CN114885183A - Method, device, medium and equipment for identifying gift package risk user - Google Patents
Method, device, medium and equipment for identifying gift package risk user Download PDFInfo
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/20—Servers specifically adapted for the distribution of content, e.g. VOD servers; Operations thereof
- H04N21/21—Server components or server architectures
- H04N21/218—Source of audio or video content, e.g. local disk arrays
- H04N21/2187—Live feed
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/20—Servers specifically adapted for the distribution of content, e.g. VOD servers; Operations thereof
- H04N21/23—Processing of content or additional data; Elementary server operations; Server middleware
- H04N21/239—Interfacing the upstream path of the transmission network, e.g. prioritizing client content requests
- H04N21/2393—Interfacing the upstream path of the transmission network, e.g. prioritizing client content requests involving handling client requests
- H04N21/2396—Interfacing the upstream path of the transmission network, e.g. prioritizing client content requests involving handling client requests characterized by admission policies
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/20—Servers specifically adapted for the distribution of content, e.g. VOD servers; Operations thereof
- H04N21/25—Management operations performed by the server for facilitating the content distribution or administrating data related to end-users or client devices, e.g. end-user or client device authentication, learning user preferences for recommending movies
- H04N21/254—Management at additional data server, e.g. shopping server, rights management server
- H04N21/2541—Rights Management
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/40—Client devices specifically adapted for the reception of or interaction with content, e.g. set-top-box [STB]; Operations thereof
- H04N21/45—Management operations performed by the client for facilitating the reception of or the interaction with the content or administrating data related to the end-user or to the client device itself, e.g. learning user preferences for recommending movies, resolving scheduling conflicts
- H04N21/462—Content or additional data management, e.g. creating a master electronic program guide from data received from the Internet and a Head-end, controlling the complexity of a video stream by scaling the resolution or bit-rate based on the client capabilities
- H04N21/4627—Rights management associated to the content
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Abstract
The invention provides a method, a device, a medium and equipment for identifying gift package risk users, wherein the method comprises the following steps: acquiring behavior characteristics corresponding to a target user; the behavior characteristics comprise: at least one of equipment information, system version information and contact information used when the gift package is registered and received; determining a feature ratio of each behavioral feature; determining a behavior set category to which the behavior features belong based on the feature ratio; determining a comprehensive risk value of the behavior characteristics based on the behavior set category to which the behavior characteristics belong; determining a behavior risk value of the target user based on the comprehensive risk values of all the behavior characteristics and the quantity of all the behavior characteristics; identifying the target user according to the behavior risk value; in this way, the behavior characteristics of the behavior of each user registered and received in the gift bag are extracted, the behavior characteristics are utilized to determine the behavior risk value of each user, and whether each user is a risk user or not is accurately identified; if the user is determined to be a risky user, the gift bag is not issued to the user, and therefore the activity risk is reduced.
Description
Technical Field
The application relates to the technical field of live broadcast platform wind control, in particular to a method, a device, a medium and equipment for identifying gift package risk users.
Background
The live broadcast platform often holds some activities of feedback users, generally, activity prizes are issued through gift bags, and the users open the gift bags to obtain corresponding real objects, cash or virtual articles. These gift-bag event rewards have some value and are therefore often viewed by dark property. If the behavior of black production cannot be prevented in time, a large loss is caused to the platform cost.
Generally speaking, the method for identifying the black products in the process of sending the gift bags is mainly to monitor the gift bag activities, if the suspected users of the gift bag activities are found to be large in proportion, further strategy interception can be performed on the high-risk gift bag activities, and therefore the gift bag activities with a large number of black products can be identified, and the black products are intercepted.
However, at present, the black products are more and more prone to distributed attack for hiding the whereabouts, each gift package activity has no obvious risk as a whole, and therefore the identification method in the prior art cannot accurately identify black product risk users.
Disclosure of Invention
Aiming at the problems in the prior art, the embodiment of the invention provides a method, a device, a medium and equipment for identifying a gift bag risk user, so as to solve or partially solve the technical problem that the risk user in the gift bag activity cannot be accurately identified in the prior art.
