CN110020211B - Method and device for evaluating influence of user attributes - Google Patents
Method and device for evaluating influence of user attributes Download PDFInfo
- Publication number
- CN110020211B CN110020211B CN201710992405.1A CN201710992405A CN110020211B CN 110020211 B CN110020211 B CN 110020211B CN 201710992405 A CN201710992405 A CN 201710992405A CN 110020211 B CN110020211 B CN 110020211B
- Authority
- CN
- China
- Prior art keywords
- user
- attribute
- influence
- value
- determining
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/953—Querying, e.g. by the use of web search engines
- G06F16/9535—Search customisation based on user profiles and personalisation
-
- 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
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/01—Social networking
Abstract
The invention discloses a method and a device for evaluating user attribute influence, and relates to the technical field of internet. One embodiment of the method comprises: obtaining historical behavior records of a plurality of objects, wherein the objects belong to the same object class, the historical behavior records comprise user attributes, and the user attributes comprise a plurality of attribute values; determining a difference degree value of each object operated by users with different attribute values based on the historical behavior record; and determining the influence of the user attribute according to the difference degree value of each object operated by the user with different attribute values. According to the embodiment, the calculation complexity is reduced, the calculation amount is reduced, the user attribute influence can be calculated on large-scale data quickly, the calculated influence is consistent with the actual situation, and the universality is enhanced.
Description
Technical Field
The invention relates to the technical field of internet, in particular to a method and a device for evaluating user attribute influence.
Background
With the continuous development of internet technology, various applications based on internet technology are emerging, such as social networks like forums, shopping websites, microblog WeChat, and the like. In such a virtual social environment, people have various attributes and can also generate various behaviors, and the attributes have certain influence on the behaviors of the people, and how to evaluate the influence of the attributes on the behaviors of users is a hot problem to be researched. Currently, commonly used methods for evaluating the influence of user attributes include an influence calculation method based on information gain and a calculation method based on cosine similarity.
The above two methods are described below by taking an example of evaluating the influence of the user attribute on selecting a commodity.
Influence calculation method based on information gain
In the information theory, the information entropy represents the uncertainty of a random variable, and the condition entropy represents the uncertainty of the random variable under a certain condition. The information gain is the difference between the information entropy and the conditional entropy, that is, the magnitude of the information gain indicates the degree of uncertainty reduction of the random variable under a certain condition. When the influence is calculated, the probability distribution of all users selecting commodities in the categories is calculated, and the total information entropy is calculated. And then, calculating the conditional entropy under different attribute values under each user characteristic attribute, and accumulating. And calculating the difference between the total information entropy and the accumulated sum of the conditional entropies, wherein the larger the difference is, the larger the influence of the attribute on the selection of the commodity is.
Influence calculation method based on cosine similarity
The degree of similarity of the two probability distributions can be measured by cosine similarity. If the cosine similarity is larger, the similarity is shown. The smaller the cosine similarity, the larger the difference between the two probability distributions. When the influence is calculated, firstly, the probability distribution of the selected commodities under different attribute values under the specific attributes of all the users is calculated, then the probability distribution difference of the commodities selected by the users with different attribute values is measured by utilizing cosine similarity, and the larger the cosine similarity is, the larger the influence of the attributes on the selection of the commodities of the category is.
In the process of implementing the invention, the inventor finds that at least the following problems exist in the prior art:
because the magnitude of the information gain is not limited in a fixed range, the normalization of the influence coefficient is problematic, and the influence of the same user attribute on different categories cannot be easily compared. When the method calculates attributes with more attribute values, the calculated information gain is often higher, and the influence of the attributes is larger, so that the situation that the calculated information gain is inconsistent with the actual situation can occur. The method needs a large amount of calculation, and when the number of commodities in the category is large, the efficiency of the algorithm is also affected.
Because the increase of the cosine similarity is nonlinear, when the cosine similarity is applied to scenes with attribute values exceeding two types, the influence can not be simply calculated by operations such as addition of the similarity, and the application range of the cosine similarity is limited.
In summary, it can be known that the existing method for calculating the influence of the user attribute has the defects of complicated steps, inconsistency between the calculated influence and the actual situation, and limited application range.
Disclosure of Invention
In view of this, embodiments of the present invention provide a method and an apparatus for evaluating user attribute influence, which can significantly reduce computation complexity, reduce computation workload, and quickly compute user attribute influence on large-scale data, and the calculated influence conforms to an actual situation, thereby enhancing universality.
