sets of target location data comprises:
analyzing the login position distribution state of the user in the preset time period according to the position information in the multiple groups of target position data;
and/or the presence of a gas in the gas,
and analyzing the login time distribution state of the user in the preset time period according to the time information in the multiple groups of target position data.
3. The method of claim 2, wherein the location information comprises a longitude parameter and a latitude parameter;
analyzing the login position distribution state of the user in the preset time period according to the position information in the multiple groups of target position data, wherein the method comprises the following steps:
according to longitude parameters in the multiple groups of target position data, calculating the longitude position variance of the user in the preset time period;
calculating latitude position variance of the user in the preset time period according to latitude parameters in the multiple groups of target position data;
and analyzing the login position distribution state of the user in the preset time period according to the longitude position variance and the latitude position variance.
4. The method of claim 3, wherein determining the type of the user based on the location information in the plurality of sets of target location data and the login distribution status of the user comprises:
if the longitude variance is smaller than a longitude variance threshold and the latitude variance is smaller than a latitude variance threshold, the login location distribution state is that the login location of the user is concentrated in a fixed area within the preset time period, and the longitude and latitude parameters of the fixed area comprise the longitude parameter and the latitude parameter.
5. The method according to claim 2, wherein the analyzing the login time distribution status of the user in the preset time period according to the time information in the plurality of sets of target location data comprises:
calculating the login variance of the user in the preset time period according to the time information in the multiple groups of target position data;
and analyzing the login time distribution state of the user in the preset time period according to the login variance.
6. The method of claim 5, wherein analyzing the login time distribution status of the user within the preset time period according to the login variance comprises:
if the login variance is smaller than a login variance threshold, the login position distribution state of the user in the preset time period is that the login time of the user in the preset time period is centralized.
7. The method of claim 1, wherein determining the type of the user based on the login profile status of the user comprises:
and if the login distribution state of the user indicates that the login position of the user is concentrated in the target area within the preset time and the login time is not concentrated, determining that the type of the user is the target type.
8. An apparatus for determining a user type, comprising:
the screening unit is used for screening multiple groups of target position data in a preset time period according to multiple groups of position data of a user login application program, wherein each group of position data comprises time information and position information corresponding to the time information;
the analysis unit is used for analyzing the login distribution state of the user in the preset time period according to the multiple groups of target position data;
and the determining unit is used for determining the type of the user based on the login distribution state of the user.
9. An electronic device, comprising:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-7.
10. A computer-readable medium, on which a computer program is stored, which, when being executed by a processor, carries out the method according to any one of claims 1-7.
CN201911200962.0A 2019-11-29 2019-11-29 Method and device for determining user type, electronic equipment and storage medium Pending CN112884497A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114491357A (en) * 2021-12-27 2022-05-13 北京金堤科技有限公司 Method, device and equipment for determining user region attribute and computer storage medium

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114491357A (en) * 2021-12-27 2022-05-13 北京金堤科技有限公司 Method, device and equipment for determining user region attribute and computer storage medium
CN114491357B (en) * 2021-12-27 2023-11-03 北京金堤科技有限公司 Method, device, equipment and computer storage medium for determining user region attribute

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