The technical scheme of the invention is realized as follows:
in a first aspect of the present invention, there is provided a method for identifying a gift package risking user, the method comprising:
acquiring behavior characteristics corresponding to a target user; the target user is a user with gift bag registration getting behavior, and the behavior characteristics comprise: at least one of equipment information, system version information and contact information used when the gift package is registered and received;
determining a feature ratio for each of the behavioral features;
determining a behavior set category to which the behavior feature belongs based on the feature ratio;
determining a comprehensive risk value of the behavior feature based on the behavior set category to which the behavior feature belongs;
determining the behavior risk value of the target user based on the comprehensive risk value of all the behavior characteristics and the quantity of all the behavior characteristics;
and identifying the target user according to the behavior risk value.
In the foregoing solution, the determining a feature ratio of each behavior feature includes:
according to the formula
Determining a feature ratio r (F _ v) for each of the behavior features; wherein the content of the first and second substances,
the F _ v is any one of the behavior characteristics, the F is a characteristic name of the behavior characteristic, the v is any characteristic value under the characteristic name F, the n (F _ v) is the number of the behavior characteristics F _ v, and the Σ n (F) is the total number of all the behavior characteristics corresponding to the characteristic name F.
In the above scheme, determining the behavior set category to which the behavior feature belongs based on the feature ratio includes:
according to the formula
Determining a sharing ease value L (F) of a feature name of the behavioral feature;
if the sharing easiness degree value of the characteristic name is larger than or equal to the sharing threshold value, determining the behavior set category to which the behavior characteristic belongs as an easily-generated sharing characteristic set;
if the sharing easiness degree value of the characteristic name is smaller than the sharing threshold value, determining that the behavior set category to which the behavior characteristic belongs is a characteristic set which is not easy to share; wherein the content of the first and second substances,
the F _ v is any one of the behavior features, the F is a feature name of the behavior feature, the v is any feature value under the feature name F, the v (F) is all feature value sets under the feature name F, the | v (F) | is the number of all feature values in v (F), and the r (F _ v) is a feature ratio of the behavior feature.
In the foregoing solution, the determining a comprehensive risk value of a behavior feature based on the behavior set category to which the behavior feature belongs includes:
if the behavior set category of the behavior characteristics is determined to be the easy-to-occur shared characteristic set, according to a formula
Determining a first risk value d (F _ v) of the behavior feature F _ v;
determining a second risk value b (F) for the feature name F of the behavioral feature according to the formula b (F) -1-r (m (F _ v));
according to the formula
Determining a composite risk value W (F _ v) of the behavioral characteristics; wherein the content of the first and second substances,
the r (F _ v) is a feature ratio of the behavior feature F _ v, the m (F _ v) is a behavior feature corresponding to a maximum feature ratio, and the r (m (F _ v)) is a feature ratio corresponding to the m (F _ v).
In the foregoing solution, the determining a comprehensive risk value of a behavior feature based on the behavior set category to which the behavior feature belongs includes:
if the behavior set category of the behavior characteristics is determined to be a shared characteristic set which is not easy to occur, according to a formula
Determining a first risk value d (F _ v) of the behavior feature F _ v;
determining a second risk value b (F) for the feature name F of the behavioral feature according to the formula b (F) ═ r (m (F _ v));
according to the formula
Determining a composite risk value W (F _ v) of the behavioral characteristics; wherein the content of the first and second substances,
the r (F _ v) is a feature ratio of the behavior feature F _ v, the m (F _ v) is a behavior feature corresponding to a maximum feature ratio, and the r (m (F _ v)) is a feature ratio corresponding to the m (F _ v).
In the foregoing solution, the determining the behavioral risk value of the target user based on the comprehensive risk value of all the behavioral characteristics and the number of all the behavioral characteristics includes:
according to the formula
Determining a behavioral risk value risk (act) of the target user; wherein the content of the first and second substances,
the w (F _ v) is a comprehensive risk value of the behavioral characteristics, the F _ v is any one of the behavioral characteristics, the F is a characteristic name of the behavioral characteristics, the v is any characteristic value under the characteristic name F, the F (act) is all behavioral characteristic sets associated with the target user, and the | F (act) | is the total number of the behavioral characteristics in the behavioral characteristic sets.