To achieve the above object, according to an aspect of the embodiments of the present invention, a method for evaluating influence of a user attribute is provided.
The method for evaluating the influence of the user attribute comprises the following steps: obtaining historical behavior records of a plurality of objects, wherein the objects belong to the same object class, the historical behavior records comprise user attributes, and the user attributes comprise a plurality of attribute values; determining a difference degree value of each object operated by users with different attribute values based on the historical behavior record; and determining the influence of the user attribute according to the difference degree value of each object operated by the user with different attribute values.
Optionally, the step of determining the difference degree value of each object operated by the user with different attribute values according to the embodiment of the present invention includes: for each of the plurality of attribute values, determining a probability that a user having the attribute value operates on each of the plurality of objects; and determining the difference degree value of each object operated by the user with different attribute values according to the probability corresponding to each attribute value.
Alternatively, the evaluation method of user attribute influence of the embodiment of the present invention determines the difference degree value according to the following formula (1),
wherein D isijRepresenting said difference measure, PijRepresenting the probability of a user having the ith attribute value operating on the jth object,
and the average value of the probabilities of the j-th object operated by the users from i to n is represented by 1 < i ≦ m, 1 < j ≦ n, m represents the number of the attribute values, and n represents the number of the objects.
Optionally, the method for evaluating user attribute influence according to the embodiment of the present invention determines the user attribute influence according to the following formula (2):
wherein W represents the user attribute influence, and K represents a normalization coefficient.
Optionally, the normalization coefficient K of the embodiment of the present invention is 2m "2.
Optionally, before determining the difference degree value of each object operated by the user with different attribute values, the method of the embodiment of the present invention further includes: and carrying out duplicate removal processing on the historical behavior records so as to screen out repeated operation of the same user on the same object.
To achieve the above object, according to another aspect of the embodiments of the present invention, there is provided an apparatus for evaluating influence of a user attribute.
The device for evaluating the influence of the user attribute comprises the following components: the behavior record acquisition module is used for acquiring historical behavior records of a plurality of objects, wherein the objects belong to the same object class, the historical behavior records comprise user attributes, and the user attributes comprise a plurality of attribute values; the difference degree value determining module is used for determining the difference degree value of each object operated by the user with different attribute values based on the historical behavior record; and the influence determining module is used for determining the influence of the user attribute according to the difference degree value of each object operated by the user with different attribute values.
Optionally, the difference degree value determining module of the embodiment of the present invention is configured to: for each of the plurality of attribute values, determining a probability that a user having the attribute value operates on each of the plurality of objects; and determining the difference degree value of each object operated by the user with different attribute values according to the probability corresponding to each attribute value.
Alternatively, the difference degree value determination module of the embodiment of the present invention determines the difference degree value according to the following formula (1),
wherein D isijRepresenting said difference measure, PijRepresenting the probability of a user having the ith attribute value operating on the jth object,
and the average value of the probabilities of the j-th object operated by the users from i to n is represented by 1 < i ≦ m, 1 < j ≦ n, m represents the number of the attribute values, and n represents the number of the objects.
Optionally, the influence determining module of the embodiment of the present invention determines the user attribute influence according to the following formula (2):
wherein W represents the user attribute influence, and K represents a normalization coefficient.
Optionally, the normalization coefficient K of the embodiment of the present invention is 2m "2.
Optionally, the apparatus for evaluating user attribute influence according to the embodiment of the present invention further includes a deduplication module, configured to perform deduplication processing on the historical behavior record to screen out duplicate operations of the same user on the same object.
To achieve the above object, according to still another aspect of embodiments of the present invention, there is provided an electronic device for a user attribute influence evaluation method.
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 realize the evaluation method for the influence of the user attribute.
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 according to an embodiment of the present invention has stored thereon a computer program that, when executed by a processor, implements the method for evaluating influence of user attributes of an embodiment of the present invention.
One embodiment of the above invention has the following advantages or benefits: because the technical means of utilizing the difference degree value of each object operated by the user with different attribute values is adopted, the technical problems of complicated steps, inconsistent calculated influence with the actual situation and limited application range in the prior art are solved, and the technical effects of reducing the calculation complexity, reducing the calculation amount and improving the universality and the accuracy are further achieved.