In the foregoing solution, the identifying the target user according to the behavior risk value includes:
and if the behavior risk value is determined to be larger than a preset risk threshold value, determining that the target user is a risk user.
In a second aspect of the present invention, there is provided an apparatus for identifying a gift bag risking user, the apparatus comprising:
the acquiring unit is used for acquiring behavior characteristics corresponding to a target user; the target user is a user with gift bag registration getting behavior, and the behavior characteristics comprise: at least one of equipment information, system version information and contact information used when the gift package is registered and received;
a determination unit configured to determine a feature ratio of each of the behavior features; determining a behavior set category to which the behavior feature belongs based on the feature ratio; determining a comprehensive risk value of the behavior feature based on the behavior set category to which the behavior feature belongs; determining the behavior risk value of the target user based on the comprehensive risk value of all the behavior characteristics and the quantity of all the behavior characteristics;
and the identification unit is used for identifying the target user according to the behavior risk value.
In a third aspect of the invention, a computer-readable storage medium is provided, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of the first aspect.
In a fourth aspect of the invention, a computer device is provided, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the method of any one of the first aspect when executing the program.
The invention provides a method, a device, a medium and equipment for identifying gift package risk users, wherein the method comprises the following steps: acquiring behavior characteristics corresponding to a target user; the target user is a user with gift bag registration getting behavior, and the behavior characteristics comprise: at least one of equipment information, system version information and contact information used when the gift package is registered and received; determining a feature ratio for each of the behavioral features; determining a behavior set category to which the behavior feature belongs based on the feature ratio; determining a comprehensive risk value of the behavior feature based on the behavior set category to which the behavior feature belongs; determining the behavior risk value of the target user based on the comprehensive risk value of all the behavior characteristics and the quantity of all the behavior characteristics; identifying the target user according to the behavior risk value; in this way, the behavior characteristics of the behavior of each user registered and received in the gift bag are extracted, the behavior characteristics are utilized to determine the behavior risk value of each user, and whether each user is a risk user or not is accurately identified; if the user is determined to be a risky user, the gift bag is not further issued to the user, and the risk of gift bag activity is reduced.
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Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to refer to like parts throughout the drawings.
In the drawings:
FIG. 1 is a flow chart illustrating a method for identifying a gift package risking user according to one embodiment of the present invention;
fig. 2 is a schematic structural diagram of a device for identifying a gift bag risk user according to an embodiment of the present invention;
FIG. 3 shows a schematic diagram of a computer device configuration according to an embodiment of the invention;
FIG. 4 shows a schematic structural diagram of a computer-readable storage medium according to an embodiment of the invention.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
The embodiment provides a method for identifying a gift package risky user, as shown in fig. 1, the method mainly includes the following steps:
s110, acquiring behavior characteristics corresponding to a target user; the target user is a user with gift bag registration getting behavior, and the behavior characteristics comprise: at least one of equipment information, system version information and contact information used when the gift package is registered and received;
generally, before a gift package is issued to a user, the user is required to register information such as name, address, contact information, and the like. In this embodiment, when a user registers and receives a gift package, behavior characteristics corresponding to a target user are obtained. The target user is any one of the registered gift bag receiving users.
Specifically, first, feature extraction is performed on a user, where the feature includes a feature name and a feature value under the feature name, and the extracted feature name and the feature value are spliced to form a feature character string, where the feature character string is a behavior feature in this embodiment.
For example, if the feature name is F and the feature value is v, the corresponding behavior feature is F _ v.
In this embodiment, the behavior characteristics at least include: the gift bag registers device information, system version information and recipient address information used in the pickup.
When the behavior feature is device information, the corresponding feature name may be a device name, and the feature value may be a device ID;
when the behavior characteristic is system version information, the corresponding characteristic name can be a specific version name, and the characteristic value can be a version number;
when the behavior characteristic is contact information, the corresponding characteristic name can be an address, and the characteristic value can be the province, the city and the specific area of the received goods. The characteristic name can also be a name, and then the characteristic value is the specific name; the characteristic name can also be a contact address, and the characteristic value is a telephone number.