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 a main flow of an evaluation method of influence of a user attribute according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of the main modules of an apparatus for evaluating influence of user attributes according to an embodiment of the present invention;
FIG. 3 is an exemplary system architecture diagram in which embodiments of the present invention may be employed;
fig. 4 is a schematic block diagram of a computer system suitable for use in implementing a terminal device or server of an embodiment of the 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.
Fig. 1 is a main flow diagram of a method for evaluating user influence according to an embodiment of the present invention. As shown in fig. 1, the method for evaluating influence of a user according to an embodiment of the present invention mainly includes the following steps:
step S101: obtaining historical behavior records of a plurality of objects, wherein the plurality of objects belong to the same object class, the historical behavior records comprise user attributes, and the user attributes comprise a plurality of attribute values.
The method for evaluating the influence of the user attributes provided by the embodiment of the invention can be applied to various application scenes such as commercial websites, electronic commerce or social networks. In different application scenarios, objects, behaviors may have different meanings. Specifically, the object refers to content on a website, such as a social hotspot, a comment, a commodity, and the like. Behavior refers to user's manipulation of objects, such as browsing, commenting, collecting, purchasing, and the like.
In the field of electronic commerce or a commodity search engine, historical browsing records of commodities in a certain category can be acquired, the influence of user attributes on selection of commodities of different categories is evaluated by using the method provided by the embodiment of the invention, and commodities which the user may want to know or purchase can be recommended for the user, so that accurate and personalized services are provided for the user, the user experience is improved, and a data basis is provided for an operator to objectively make an operation strategy. In a social network such as a microblog, a microblog release record or a comment and forwarding record of a registered user can be obtained, the influence of user attributes on information propagation is evaluated by using the method of the embodiment of the invention, and valuable users are found out according to the evaluation of the influence of the users, so that a theoretical basis is provided for the extended application of the microblog, such as the public opinion monitoring and microblog marketing promotion of the microblog.
In embodiments of the present invention, objects may be classified. For example, when the object is a social hotspot, the object can be classified into a civil category, an entertainment category and a scientific and technological category, and when the object is military news or social news or financial news, the object can be classified into the civil category; when the object is a movie report, a constellation commentary or entertainment news, the object can be divided into entertainment categories; when the object is a report issued by a new electronic product and a report in the scientific and technological field, the object can be classified into the scientific and technological class. For example, when the object is a commodity, the object can be classified into a clothing class, a home class, a digital product class, and the like.
In the embodiment of the present invention, the user attribute may be a gender attribute, a region attribute (which is obtained by dividing a city where the user is located, such as a first-line city, a second-line city, and a third-line city), a user level attribute, an age attribute, and the like, which are not limited herein.
In the embodiment of the present invention, the historical behavior record may be a behavior record of a plurality of objects within a period of time, for example, the period of time may be two months, and a person skilled in the art may determine the history according to specific requirements, and the present invention is not limited herein.
Step S102: based on the historical behavior record, a difference degree value of each object operated by users with different attribute values is determined.
In an alternative embodiment, the step of determining a disparity value for each object manipulated by a user having a different attribute value comprises: for each of the plurality of attribute values, determining a probability that a user having the attribute value operates on each of the plurality of objects; and determining the difference degree value of each object operated by the user with different attribute values according to the probability corresponding to each attribute value.
The method provided by the embodiment of the present invention is described below by taking n commodities belonging to the same commodity category as an example, wherein the user attribute includes m attribute values.
1. Determining the number of browsing persons of each commodity under different attribute values according to the historical browsing records; 2. for each attribute value, adding the number of browsed people of all the commodities under the attribute value to serve as the total number of browsed people of all the commodities under the attribute value; 3. dividing the browsing times of the commodities under the attribute value by the total browsing times of all the commodities under the attribute value to obtain the probability of the operation of the user of the attribute value on each commodity in the n commodities.
By sijRepresenting the browsing times of the jth commodity under the ith attribute value, wherein i is more than or equal to 1 and less than or equal to n, j is more than 1 and less than or equal to m, and S is usedjRepresents the total number of browsed persons of all the commodities under the ith attribute value, and is represented by PijAnd if the probability that the user representing the ith attribute value browses the jth commodity is higher than the preset threshold, the following steps are carried out:
in an alternative embodiment, the step of determining the difference degree value of each object operated by the user with different attribute values according to the probability corresponding to each attribute value comprises determining the difference degree value according to the following formula (1):
wherein D isijRepresenting said difference measure, PijRepresenting the probability of a user having the ith attribute value operating on the jth object,
and the average value of the probabilities of the j-th object operated by the users from i to n is represented by i & lt1 & gti & ltm & lt1 & ltj & ltn, wherein m represents the number of the attribute values, and n represents the number of the objects.