For example, assuming the feature name is OS version, the corresponding feature value may be a7.0, a8.0, or a 9.0; the formed behavior characteristics are as follows: OS _ a7.0, OS _ a8.0 and OS _ a 9.0.
In this way, one or more behavior characteristics can be determined for a registered behavior of a gift bag pickup, which makes a good basis for subsequent risk identification.
S111, determining the characteristic ratio of each behavior characteristic;
for each behavior feature, a feature ratio of each behavior feature needs to be determined, which specifically includes:
according to the formula
Determining a feature ratio r (F _ v) of each behavior feature; wherein the content of the first and second substances,
f _ v is any behavior feature, F is a feature name of the behavior feature, v is any feature value under the feature name F, n (F _ v) is the number of the behavior features F _ v, and Σ n (F) is the total number of all behavior features corresponding to the feature name F.
The principle of the above formula is: and determining the feature proportion of the behavior features F _ v in the same type of behavior features by using the quantity of the behavior features F _ v and the total quantity of all the behavior features corresponding to the feature name F, wherein the feature proportion is the feature proportion of the behavior features F _ v.
Continuing with the version information, assuming, for example, that the feature name is OS version, the corresponding feature value may be a7.0, a8.0, or a 9.0; the formed behavior characteristics are as follows: OS _ A7.0, OS _ A8.0 and OS _ A9.0; the number of OS _ a7.0 is 25, the number of OS _ a8.0 is 25, and the number of OS _ a9.0 is 50. The characteristic ratio of the behavior characteristic OS _ a7.0 is then: 25/(25+25+50) ═ 0.25;
the feature ratio of the behavior feature OS _ a8.0 is: 25/(25+25+50) ═ 0.25;
the characteristic ratio of the behavior characteristic OS _ a9.0 is: 50/(25+25+50) ═ 0.5.
Therefore, the characteristic ratio of each behavior characteristic can be determined, and a foundation is laid for subsequently determining the behavior risk value.
S112, determining a behavior set category to which the behavior feature belongs based on the feature ratio; determining a comprehensive risk value of the behavior feature based on the behavior set category to which the behavior feature belongs;
since different behavior features have different characteristics, some behavior features are easily shared among multiple users, such as version information. The more such behavioral characteristics, the less the overall risk. Some behavior features, such as device information, are not easily shared among multiple users, and the more behavior features, the greater the overall risk. In order to improve the determination accuracy of the subsequent risk value, the present embodiment also needs to determine the behavior set category to which each behavior feature belongs.
In one embodiment, determining the behavior set category to which the behavior feature belongs based on the feature ratio includes:
according to the formula
Determining a sharing ease degree value L (F) of a feature name of the behavior feature;
if the sharing easiness degree value of the characteristic name is larger than or equal to the sharing threshold value, determining the behavior set category to which the behavior characteristic belongs as an easily-generated sharing characteristic set;
if the sharing easiness degree value of the characteristic name is smaller than the sharing threshold value, determining the behavior set category to which the behavior characteristic belongs as a characteristic set which is not easy to share; wherein the content of the first and second substances,
f _ v is any behavior feature, F is the feature name of the behavior feature, v is any feature value under the feature name F, v (F) is all the feature value sets under the feature name F, | v (F) | is the number of all the feature values in v (F), and r (F _ v) is the feature ratio of the behavior feature.
The principle of the above formula is: for behavior features that are prone to sharing, the corresponding feature values may be relatively small. Generally, the less the behavior feature can take the same feature value, the more easily the sharing occurs as the number of feature value sets | v (f) | is smaller. Meanwhile, for the behavior characteristics which are easy to share, the distribution is not uniform, and the distribution of some characteristic values is more concentrated, so that the characteristic ratio r (F _ v) and the uniform distribution are adopted
The sum of the squares of the differences between them is measured, and a larger value indicates that the sharing of the behavior feature is easier to occur.
It should be noted that the sharing threshold in this embodiment may be set based on actual conditions, such as 0.05, which is not limited herein.
Continuing with the above example, assuming the feature name is OS version, then the corresponding feature value may be a7.0, a8.0 or a9.0, then | v (f) | 3, and for the feature name OS, the sharing ease value for the feature name is calculated as follows:
assuming that the sharing threshold is 0.05, since 0.068>0.05, the feature name OS belongs to the feature set subject to sharing.
Then, in one embodiment, determining a composite risk value of a behavior feature based on the behavior set category to which the behavior feature belongs includes:
if the behavior set category of the behavior features is determined to be the easy-to-occur shared feature set, according to a formula
Determining a first risk value d (F _ v) of the behavior feature F _ v;
determining a second risk value b (F) for the feature name F of the behavioral feature according to the formula b (F) ═ 1-r (m (F _ v));
according to the formula
Determining a comprehensive risk value W (F _ v) of the behavior characteristics; alternatively, the first and second electrodes may be,
determining a comprehensive risk value of the behavior feature based on the behavior set category to which the behavior feature belongs, including:
if the behavior set category of the behavior feature is determined to be a shared feature set which is not easy to occur, according to a formula
Determining a first risk value d (F _ v) of the behavior feature F _ v;
determining a second risk value b (F) for the feature name F of the behavioral feature according to the formula b (F) ═ r (m (F _ v));
according to the formula
Determining a composite risk value W (F _ v) of the behavior feature; wherein the content of the first and second substances,
r (F _ v) is a feature ratio of the behavior feature F _ v, m (F _ v) is a behavior feature corresponding to the maximum feature ratio, and r (m (F _ v)) is a feature ratio corresponding to m (F _ v).
For example, the characteristic ratio of the behavior characteristic OS _ a7.0 is 0.25; the feature ratio of the behavior feature OS _ a8.0 is 0.25; the characteristic ratio of the behavior characteristic OS _ a9.0 is 0.5. Then m (F _ v) is the behavior feature OS _ a 9.0.
The principle of the above formula is: in determining the composite risk value, two partial risks are considered, one partial risk being a first risk value of the behavioral characteristic itself and a second risk value of the characteristic name of the behavioral characteristic (i.e., the homogeneous characteristic of the behavioral characteristic).
For the determination of the first risk value, a quotient of the characteristic ratio r (F _ v) and the characteristic ratio r (m (F _ v)) is used for the measurement. If the feature name of the behavior feature F _ v belongs to the feature set easy to share, the higher the feature ratio of the behavior feature F _ v, the lower the risk of the feature, and therefore the quotient is subtracted by 1.
Conversely, if the feature name of the behavior feature F _ v belongs to the feature set that is not easy to share, the greater r (F _ v), the greater the risk. Dividing r (m (F _ v)) in the formula is equivalent to normalization (unifies dimensions), and has the advantages that risk values under different feature names can be directly compared, and different behavior features are treated equally. If the processing is not carried out, because the quantity distribution of each behavior feature corresponding to each feature name has larger difference, the method for directly adopting the feature frequency to measure the risk is unfair to some behavior features.
For the determination of the second risk value, the second risk value may be directly measured using r (m (F _ v)). If the feature name of the behavior feature F _ v belongs to the feature set easy to share, the risk is smaller if r (m (F _ v)) is larger, and therefore the second risk value is represented by subtracting r (m (F _ v)) from 1;
if the feature name of the behavior feature F _ v belongs to the feature set which is not easy to share, the risk is higher if r (m (F _ v)) is larger, and therefore the second risk value is directly represented by r (m (F _ v));
in determining the integrated risk value, the calculation results of the above two are considered in combination, and are respectively weighted by 0.5. And because the weight values are all between 0 and 1, the comprehensive risk value obtained by final calculation is also between 0 and 1.
Continuing with the example of the feature name OS belonging to the set of features susceptible to sharing, the first risk value is:
the second risk value is: (f) ═ 1-0.5 ═ 0.5.
The composite risk values are: w (F _ v) ═ 1/2 ═ (0.5+0.5) ═ 0.5
According to the method and the device, the comprehensive risk value of each behavior characteristic is determined, a data base is made for subsequently determining the behavior risk value of each user, and the risk identification precision is improved.
S113, determining the behavior risk value of the target user based on the comprehensive risk value of all the behavior characteristics and the quantity of all the behavior characteristics; and identifying the target user according to the behavior risk value.