In alternative embodiments, the difference value may be a deviation, and its physical meaning is easy to understand and easy to perform subsequent processing according to its physical meaning.
The method for evaluating the influence of the user attributes provided by the embodiment of the invention utilizes the probability (namely P) of the crowd with different attribute values operating different objectsij) To calculate a disparity value (i.e., D) for each object operated by a user having a different attribute valueij) The influence caused by different lengths of time taken by historical behavior records and different numbers of people in groups under different attribute values is avoided, and the statistical significance is achieved.
In an optional embodiment, before determining the probability of the user of each attribute value operating on a different object, the method further comprises: and carrying out duplicate removal processing on the historical behavior records so as to screen out repeated operation of the same user on the same object.
Optionally, the history browsing record comprises an object identification and a user ID. And carrying out deduplication processing on the historical behavior record according to the object identification and the user ID so as to screen out repeated operation of the same user on the same object, namely, the repeated operation of the same user on the same object is only calculated once. Therefore, the influence of repeated operation of the same object by some noisy users on the calculation of the influence of the user attribute can be eliminated.
Step S103: and determining the influence of the user attribute according to the difference degree value of each object operated by the user with different attribute values.
In an alternative embodiment, the user attribute influence is determined according to the following equation (2):
wherein W represents the user attribute influence, and K represents a normalization coefficient.
The method for evaluating the influence of the user attribute provided by the embodiment of the invention utilizes the difference degree value linearly related to the influence of the user attribute, so that the method can utilize the accumulated sum of the difference degree values to calculate the influence of the user attribute, and has the advantages of simple algorithm, small calculated amount and clear physical significance.
In an alternative embodiment, the normalization coefficient K may be determined from the above-mentioned difference degree values, for example, the normalization coefficient may be a limit value of the sum of the above-mentioned difference degree values, and the limit value may be determined according to the following formula:
the normalized coefficient K may be 2 m-2.
The evaluation method for the influence of the user attribute provided by the embodiment of the invention converts the influence of the user attribute into the numerical value in the [0,1] interval through the normalization coefficient so as to eliminate the influence caused by the number of the attribute values in the user attribute and enhance the universality.
According to the method for evaluating the user attribute influence, the calculated difference degree value is linearly related to the user attribute influence, so that the method can measure the user attribute influence by using the accumulated sum of the difference degree values, the algorithm is simple, the calculated amount is small, and the physical significance is clear; the difference degree value is calculated by utilizing the probability of the operation of the crowds with different attribute values on different objects, so that the influence caused by different time lengths of historical behavior records and different crowds under different attribute values is avoided, the probability is simple to calculate, the calculation complexity is reduced, the calculation amount is reduced, and the influence of the user attribute on large-scale data can be calculated quickly; the normalization coefficient is determined according to the number of the attribute values in the user attribute, so that the influence of the number of the attribute values in the user attribute can be eliminated, and the universality is enhanced.
The following describes a method for evaluating influence of user attributes according to an embodiment of the present invention with a specific example.
There are three commodities within a class: A. b, C are provided. The user attribute is a gender attribute, and the attribute values are three: male, female and unknown.
The following table 1 shows the browsing history of all the commodities in the category over two months.
Table 1:
the numbers in table 1 are the number of viewers for each item at different attribute values.
And adding the number of browsed people of all the commodities under the attribute value to serve as the total number of browsed people of all the commodities under the attribute value aiming at each attribute value. The attribute value is male, and the total number of people browsing all the commodities is 1000; the attribute value is female, and the total number of browsing persons of all the commodities is 800; the attribute value is unknown, and the total number of people browsing all the commodities is 500.
For each attribute value, the probability of each product viewed by the user of the attribute value and the mean value of the probabilities are calculated according to equation (3), as shown in table 2 below.
Table 2:
the difference degree value of each object operated by the user having a different attribute value is calculated according to equation (1), as shown in table 3 below.
Table 3:
the normalized coefficient K2 x 3-2 x 4 is calculated.
Calculating the influence W of the user attribute on browsing the commodities in the category according to the formula (2):
fig. 2 is a schematic diagram of main blocks of an apparatus for evaluating influence of user attributes according to an embodiment of the present invention. As shown in fig. 2, the apparatus 200 for evaluating influence of user attributes according to the embodiment of the present invention mainly includes: a behavior record acquisition module 201, a difference degree value determination module 202 and an influence determination module 203.
The behavior record obtaining module 201 is configured to obtain historical behavior records of multiple objects, where the multiple objects belong to the same object class, the historical behavior records include user attributes, and the user attributes include multiple attribute values;
a difference degree value determining module 202, configured to determine, based on the historical behavior record, a difference degree value of each object operated by a user with a different attribute value;
and the influence determining module 203 is configured to determine the influence of the user attribute according to the difference degree value and the normalization coefficient.
In alternative embodiments, the variance value determination module 202 is configured to:
for each of the plurality of attribute values, determining a probability that a user having the attribute value operates on each of the plurality of objects;
and determining the difference degree value of each object operated by the user with different attribute values according to the probability corresponding to each attribute value.
In an alternative embodiment, the variance value determination module 202 determines the variance value according to equation (1) below,
wherein D isijRepresenting said difference measure, PijRepresenting the probability of a user having the ith attribute value operating on the jth object,
and the average value of the probabilities of the j-th object operated by the users from i to n is represented by 1 < i ≦ m, 1 < j ≦ n, m represents the number of the attribute values, and n represents the number of the objects.
In an alternative embodiment, the influence determination module 203 determines the user attribute influence according to equation (2) below:
wherein W represents the user attribute influence, and K represents a normalization coefficient.
In an alternative embodiment, the normalization factor K is 2 m-2.
In an optional embodiment, the evaluation apparatus 200 further includes a deduplication module, configured to perform deduplication processing on the historical behavior record to screen out duplicate operations on the same object by the same user.
According to the evaluation device for the user attribute influence, the calculated difference degree value is linearly related to the user attribute influence, so that the method can measure the user attribute influence by using the accumulated sum of the difference degree values, the algorithm is simple, the calculated amount is small, and the physical significance is clear; the difference degree value is calculated by utilizing the probability of the operation of the crowds with different attribute values on different objects, so that the influence caused by different time lengths of historical behavior records and different crowds under different attribute values is avoided, the probability is simple to calculate, the calculation complexity is reduced, the calculation amount is reduced, and the influence of the user attribute on large-scale data can be calculated quickly; the normalization coefficient is determined according to the number of the attribute values in the user attribute, so that the influence of the number of the attribute values in the user attribute can be eliminated, and the universality is enhanced.
The device for evaluating the influence of the user attribute provided by the embodiment of the invention can be used for executing the method provided by the embodiment of the invention, and has corresponding functional modules and beneficial effects of the executing method. For technical details that are not described in detail in this embodiment, reference may be made to the method provided by the embodiment of the present invention.
Fig. 3 illustrates an exemplary system architecture 300 of a user attribute influencing method or user attribute influencing device to which embodiments of the present invention may be applied.
As shown in fig. 3, the system architecture 300 may include terminal devices 301, 302, 303, a network 304, and a server 305. The network 304 serves as a medium for providing communication links between the terminal devices 301, 302, 303 and the server 305. Network 304 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.
The user may use the terminal device 301, 302, 303 to interact with the server 305 via the network 304 to receive or send messages or the like. The terminal devices 301, 302, 303 may have various communication client applications installed thereon, such as shopping applications, web browser applications, search applications, instant messaging tools, mailbox clients, social platform software, and the like.
The terminal devices 301, 302, 303 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 305 may be a server providing various services, such as a background management server providing support for shopping websites browsed by the user using the terminal devices 301, 302, 303. The background 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 (e.g., target push information and product information) to the terminal device.
It should be noted that the method for evaluating the influence of the user attribute provided by the embodiment of the present invention is generally executed by the server 305, and accordingly, the apparatus for evaluating the influence of the user attribute is generally disposed in the server 305.
It should be understood that the number of terminal devices, networks, and servers in fig. 3 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
Referring now to FIG. 4, a block diagram of a computer system 400 suitable for use with a terminal device implementing an embodiment of the invention is shown. The terminal device shown in fig. 4 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present invention.
As shown in fig. 4, the computer system 400 includes a Central Processing Unit (CPU)401 that can perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM)402 or a program loaded from a storage section 408 into a Random Access Memory (RAM) 403. In the RAM 403, various programs and data necessary for the operation of the system 400 are also stored. The CPU 401, ROM 402, and RAM 403 are connected to each other via a bus 404. An input/output (I/O) interface 405 is also connected to bus 404.