After the determination of the composite risk value of each behavior feature, in an embodiment, the determining the behavior risk value of the target user based on the composite risk values of all the behavior features and the number of all the behavior features includes:
according to the formula
Determining a behavioral risk value risk (act) of the target user; wherein the content of the first and second substances,
w (F _ v) is a comprehensive risk value of the behavior characteristics, F _ v is any behavior characteristic, F is a characteristic name of the behavior characteristics, v is any characteristic value under the characteristic name F, F (act) is all behavior characteristic sets related to the target user, and | F (act) | is the total quantity of the behavior characteristics in the behavior characteristic sets.
The principle of the above formula is: and for the primary gift bag registration getting behavior, determining all behavior characteristic sets F (act) related to the target user, and further obtaining the average value of all comprehensive risk values w (F _ v) according to F (act), so that the behavior risk value of the behavior of the target user can be obtained.
In the step, a corresponding behavior risk value is determined for each target user, specifically for each user, and the behavior risk value takes the comprehensive risk values corresponding to all behavior feature sets associated with the target user into account, so that the identification accuracy of the risk users can be improved.
And then identifying the target user according to the behavior risk value.
Specifically, a risk threshold is set, and if the behavior risk value is determined to be greater than a preset risk threshold, the target user is determined to be a risk user. Then, the reward obtaining behavior of the gift bag activity is limited, for example, the user is cancelled, and no gift bag reward is issued to the user.
The value range of the risk threshold is generally 0.5-1, and the risk threshold can be adjusted based on actual conditions without limitation. The risk threshold has the following influencing factors: if the service needs to be identified more accurately, the miskill complaint rate is reduced, and then the risk threshold can be improved; if the activity effect needs to be guaranteed as much as possible and the coverage rate of the identification is increased, the risk threshold value can be appropriately reduced.
The embodiment determines the behavior risk value of each user by extracting the behavior characteristics of each user registering and getting behaviors in the gift package and utilizing the behavior characteristics, and accurately identifies whether each user is a risk user; if the user is determined to be a risky user, the gift bag is not further issued to the user, and the risk of gift bag activity is reduced.
Based on the same inventive concept as the foregoing embodiment, this embodiment further provides a device for identifying a gift package risky user, as shown in fig. 2, the device includes:
an obtaining unit 21, configured to obtain behavior characteristics corresponding to a target user; the target user is a user with gift bag registration getting behavior, and the behavior characteristics comprise: at least one of equipment information, system version information and contact information used when the gift package is registered and received;
a determination unit 22 for determining a feature ratio of each of the behavior features; determining a behavior set category to which the behavior feature belongs based on the feature ratio; determining a comprehensive risk value of the behavior feature based on the behavior set category to which the behavior feature belongs; determining the behavior risk value of the target user based on the comprehensive risk value of all the behavior characteristics and the quantity of all the behavior characteristics;
and the identification unit 23 is configured to identify the target user according to the behavior risk value.
Since the device described in the embodiment of the present invention is a device used for implementing the method for identifying a gift package risk user according to the embodiment of the present invention, a person skilled in the art can understand the specific structure and the deformation of the device based on the method described in the embodiment of the present invention, and thus the detailed description is omitted here. All devices adopted by the method of the embodiment of the invention belong to the protection scope of the invention.
Based on the same inventive concept, the present embodiment provides a computer apparatus 300, as shown in fig. 3, including a memory 310, a processor 320, and a computer program 311 stored on the memory 310 and executable on the processor 320, wherein when the processor 320 executes the computer program 311, any step of the method described above is implemented.
Based on the same inventive concept, the present embodiment provides a computer-readable storage medium 400, as shown in fig. 4, on which a computer program 411 is stored, which computer program 411, when being executed by a processor, realizes the steps of any of the methods described in the previous paragraphs.
Through one or more embodiments of the present invention, the present invention has the following advantageous effects or advantages:
the invention provides a method, a device, a medium and equipment for identifying gift package risk users, wherein the method comprises the following steps: acquiring behavior characteristics corresponding to a target user; the target user is a user with gift bag registration getting behavior, and the behavior characteristics comprise: at least one of equipment information, system version information and contact information used when the gift package is registered and received; determining a feature ratio for each of the behavioral features; determining a behavior set category to which the behavior feature belongs based on the feature ratio; determining a comprehensive risk value of the behavior feature based on the behavior set category to which the behavior feature belongs; determining the behavior risk value of the target user based on the comprehensive risk value of all the behavior characteristics and the quantity of all the behavior characteristics; identifying the target user according to the behavior risk value; in this way, the behavior characteristics of the behavior of each user registered and received in the gift bag are extracted, the behavior characteristics are utilized to determine the behavior risk value of each user, and whether each user is a risk user or not is accurately identified; if the user is determined to be a risky user, the gift bag is not further issued to the user, and the risk of gift bag activity is reduced.
The algorithms and displays presented herein are not inherently related to any particular computer, virtual machine, or other apparatus. Various general purpose systems may also be used with the teachings herein. The required structure for constructing such a system will be apparent from the description above. Moreover, the present invention is not directed to any particular programming language. It is appreciated that a variety of programming languages may be used to implement the teachings of the present invention as described herein, and any descriptions of specific languages are provided above to disclose the best mode of the invention.
In the description provided herein, numerous specific details are set forth. It is understood, however, that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
Similarly, it should be appreciated that in the foregoing description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be interpreted as reflecting an intention that: that the invention as claimed requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this invention.
Those skilled in the art will appreciate that the modules in the device in an embodiment may be adaptively changed and disposed in one or more devices different from the embodiment. The modules or units or components of the embodiments may be combined into one module or unit or component, and furthermore they may be divided into a plurality of sub-modules or sub-units or sub-components. All of the features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or elements of any method or apparatus so disclosed, may be combined in any combination, except combinations where at least some of such features and/or processes or elements are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.
Furthermore, those skilled in the art will appreciate that while some embodiments herein include some features included in other embodiments, rather than other features, combinations of features of different embodiments are meant to be within the scope of the invention and form different embodiments. For example, in the following claims, any of the claimed embodiments may be used in any combination.
The various component embodiments of the invention may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art will appreciate that a microprocessor or Digital Signal Processor (DSP) may be used in practice to implement some or all of the functionality of some or all of the components of a gateway, proxy server, system according to embodiments of the present invention. The present invention may also be embodied as apparatus or device programs (e.g., computer programs and computer program products) for performing a portion or all of the methods described herein. Such programs implementing the present invention may be stored on computer-readable media or may be in the form of one or more signals. Such a signal may be downloaded from an internet website or provided on a carrier signal or in any other form.
It should be noted that the above-mentioned embodiments illustrate rather than limit the invention, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The usage of the words first, second and third, etcetera do not indicate any ordering. These words may be interpreted as names.
While the preferred embodiments of the present application have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all alterations and modifications as fall within the scope of the application.
The above description is only exemplary of the present invention and should not be taken as limiting the scope of the present invention, and any modifications, equivalents, improvements, etc. that are within the spirit and principle of the present invention should be included in the present invention.
Claims (10)
1. A method of identifying a gift package risking user, the method comprising:
acquiring behavior characteristics corresponding to a target user; the target user is a user with gift bag registration getting behavior, and the behavior characteristics comprise: at least one of equipment information, system version information and contact information used when the gift package is registered and received;
determining a feature ratio for each of the behavioral features;
determining a behavior set category to which the behavior feature belongs based on the feature ratio;
determining a comprehensive risk value of the behavior feature based on the behavior set category to which the behavior feature belongs;
determining the behavior risk value of the target user based on the comprehensive risk value of all the behavior characteristics and the quantity of all the behavior characteristics;
and identifying the target user according to the behavior risk value.
2. The method of claim 1, wherein said determining a feature ratio for each of said behavior features comprises:
according to the formula
Determining a feature ratio r (F _ v) for each of the behavior features; wherein the content of the first and second substances,
the F _ v is any one of the behavior characteristics, the F is a characteristic name of the behavior characteristic, the v is any characteristic value under the characteristic name F, the n (F _ v) is the number of the behavior characteristics F _ v, and the Σ n (F) is the total number of all the behavior characteristics corresponding to the characteristic name F.
3. The method of claim 1, wherein determining the behavior set category to which the behavior feature belongs based on the feature ratio comprises:
according to the formula
Determining a sharing ease degree value L (F) of a feature name of the behavioral feature;
if the sharing easiness degree value of the characteristic name is larger than or equal to the sharing threshold value, determining the behavior set category to which the behavior characteristic belongs as an easily-generated sharing characteristic set;
if the sharing easiness degree value of the characteristic name is smaller than the sharing threshold value, determining that the behavior set category to which the behavior characteristic belongs is a characteristic set which is not easy to share; wherein the content of the first and second substances,
the F _ v is any one of the behavior features, the F is a feature name of the behavior feature, the v is any feature value under the feature name F, the v (F) is all feature value sets under the feature name F, the | v (F) | is the number of all feature values in v (F), and the r (F _ v) is a feature ratio of the behavior feature.
4. The method of claim 1, wherein determining a composite risk value for a behavior feature based on the behavior collection category to which the behavior feature belongs comprises:
if the behavior set type of the behavior characteristics is determined to be an easy-to-occur sharing characteristic set, according to a formula
Determining a first risk value d (F _ v) of the behavior feature F _ v;
determining a second risk value b (F) for the feature name F of the behavioral feature according to the formula b (F) -1-r (m (F _ v));
according to the formula
Determining a composite risk value W (F _ v) of the behavioral characteristics; wherein the content of the first and second substances,
the r (F _ v) is a feature ratio of the behavior feature F _ v, the m (F _ v) is a behavior feature corresponding to a maximum feature ratio, and the r (m (F _ v)) is a feature ratio corresponding to the m (F _ v).
5. The method of claim 1, wherein determining a composite risk value for a behavior feature based on the behavior collection category to which the behavior feature belongs comprises:
if the behavior set category of the behavior characteristics is determined to be a shared characteristic set which is not easy to occur, according to a formula
Determining a first risk value d (F _ v) of the behavior feature F _ v;
determining a second risk value b (F) for the feature name F of the behavioral feature according to the formula b (F) ═ r (m (F _ v));
according to the formula
Determining a composite risk value W (F _ v) of the behavioral characteristics; wherein the content of the first and second substances,
the r (F _ v) is a feature ratio of the behavior feature F _ v, the m (F _ v) is a behavior feature corresponding to a maximum feature ratio, and the r (m (F _ v)) is a feature ratio corresponding to the m (F _ v).
6. The method of claim 1, wherein determining the behavioral risk value for the target user based on the composite risk value for all behavioral characteristics and the quantity of all behavioral characteristics comprises:
according to the formula
Determining a behavioral risk value risk (act) of the target user; wherein the content of the first and second substances,
the w (F _ v) is a comprehensive risk value of the behavioral characteristics, the F _ v is any one of the behavioral characteristics, the F is a characteristic name of the behavioral characteristics, the v is any characteristic value under the characteristic name F, the F (act) is all behavioral characteristic sets associated with the target user, and the | F (act) | is the total number of the behavioral characteristics in the behavioral characteristic sets.
7. The method of claim 1, wherein identifying the target user according to the behavioral risk value comprises:
and if the behavior risk value is determined to be larger than a preset risk threshold value, determining that the target user is a risk user.
8. An apparatus for identifying a gift bag risking user, the apparatus comprising:
the acquisition unit is used for acquiring behavior characteristics corresponding to a target user; the target user is a user with gift bag registration getting behavior, and the behavior characteristics comprise: at least one of equipment information, system version information and contact information used when the gift package is registered and received;
a determination unit configured to determine a feature ratio of each of the behavior features; determining a behavior set category to which the behavior feature belongs based on the feature ratio; determining a comprehensive risk value of the behavior characteristics based on the behavior set category to which the behavior characteristics belong; determining the behavior risk value of the target user based on the comprehensive risk value of all the behavior characteristics and the quantity of all the behavior characteristics;
and the identification unit is used for identifying the target user according to the behavior risk value.
9. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 7.
10. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the steps of the method according to any of claims 1-7 are implemented when the program is executed by the processor.
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