The following components are connected to the I/O interface 405: an input section 406 including a keyboard, a mouse, and the like; an output section 407 including a display device such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage section 408 including a hard disk and the like; and a communication section 409 including a network interface card such as a LAN card, a modem, or the like. The communication section 409 performs communication processing via a network such as the internet. A driver 410 is also connected to the I/O interface 405 as needed. A removable medium 411 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 410 as necessary, so that a computer program read out therefrom is mounted into the storage section 408 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 may be downloaded and installed from a network through the communication section 409, and/or installed from the removable medium 411. The computer program performs the above-described functions defined in the system of the present invention when executed by a Central Processing Unit (CPU) 401.
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 module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams 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 modules described in the embodiments of the present invention may be implemented by software or hardware. The described modules may also be provided in a processor, which may be described as: a processor includes a sending module obtaining module, a determining module, and a first processing module. The names of these modules do not form a limitation on the modules themselves in some cases, and for example, the sending module may also be described as a "module sending a picture acquisition request to a connected server".
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 comprise:
obtaining historical behavior records of a plurality of objects, wherein the objects belong to the same object class, the historical behavior records comprise user attributes, and the user attributes comprise a plurality of attribute values;
determining a difference degree value of each object operated by users with different attribute values based on the historical behavior record;
and determining the influence of the user attribute according to the difference degree value of each object operated by the user with different attribute values.
According to the technical scheme of the embodiment of the invention, the influence of the user attribute on the object operation can be accurately measured. According to the technical scheme, the calculation complexity is obviously reduced, the calculation amount is small, the user attribute influence can be calculated on large-scale data quickly, the calculated influence is consistent with the actual situation, and the universality is enhanced.
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 for evaluating influence of user attributes is characterized by comprising the following steps:
obtaining historical behavior records of a plurality of objects, wherein the objects belong to the same object class, the historical behavior records comprise user attributes, and the user attributes comprise a plurality of attribute values;
determining a difference degree value of each object operated by users with different attribute values based on the historical behavior record; determining a difference degree value of the each object operated by the user having the different attribute value includes: for each of the plurality of attribute values, determining a probability that a user having the attribute value operates on each of the plurality of objects; determining the difference degree value of each object operated by users with different attribute values according to the probability corresponding to each attribute value; determining the degree of difference value according to the following formula (1),
wherein D isijRepresenting said difference measure, PijRepresenting the probability of a user having the ith attribute value operating on the jth object,
an average value representing probabilities of the user's operation on the jth object from i-1 to i-n, 1 < i ≦ m, 1 < j ≦ n, m representing the number of the plurality of attribute valuesA number, n, representing the number of said plurality of objects;
and determining the influence of the user attribute according to the difference degree value of each object operated by the user with different attribute values.
2. The method of claim 1, wherein the user attribute influence is determined according to the following equation (2):
wherein W represents the user attribute influence, and K represents a normalization coefficient.
3. The method according to claim 2, characterized in that the normalization coefficient K is 2m "2.
4. The method of any of claims 1-3, wherein prior to determining a disparity value for each object operated by a user having a different attribute value, the method further comprises:
and carrying out duplicate removal processing on the historical behavior records so as to screen out repeated operation of the same user on the same object.
5. An apparatus for evaluating influence of a user attribute, comprising:
the behavior record acquisition module is used for acquiring historical behavior records of a plurality of objects, wherein the objects belong to the same object class, the historical behavior records comprise user attributes, and the user attributes comprise a plurality of attribute values;
the difference degree value determining module is used for determining the difference degree value of each object operated by the user with different attribute values based on the historical behavior record; the difference degree value determination module is used for: for each of the plurality of attribute values, determining a probability that a user having the attribute value operates on each of the plurality of objects; determining the difference degree value of each object operated by users with different attribute values according to the probability corresponding to each attribute value; the degree of difference value determination module determines the degree of difference value according to the following equation (1),
wherein D isijRepresenting said difference measure, PijRepresenting the probability of a user having the ith attribute value operating on the jth object,
a mean value representing probabilities of the j-th object being operated by the user from i-1 to i-n, 1 < i ≦ m, 1 < j ≦ n, m representing the number of the plurality of attribute values, and n representing the number of the plurality of objects;
and the influence determining module is used for determining the influence of the user attribute according to the difference degree value of each object operated by the user with different attribute values.
6. The apparatus of claim 5, wherein the influence determination module determines the user attribute influence according to equation (2):
wherein W represents the user attribute influence, and K represents a normalization coefficient.
7. The apparatus of claim 6, wherein the normalization coefficient K is 2m "2.
8. The apparatus according to any one of claims 5-7, further comprising a deduplication module configured to perform deduplication processing on the historical behavior record to screen duplicate operations on the same object by the same 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 1-4.
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 1-4.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710992405.1A CN110020211B (en) | 2017-10-23 | 2017-10-23 | Method and device for evaluating influence of user attributes |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710992405.1A CN110020211B (en) | 2017-10-23 | 2017-10-23 | Method and device for evaluating influence of user attributes |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110020211A CN110020211A (en) | 2019-07-16 |
| CN110020211B true CN110020211B (en) | 2021-08-17 |
Family
ID=67186694
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710992405.1A Active CN110020211B (en) | 2017-10-23 | 2017-10-23 | Method and device for evaluating influence of user attributes |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110020211B (en) |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104102730A (en) * | 2014-07-24 | 2014-10-15 | 中国软件与技术服务股份有限公司 | Known label-based big data normal mode extracting method and system |
| CN104933157A (en) * | 2015-06-26 | 2015-09-23 | 百度在线网络技术（北京）有限公司 | Method and device used for obtaining user attribute information, and server |
| CN105095267A (en) * | 2014-05-09 | 2015-11-25 | 阿里巴巴集团控股有限公司 | User involving project recommendation method and apparatus |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102902691B (en) * | 2011-07-28 | 2016-09-07 | 上海拉手信息技术有限公司 | Recommend method and system |
| US9569785B2 (en) * | 2012-11-21 | 2017-02-14 | Marketo, Inc. | Method for adjusting content of a webpage in real time based on users online behavior and profile |
| CN104699725B (en) * | 2013-12-10 | 2018-10-09 | 阿里巴巴集团控股有限公司 | data search processing method and system |
-
2017
- 2017-10-23 CN CN201710992405.1A patent/CN110020211B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105095267A (en) * | 2014-05-09 | 2015-11-25 | 阿里巴巴集团控股有限公司 | User involving project recommendation method and apparatus |
| CN104102730A (en) * | 2014-07-24 | 2014-10-15 | 中国软件与技术服务股份有限公司 | Known label-based big data normal mode extracting method and system |
| CN104933157A (en) * | 2015-06-26 | 2015-09-23 | 百度在线网络技术（北京）有限公司 | Method and device used for obtaining user attribute information, and server |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110020211A (en) | 2019-07-16 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US10747771B2 (en) | Method and apparatus for determining hot event | |
| CN107451832B (en) | Method and device for pushing information | |
| CN108595448B (en) | Information pushing method and device | |
| CN108810047B (en) | Method and device for determining information push accuracy rate and server | |
| CN111160847B (en) | Method and device for processing flow information | |
| CN107291835B (en) | Search term recommendation method and device | |
| CN108512674B (en) | Method, device and equipment for outputting information | |
| WO2022156589A1 (en) | Method and device for determining live broadcast click rate | |
| CN113495991A (en) | Recommendation method and device | |
| CN107357847B (en) | Data processing method and device | |
| CN110020211B (en) | Method and device for evaluating influence of user attributes | |
| CN107483595B (en) | Information pushing method and device | |
| CN113722593B (en) | Event data processing method, device, electronic equipment and medium | |
| CN110827101A (en) | Shop recommendation method and device | |
| CN111666481B (en) | Data mining method, device, computer readable medium and electronic equipment | |
| CN109961304B (en) | Method and apparatus for generating information | |
| CN110378714B (en) | Method and device for processing access data | |
| CN112184370A (en) | Method and device for pushing product | |
| CN109947830B (en) | Method and apparatus for outputting information | |
| CN113763080A (en) | Method and device for determining recommended article, electronic equipment and storage medium | |
| CN113780703A (en) | Index adjusting method and device | |
| CN112989276A (en) | Evaluation method and device of information push system | |
| CN110866040B (en) | User portrait generation method, device and system | |
| CN108846743B (en) | Method and apparatus for generating information | |
| CN112667627B (en) | Data processing method and device |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |