CN112348300A - Method and device for pushing information - Google Patents
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Abstract
The embodiment of the application discloses a method and a device for pushing information. One embodiment of the method comprises: acquiring user information of a candidate user, wherein the user information comprises behavior information of the candidate user and information used for representing purchasing power of the candidate user; acquiring the weight corresponding to each piece of user information, and determining the score of the candidate user based on each piece of user information and the weight corresponding to each piece of user information; determining whether the candidate user is a target user based on the score of the candidate user; and in response to determining that the candidate user is the target user, executing a preset operation aiming at the candidate user, wherein the operation comprises pushing target information. The embodiment improves the pertinence of information push.
Description
Technical Field
The embodiment of the application relates to the technical field of computers, in particular to a method and a device for pushing information.
Background
Currently, in an e-commerce website, a user's life cycle is generally divided into a survey period, a formation period, a stabilization period and a degeneration period. The users (new users) in the investigation period are the update objects of the e-commerce website, and many high-quality users are not acceptable for the e-commerce website. How to attract new users has important significance to each E-commerce website and is also the key for competition of each Internet company.
Disclosure of Invention
The embodiment of the application provides a method and a device for pushing information.
In a first aspect, an embodiment of the present application provides a method for pushing information, including: acquiring user information of a candidate user, wherein the user information comprises behavior information of the candidate user and information used for representing purchasing power of the candidate user; acquiring the weight corresponding to each piece of user information, and determining the score of the candidate user based on each piece of user information and the weight corresponding to each piece of user information; determining whether the candidate user is a target user based on the score of the candidate user; and in response to determining that the candidate user is the target user, executing a preset operation aiming at the candidate user, wherein the operation comprises pushing target information.
In some embodiments, the information characterizing the purchasing power of the candidate user comprises a price interval to which a price of the candidate user's terminal device belongs; and acquiring user information of the candidate user, including: acquiring a user agent corresponding to the candidate user, and searching the network access model of the terminal equipment of the candidate user from the user agent; acquiring the brand name and the model of the terminal equipment by utilizing the network access model; and acquiring the price of the terminal equipment by using the brand name and the model, and determining the price interval to which the price of the terminal equipment belongs.
In some embodiments, the price interval corresponds to a preset numerical value; and determining the scores of the candidate users based on the user information and the corresponding weight of each user information, wherein the determining comprises the following steps: determining the product of a numerical value corresponding to a price interval to which the price of the terminal equipment belongs and the corresponding weight as a first sub-score; determining a second sub-score of the candidate user based on the behavior information of the candidate user; and determining the sum of the first sub-score and the second sub-score as the score of the candidate user.
In some embodiments, the information characterizing the purchasing power of the candidate user comprises a price interval to which the price of the house indicated by the position of the candidate user belongs; and acquiring user information of the candidate user, including: acquiring position information of the position of a candidate user; and acquiring the price of the house indicated by the position of the candidate user by using the position information, and determining the price interval to which the price of the house belongs.
In some embodiments, the price interval corresponds to a preset numerical value; and determining the scores of the candidate users based on the user information and the corresponding weight of each user information, wherein the determining comprises the following steps: determining the product of the numerical value corresponding to the price interval to which the price of the house belongs and the corresponding weight as a third sub-score; determining a second sub-score of the candidate user based on the behavior information of the candidate user; and determining the sum of the third sub-score and the second sub-score as the score of the candidate user.
In some embodiments, determining whether the candidate user is the target user based on the score of the candidate user comprises: the method comprises the steps of obtaining scores of users indicated by user identifications in a preset user identification set, wherein the user identification set comprises user identifications of candidate users; sequencing all the user identifications in the user identification set from front to back according to the sequence of scores from large to small; determining whether the user identification of the candidate user exists in the top twenty percent of the sorting result by utilizing the twenty-eight law; and if so, determining the candidate user as the target user.
In some embodiments, the weights are determined by: constructing a judgment matrix by taking the user information as each factor in a criterion layer in the analytic hierarchy process; determining the maximum eigenvalue and eigenvector of the judgment matrix; performing consistency check on the judgment matrix based on the maximum characteristic value; and if the judgment matrix is a consistency matrix, determining each numerical value in the characteristic vector as the weight of the corresponding factor.
In a second aspect, an embodiment of the present application provides an apparatus for pushing information, including: the device comprises an acquisition unit, a display unit and a display unit, wherein the acquisition unit is configured to acquire user information of a candidate user, and the user information comprises behavior information of the candidate user and information for representing purchasing power of the candidate user; a first determining unit configured to acquire weights corresponding to the respective user information and determine scores of the candidate users based on the respective user information and the weights corresponding to each user information; a second determination unit configured to determine whether the candidate user is a target user based on the score of the candidate user; the execution unit is configured to execute a preset operation aiming at the candidate user in response to the fact that the candidate user is determined to be the target user, wherein the operation comprises pushing target information.
In a third aspect, an embodiment of the present application provides an electronic device, including: one or more processors; a storage device, on which one or more programs are stored, which, when executed by the one or more processors, cause the one or more processors to implement the method as described in any implementation manner of the first aspect.
In a fourth aspect, the present application provides a computer-readable medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the method as described in any implementation manner of the first aspect.
According to the method and the device for pushing the information, the user information of the candidate user is obtained firstly, and the user information comprises the behavior information of the candidate user and the information for representing the purchasing power of the candidate user; then, acquiring the weight corresponding to each user information, and determining the score of the candidate user based on each user information and the weight corresponding to each user information; then, determining whether the candidate user is a target user or not based on the score of the candidate user; and finally, if the candidate user is determined to be the target user, executing a preset operation aiming at the candidate user, wherein the operation comprises target information pushing. By the method, users with high value can be selected from users in the stage of the investigation period, and the target information is pushed to the users, so that the pertinence of information pushing is improved.
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Other features, objects and advantages of the present application will become more apparent upon reading of the following detailed description of non-limiting embodiments thereof, made with reference to the accompanying drawings in which:
FIG. 1 is an exemplary system architecture diagram in which various embodiments of the present application may be applied;
FIG. 2 is a flow diagram of one embodiment of a method for pushing information, according to the present application;
FIG. 3 is a flow chart of determining weights in a method for pushing information according to the present application;
FIG. 4 is a hierarchical structure diagram for determining a user quality score in a method for pushing information according to the present application;
FIG. 5 is a schematic block diagram illustrating one embodiment of an apparatus for pushing information according to the present application;
FIG. 6 is a schematic block diagram of a computer system suitable for use in implementing an electronic device according to embodiments of the present application.
Detailed Description
The present application will be described in further detail with reference to the following drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the relevant invention and not restrictive of the invention. It should be noted that, for convenience of description, only the portions related to the related invention are shown in the drawings.
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.
Fig. 1 shows an exemplary system architecture 100 to which embodiments of the method for pushing information of the present application may be applied.
As shown in fig. 1, the system architecture 100 may include terminal devices 1011, 1012, 1013, networks 1021, 1022, 1023, a push server 103 and a target server 104. The network 1021 is used to provide a medium for communication links between the terminal devices 1011, 1012, 1013 and the push server 103. The network 1022 is a medium used to provide communication links between the terminal devices 1011, 1012, 1013 and the target server 104. The network 1023 serves as a medium for providing a communication link between the push server 103 and the target server 104. Networks 1021, 1022, 1023 can include various connection types, such as wired, wireless communication links, or fiber optic cables, among others.
The user may interact with the push server 103 over the network 1021 using the terminal devices 1011, 1012, 1013 to send or receive messages or the like (e.g., the push server 103 may push target information to the terminal devices 1011, 1012, 1013), etc. The user may also use the terminal devices 1011, 1012, 1013 to interact with the target server 104 via the network 1022 to send or receive messages or the like (e.g., the terminal devices 1011, 1012, 1013 may obtain item information from the target server 104), etc. Various communication client applications, such as shopping applications, search applications, instant messaging software, etc., may be installed on the terminal devices 1011, 1012, 1013.
The terminal devices 1011, 1012, 1013 may be hardware or software. When the terminal devices 1011, 1012, 1013 are hardware, they may be various electronic devices supporting information interaction, including but not limited to smart phones, tablet computers, laptop portable computers, desktop computers, and the like. When the terminal devices 1011, 1012, 1013 are software, they may be installed in the electronic devices listed above. It may be implemented as multiple pieces of software or software modules, or as a single piece of software or software module. And is not particularly limited herein.
The push server 103 may be a server that provides various services. For example, a backend server that pushes the target information to the terminal devices 1011, 1012, 1013. The push server 103 may first obtain information user information including behavior information of the candidate user and purchasing power for characterizing the candidate user; then, the weight corresponding to each user information can be obtained, and the score of the candidate user is determined based on each user information and the weight corresponding to each user information; then, whether the candidate user is a target user can be determined based on the score of the candidate user; finally, if it is determined that the candidate user is the target user, a preset operation may be performed for the candidate user, where the operation includes pushing target information to the terminal device 1011, 1012, 1013.
The push server 103 may obtain the user information of the candidate user from the target server 104. After the candidate user interacts with the target server 104 through the network 1022 using the terminal devices 1011, 1012, 1013, the target server 104 may store therein the user information of the candidate user.
The push server 103 may be hardware or software. When the push server 103 is hardware, it may be implemented as a distributed server cluster composed of multiple servers, or may be implemented as a single server. When the push server 103 is software, it may be implemented as a plurality of software or software modules (e.g., for providing distributed services), or as a single software or software module. And is not particularly limited herein.
It should be noted that the target server 104 may be hardware or software. When the target server 104 is hardware, it may be implemented as a distributed server cluster composed of multiple servers, or may be implemented as a single server. When target server 104 is software, it may be implemented as multiple pieces of software or software modules (e.g., to provide distributed services), or as a single piece of software or software module. And is not particularly limited herein.
It should be noted that the method for pushing information provided in the embodiment of the present application is generally performed by the push server 103, and accordingly, the apparatus for pushing information is generally disposed in the push server 103.
It should be further noted that the push server 103 may locally store the user information of the candidate user. The exemplary system architecture 100 may not have networks 1022, 1023 and the target server 104 present at this time.
It should be understood that the number of terminal devices, networks, push servers, and target servers in fig. 1 are merely illustrative. There may be any number of terminal devices, networks, push servers, and target servers, as desired for implementation.
With continued reference to fig. 2, a flow 200 of one embodiment of a method for pushing information in accordance with the present application is shown. The method for pushing the information comprises the following steps:
In the present embodiment, an executing subject (e.g., a push server shown in fig. 1) of the method for pushing information may acquire user information of a candidate user. The candidate users may include users (users in a research period) that are not registered in a preset application, and the preset application may be an application program supported by the execution agent. Here, the user information may include behavior information of the candidate user and information for characterizing purchasing power of the candidate user. User behavior may include, but is not limited to, at least one of: search, click, browse, add merchandise information to shopping carts and collections. The behavior information of the user may include, but is not limited to, at least one of: action name, action times and action time. Purchasing power refers to a person's ability to pay money to purchase goods or labor, or the total amount of money used to purchase goods over a period of time. The purchasing power can reflect the consuming power of the user.
In some embodiments, information (e.g., advertisement information) related to the preset application may be delivered in the target application, and if the candidate user browses information in the target application, the information related to the preset application is browsed, clicked or searched by the user, the execution body may obtain the user information of the candidate user from a target server (a server that provides support for the target application).
In this embodiment, the execution subject may obtain weights corresponding to the user information. Here, the weight may be set in advance. Then, the execution agent may determine the score of the candidate user based on the user information and the weight corresponding to each user information in the user information. Specifically, the execution main body may store a correspondence table of correspondence between user information and scores, and the execution main body may search the correspondence table for a score corresponding to each piece of user information, and then may determine a weighted average of the scores of the pieces of user information as the score of the candidate user.
In this embodiment, the executing entity may determine whether the candidate user is the target user based on the score of the candidate user determined in step 202. Specifically, the executing entity may determine whether the score of the candidate user is greater than a preset score threshold, and if so, may determine that the candidate user is the target user. Thereafter, the execution subject may perform step 204.
And step 204, responding to the determination that the candidate user is the target user, and executing preset operation aiming at the candidate user.
In this embodiment, if it is determined in step 203 that the candidate user is the target user, the execution subject may execute a preset operation for the candidate user. For example, the executing entity may set a preset permission for the user account of the candidate user, and for example, if the application is a video application, the executing entity may set an advertisement-free permission for the user account of the candidate user. If the application is a shopping application, the executive body can set the package mailing authority for the user account of the candidate user.
Here, the operation may include pushing the target information. The executing body may push the target information to the terminal device of the candidate user. The target information may include red packet information and preset coupon information (coupon information with a larger discount), for example, a 39-dollar-15-dollar coupon, a five-dollar coupon.
The executing entity may push the target information to the terminal device of the candidate user when the candidate user performs a preset second operation (e.g., register an account, add an item to a shopping cart). The execution agent may identify the candidate subscriber by an International Mobile Equipment Identity (IMEI) and/or an Identity For authentication of the candidate subscriber. The IMEI is typically referred to as a cell phone serial number, a cell phone "serial number" and is used to identify each individual mobile communication device (e.g., cell phone) in the mobile phone network, corresponding to an identification card of the mobile communication device. The IDFA is a unique identifier associated with a device, is an advertisement identifier used to track users, and can be used to open advertisements between different applications.
In some optional implementations of the embodiment, the information for characterizing the purchasing power of the candidate user may include a price interval to which a price of the terminal device of the candidate user belongs. The execution main body may obtain the user information of the candidate user as follows: the execution Agent may obtain a User Agent (UA) corresponding to the candidate User. When the candidate user accesses the target server through the terminal device, the target server can generate a user agent record, the user agent is a special character string head, is one of important information when the browser client interacts with the server, and is used for helping the website to identify an operating system and version, a CPU type, a browser and version, a browser rendering engine, a browser language, a browser plug-in and the like used by the user, so that the website can conveniently send corresponding webpage data. Then, the executing agent may search the user agent for the access model of the terminal device of the candidate user. In general, the user agent may include a network access model. The above-mentioned network access model may also be referred to as an industrial code. Then, the execution main body may obtain the brand name and the model of the terminal device by using the network access model. The execution main body may store a first correspondence table of correspondence between the network access model and both the brand name and the model. The execution subject may query a brand name and a model number corresponding to the network access model number from the first correspondence table. Then, the execution subject may obtain the price of the terminal device by using the brand name and the model. The execution body may store therein a second correspondence table of correspondences between the brand names and the model numbers and the prices. The execution subject may query the price corresponding to the brand name and the model from the second correspondence table. It should be noted that, since terminal devices of the same model may correspond to multiple memories, the price in the second correspondence table may be the price of the terminal device of the smallest memory, or may be the average value of the prices corresponding to all the memories. Finally, the executing body may determine a price interval to which the price of the terminal device belongs. The price interval may be an interval previously divided based on the price in the second correspondence table. For example, the price of the terminal device may be divided into the following price intervals: 0-500, 500-2000, 2000-5000, 5000-8000 and 8000. As an example, if the price of the terminal device is 5888, the price 5000 belongs to the price interval of 5000-. If the price of the terminal device is 10888, the price 10888 belongs to a price interval of 8000 or more.
In some optional implementation manners of this embodiment, the price interval may correspond to a preset numerical value. As an example, the price ranges 0-500 may correspond to the value 1, the price ranges 500-. Generally speaking, the higher the price in the price interval, the larger the corresponding value. The execution subject may determine the score of the candidate user based on the respective user information and the weight corresponding to each user information as follows: the executing body may first determine, as the first sub-score, a product of a numerical value corresponding to a price interval to which the price of the terminal device belongs and a corresponding weight. Then, a second sub-score of the candidate user may be determined based on the behavior information of the candidate user. Specifically, the executive body may input the behavior information of the candidate user into a pre-trained scoring model to obtain a second sub-score of the candidate user. The execution main body may further determine, for each behavior indicated by the behavior information of the candidate user, a score corresponding to the behavior by taking a product of a number of times the behavior occurs and a preset behavior weight corresponding to the behavior; then, an average of scores corresponding to the respective behaviors may be obtained as the second sub-score of the candidate user. Finally, the executing entity may determine a sum of the first sub-score and the second sub-score as the score of the candidate user.
In some optional implementations of the embodiment, the information for characterizing the purchasing power of the candidate user may include a price interval to which a price of a house indicated by the location of the candidate user belongs. The execution main body may obtain the user information of the candidate user as follows: the execution subject may obtain location information of a location where the candidate user is located, where the location information may include latitude and longitude information. Then, the price of the house indicated by the position of the candidate user can be obtained by using the position information, and the price interval to which the price of the house belongs can be determined. Here, the execution body may store a correspondence table of correspondence between the position information and the price of the house. The execution subject may look up the price of the house corresponding to the location information in the correspondence table. Then, a price interval to which the price of the house belongs can be determined. The price section may be a section divided in advance based on the house price. The price interval marked off is usually related to the city in which the house is located. For example, the price of house in Beijing can be divided into the following price intervals: less than 100 ten thousand, 100 to 200 ten thousand, 200 to 400 ten thousand, 400 to 600 ten thousand, 600 to 800 ten thousand and more than 800 ten thousand. As an example, if the price of a house is 588 ten thousand, the price 588 ten thousand belongs to a price range of 400-600 ten thousand. If the price of the house is 1088 thousands, the price of 1088 thousands belongs to the price interval of 800 or more. It should be noted that, if the location information of the locations of the plurality of candidate users is obtained, the execution main body may obtain the price of the corresponding house by using the obtained location information of the locations of the candidate users in a night time period (for example, a time period formed by eight night points to six day points).
In some optional implementation manners of this embodiment, the price interval may correspond to a preset numerical value. By way of example, a price interval of 100 ten thousand or less may correspond to a value of 1, a price interval of 100-200 ten thousand may correspond to a value of 2, a price interval of 200-400 ten thousand may correspond to a value of 3, a price interval of 400-600 ten thousand may correspond to a value of 4, a price interval of 600-800 ten thousand may correspond to a value of 5, and a price interval of 800 ten thousand or more may correspond to a value of 6. Generally speaking, the higher the price in the price interval, the larger the corresponding value. The execution subject may determine the score of the candidate user based on the respective user information and the weight corresponding to each user information as follows: the execution body may first determine, as the third sub-score, a product of a numerical value corresponding to a price section to which the price of the house belongs and a corresponding weight. Then, a second sub-score of the candidate user may be determined based on the behavior information of the candidate user. Specifically, the executive body may input the behavior information of the candidate user into a pre-trained scoring model to obtain a second sub-score of the candidate user. The execution subject may further determine, for each behavior indicated by the behavior information of the candidate user, a score corresponding to the behavior by taking a product of a number of times the behavior occurs and a preset behavior weight corresponding to the behavior; then, an average of scores corresponding to the respective behaviors may be obtained as the second sub-score of the candidate user. Finally, the executing entity may determine a sum of the third sub-score and the second sub-score as the score of the candidate user.
In some optional implementation manners of this embodiment, the executing entity may determine whether the candidate user is the target user based on the score of the candidate user by: the execution subject may obtain scores of users indicated by user identifiers in a preset user identifier set. Here, the user id in the user id set is typically a user (user in a research period) that is not registered in a preset application, and the preset application may be an application program supported by the execution subject. The user id set typically includes the user ids of the candidate users. Then, the executing body may sort the user identifiers in the user identifier set in an order from front to back according to an order from a large score to a small score. Then, it can be determined whether the user id of the candidate user exists in the first twenty percent of the ranking result by using the twenty-eight law. Two eight laws are also known as 80/20 law, Pareto's Principle, barrett's law, Juran's Principle, Critical Few Rule, Trivial majority Rule (Trivial Man Rule) least effort Rule, imbalance Rule, etc. It is meant that of any group, the most important is only a small fraction, about 20%, and the remaining 80%, although the majority, is minor. If the user id of the candidate user exists in the top twenty percent of the ranking result, the executing entity may determine the candidate user as the target user.
According to the method provided by the embodiment of the application, the high-value users are selected from the users in the investigation period stage, and the target information is pushed to the users, so that the pertinence of information pushing is improved.
With further reference to fig. 3, a flow chart 300 for determining weights in a method for pushing information is shown. The flow chart 300 for determining weights includes the steps of:
In this embodiment, the weight corresponding to each user information may be determined by an execution body for pushing information or other execution bodies for determining the weight corresponding to each user information. The execution subject may use each user information as each factor in a criterion layer in an Analytic Hierarchy Process (AHP) to construct a determination matrix. The analytic hierarchy process is a decision-making process for decomposing elements always related to decision-making into levels such as targets, criteria, indexes (schemes) and the like, and performing qualitative and quantitative analysis on the basis. The target layer contains the purpose of the decision and the problem to be solved; the criterion layer comprises the considered factors and decision criteria; the index layer contains alternatives in decision making.
Here, the user quality issues are layered. According to the value contribution of a user (the contribution of the price of terminal equipment of the user to the judgment of the quality problem of the user), the influence of the behavior contribution (the contribution of behavior information of the user to the judgment of the quality problem of the user) and the region contribution (the contribution of the price of a house indicated by the position of the user to the judgment of the quality problem of the user) on the user score is assigned with a weight, various contributions are decomposed into different composition factors, and the factors are aggregated and combined according to different levels according to the associated influence and the membership of the factors to form a multi-level analysis structure model.
Fig. 4 is a hierarchical structure diagram for deciding a user quality score in the method for pushing information according to the present application, as shown in fig. 4. In fig. 4, the index layer 401 includes a price range to which the mobile phone price belongs, a click behavior, a search behavior, a browse behavior, and a house price belong. The criteria layer 402 includes value contributions, behavior contributions, and geographic contributions. The target layer 403 includes a user quality score. Wherein, the price interval to which the mobile phone price in the index layer 401 belongs affects the value contribution in the criterion layer 402; click behavior, search behavior, and browse behavior in the index layer 401 affect behavior contributions in the criteria layer 402; the price interval to which the house price in the index layer 401 belongs affects the regional contribution in the criterion layer 402. The value contribution, behavior contribution, and regional contribution in the criteria layer 402 affect the user quality score in the target layer 403.
In this embodiment, the method for constructing the judgment matrix in the analytic hierarchy process is a consistent matrix method, that is, all the factors are not put together for comparison, but two factors are compared with each other. In contrast, relative dimensions are used to minimize the difficulty of comparing different factors of different properties with each other and to improve accuracy.
Here, the execution principal may construct the following determination matrix (1):
wherein, CijThe ratio of the importance of the element on the ith row to the importance of the element on the jth column in the matrix is determined. Here, the elements on the first row contribute C to the value1Elements on the second row contribute C to the behavior2The elements on the third line contribute C to the territory3(ii) a The elements on the first column contribute C to the value1Elements on the second column contribute C to the behavior2The elements in the third column contribute C to the territory3。C111 represents a value contribution C1With value contribution C1The ratio of the degree of importance of (A) is 1, C12Value contribution C is represented by 51And behavioral contribution C2The ratio of the degree of importance of (A) is 5, C13Value contribution C is represented by 31Contribution to the region C3The ratio of the degree of importance of (A) is 3, C210.2 represents a behavior contribution C2With value contribution C1The ratio of the degree of importance of (a) is 0.2, C221 represents a behavioral contribution C2And behavioral contribution C2The ratio of the degree of importance of (A) is 1, C230.5 represents a behavior contribution C2Contribution to the region C3Is 0.5, C310.33 represents the regional contribution C3With value contribution C1Is 0.33, C322 stands for regional contribution C3And behavioral contribution C2Is 0.33, C331 represents the regional contribution C3Contribution to the region C3The ratio of the degrees of importance of (a) is 1. Here, a ratio of 1 represents two factors having the same importanceAnd (4) sex. The ratio of 5 represents a factor that is significantly more important than another factor. The ratio 3 represents that one factor is slightly more important than the other.
In this embodiment, the execution principal may determine the maximum eigenvalue and eigenvector of the decision matrix constructed in step 301.
As an example, the executing entity may use a square root method to find the maximum eigenvalue and eigenvector of the above-mentioned decision matrix. The method comprises the following specific steps:
step one, the product of each row element of the judgment matrix (1) can be calculated by the following formula (2):
where i is the number of rows of the determination matrix, i is 1,2, n, j is the number of columns of the determination matrix, n is the maximum of the number of columns or rows, M isiIs the product of the elements of row i, CijIs the ratio of the importance of the element on the ith row to the importance of the element on the jth column in the above decision matrix (1).
Step two, the n-th root of the product of the elements in each row can be calculated by the following formula (3):
wherein,
is the n-th root of the product of the elements in row i.
Step three, if
Standardized to
Then W isiMay be the determined feature vector. Wherein,
is the n-th root of the product of the j-th column elements.
Step four, the maximum eigenvalue of the judgment matrix (1) can be determined by the following formula (4):
wherein λ ismaxIs the maximum eigenvalue (AW) of the above decision matrix (1)iIs the i-th component of decision matrix (1).
As another example, the execution subject may find the maximum eigenvalue and eigenvector of the above-described decision matrix using a sum method. The method comprises the following specific steps:
step one, each column of the judgment matrix (1) can be normalized by the following formula (5):
where i is the number of rows of the determination matrix, i is 1,2, n, j is the number of columns of the determination matrix, n is the maximum of the number of columns or rows, and D is the maximum of the number of columns or rowsjFor normalizing the value obtained by normalizing the j column element of the judgment matrix (1), CijIs the ratio of the importance of the element on the ith row to the importance of the element on the jth column in the above decision matrix (1).
Step two, elements in the matrix normalized by columns can be summed by rows.
Step three, if
Standardized to
Then N isiMay be the determined feature vector. Wherein,
is the sum of the elements in column j.
Step four, the maximum eigenvalue of the judgment matrix (1) can be determined by the following formula (6):
wherein λ ismaxIs the maximum eigenvalue (BN) of the above decision matrix (1)iIs the i-th component of decision matrix (1).
And 303, performing consistency check on the judgment matrix based on the maximum characteristic value.
In this embodiment, the execution subject may perform consistency check on the determination matrix based on the maximum eigenvalue determined in step 302.
In particular, the execution principal may introduce a consistency index
Wherein λ ismaxN is the dimension of the matrix, which is the maximum eigenvalue of the judgment matrix. If CI is 0, the judgment matrix has complete consistency; if CI is close to 0, it indicates that the above-mentioned decision matrix has satisfactory consistency. The larger the CI is, the smaller the consistency of the above determination matrix is.
It should be noted that, in order to measure the size of CI, a random consistency index RI is introduced, where RI is related to the order number of the determination matrix, and generally, the larger the order number of the matrix is, the higher the probability of occurrence of consistency random deviation is. The RI corresponding to the determination matrix may be queried by a standard value table storing a correspondence between the RI and the rank of the matrix.
Here, considering that the deviation of consistency may be caused by random reasons, when checking whether the judgment matrix has satisfactory consistency, CI is compared with the random consistency index RI to obtain a check coefficient CR, and the check coefficient CR is determined by the following formula (7):
the execution subject may compare the checking coefficient CR with 0.1, and if CR < 0.1 is determined, the determination matrix is deemed to pass the consistency check, and a consistency matrix is determined, and step 304 may be executed.
And 304, if the matrix is judged to be the consistency matrix, determining each numerical value in the characteristic vector as the weight of the corresponding factor respectively.
In this embodiment, if the matrix is determined to be a consistency matrix in step 303, the execution body may determine each value in the feature vector as the weight of the corresponding factor.
Here, when the feature vector of the determination matrix is obtained by the square root method, W may be set to WiIs determined as a weight of each factor, i.e., W can be set1Determining the weight as the first factor (value contribution), W can be2Determining the weight as the second factor (behavioral contribution), W can be determined3The weight of the third factor (regional contribution) is determined.
If the sum method is used to obtain the eigenvector of the judgment matrix, N can be usediAre determined as weights of the respective factors, i.e. N can be set1Determining the weight as the first factor (value contribution), N may be2Determining the weight as the second factor (behavioral contribution), N can be determined3The weight of the third factor (regional contribution) is determined.
According to the method provided by the embodiment of the application, the weight corresponding to each piece of user information is determined by using the analytic hierarchy process, so that the reasonability of the set weight is improved.
With further reference to fig. 5, as an implementation of the method shown in the above-mentioned figures, the present application provides an embodiment of an apparatus for pushing information, where the embodiment of the apparatus corresponds to the embodiment of the method shown in fig. 2, and the apparatus may be applied to various electronic devices.
As shown in fig. 5, the apparatus 500 for pushing information of the present embodiment includes: an acquisition unit 501, a first determination unit 502, a second determination unit 503, and an execution unit 504. The obtaining unit 501 is configured to obtain user information of a candidate user, where the user information includes behavior information of the candidate user and information for characterizing purchasing power of the candidate user; the first determination unit 502 is configured to obtain weights corresponding to the respective user information, and determine scores of the candidate users based on the respective user information and the weights corresponding to each user information; the second determination unit 503 is configured to determine whether the candidate user is the target user based on the score of the candidate user; the executing unit 504 is configured to execute a preset operation for the candidate user in response to determining that the candidate user is the target user, where the operation includes pushing target information.
In this embodiment, specific processing of the acquiring unit 501, the first determining unit 502, the second determining unit 503 and the executing unit 504 of the apparatus 500 for pushing information may refer to step 201, step 202, step 203 and step 204 in the corresponding embodiment of fig. 2.
In some optional implementations of the embodiment, the information for characterizing the purchasing power of the candidate user may include a price interval to which a price of the terminal device of the candidate user belongs. The obtaining unit 501 may obtain the user information of the candidate user as follows: the obtaining unit 501 may obtain the user agent corresponding to the candidate user. When the candidate user accesses the target server through the terminal device, the target server can generate a user agent record, the user agent is a special character string head, is one of important information when the browser client interacts with the server, and is used for helping the website to identify an operating system and version, a CPU type, a browser and version, a browser rendering engine, a browser language, a browser plug-in and the like used by the user, so that the website can conveniently send corresponding webpage data. Then, the obtaining unit 501 may search the user agent for the access model of the terminal device of the candidate user. In general, the user agent may include a network access model. The above-mentioned network access model may also be referred to as an industrial code. Then, the obtaining unit 501 may obtain the brand name and the model of the terminal device by using the network access model. The obtaining unit 501 may store a first correspondence table of correspondence between the network access model and the brand name and model. The obtaining unit 501 may query the brand name and the model number corresponding to the network access model number from the first mapping relationship table. Then, the obtaining unit 501 may obtain the price of the terminal device by using the brand name and the model. The above-mentioned obtaining unit 501 may store a second correspondence table of the correspondence between the brand name and the model number and the price. The obtaining unit 501 may query the price corresponding to the brand name and the model from the second mapping table. It should be noted that, since terminal devices of the same model may correspond to multiple memories, the price in the second correspondence table may be the price of the terminal device of the smallest memory, or may be the average value of the prices corresponding to all the memories. Finally, the obtaining unit 501 may determine a price interval to which the price of the terminal device belongs. The price interval may be an interval previously divided based on the price in the second correspondence table.
In some optional implementation manners of this embodiment, the price interval may correspond to a preset numerical value. As an example, the price ranges 0-500 may correspond to the value 1, the price ranges 500-. Generally speaking, the higher the price in the price interval, the larger the corresponding value. The first determining unit 502 may determine the score of the candidate user based on the user information and the weight corresponding to each user information as follows: the first determining unit 502 may first determine, as the first sub-score, a product of a numerical value corresponding to a price interval to which the price of the terminal device belongs and a corresponding weight. Then, a second sub-score of the candidate user may be determined based on the behavior information of the candidate user. Specifically, the first determining unit 502 may input the behavior information of the candidate user into a pre-trained scoring model, so as to obtain a second sub-score of the candidate user. The first determining unit 502 may further determine, for each behavior indicated by the behavior information of the candidate user, a score corresponding to the behavior by taking a product of a number of times the behavior occurs and a preset behavior weight corresponding to the behavior; then, an average of scores corresponding to the respective behaviors may be obtained as the second sub-score of the candidate user. Finally, the first determining unit 502 may determine a sum of the first sub-score and the second sub-score as the score of the candidate user.
In some optional implementations of the embodiment, the information for characterizing the purchasing power of the candidate user may include a price interval to which a price of a house indicated by the location of the candidate user belongs. The obtaining unit 501 may obtain the user information of the candidate user as follows: the obtaining unit 501 may obtain location information of a location where a candidate user is located, where the location information may include latitude and longitude information. Then, the price of the house indicated by the position of the candidate user can be obtained by using the position information, and the price interval to which the price of the house belongs can be determined. Here, the acquisition unit 501 may store a correspondence table of correspondence between the location information and the price of the house. The obtaining unit 501 may look up the price of the house corresponding to the location information in the correspondence table. Then, a price interval to which the price of the house belongs can be determined.
In some optional implementation manners of this embodiment, the price interval may correspond to a preset numerical value. By way of example, a price interval of 100 ten thousand or less may correspond to a value of 1, a price interval of 100-200 ten thousand may correspond to a value of 2, a price interval of 200-400 ten thousand may correspond to a value of 3, a price interval of 400-600 ten thousand may correspond to a value of 4, a price interval of 600-800 ten thousand may correspond to a value of 5, and a price interval of 800 ten thousand or more may correspond to a value of 6. Generally speaking, the higher the price in the price interval, the larger the corresponding value. The first determining unit 502 may determine the score of the candidate user based on the user information and the weight corresponding to each user information as follows: the first determining unit 502 may first determine, as the third sub-score, a product of a numerical value corresponding to a price interval to which the price of the house belongs and a corresponding weight. Then, a second sub-score of the candidate user may be determined based on the behavior information of the candidate user. Specifically, the first determining unit 502 may input the behavior information of the candidate user into a pre-trained scoring model, so as to obtain a second sub-score of the candidate user. The first determining unit 502 may further determine, for each behavior indicated by the behavior information of the candidate user, a score corresponding to the behavior by the first determining unit 502, where the product of the number of times the behavior occurs and a preset behavior weight corresponding to the behavior; then, an average of scores corresponding to the respective behaviors may be obtained as the second sub-score of the candidate user. Finally, the first determining unit 502 may determine a sum of the third sub-score and the second sub-score as the score of the candidate user.
In some optional implementations of the embodiment, the second determining unit 503 may determine whether the candidate user is the target user based on the score of the candidate user by: the second determining unit 503 may obtain scores of users indicated by user identifiers in a preset user identifier set. Here, the user id in the user id set is generally a user that is not registered in a preset application. The user id set typically includes the user ids of the candidate users. Then, the second determining unit 503 may sort the user identifiers in the user identifier set in an order from top to bottom according to the ascending order of the scores. Then, it can be determined whether the user id of the candidate user exists in the first twenty percent of the ranking result by using the twenty-eight law. The twenty-eight laws are also known as 80/20 law, pareto's law, barrett's law, zhullen's law, the key minority law, the least significant majority law, the most labor-saving law, the principle of imbalance, etc. It is meant that of any group, the most important is only a small fraction, about 20%, and the remaining 80%, although the majority, is minor. If the user id of the candidate user exists in the top twenty percent of the ranking result, the second determining unit 503 may determine the candidate user as the target user.
In some optional implementations of this embodiment, the weight may be determined as follows: firstly, constructing a judgment matrix by taking each user information as each factor in a criterion layer in an analytic hierarchy process; then, the maximum eigenvalue and eigenvector of the judgment matrix can be determined; then, consistency check can be carried out on the judgment matrix based on the maximum characteristic value; and finally, if the judgment matrix is a consistency matrix, determining each numerical value in the characteristic vector as the weight of the corresponding factor.
Referring now to fig. 6, shown is a schematic diagram of an electronic device (e.g., push server in fig. 1) 600 suitable for use in implementing embodiments of the present disclosure. The server shown is only an example and should not bring any limitation to the function and scope of use of the embodiments of the present disclosure.
As shown in fig. 6, electronic device 600 may include a processing means (e.g., central processing unit, graphics processor, etc.) 601 that may perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM)602 or a program loaded from a storage means 608 into a Random Access Memory (RAM) 603. In the RAM 603, various programs and data necessary for the operation of the electronic apparatus 600 are also stored. The processing device 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.
Generally, the following devices may be connected to the I/O interface 605: input devices 606 including, for example, a touch screen, touch pad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; output devices 607 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; storage 608 including, for example, tape, hard disk, etc.; and a communication device 609. The communication means 609 may allow the electronic device 600 to communicate with other devices wirelessly or by wire to exchange data. While fig. 6 illustrates an electronic device 600 having various means, it is to be understood that not all illustrated means are required to be implemented or provided. More or fewer devices may alternatively be implemented or provided. Each block shown in fig. 6 may represent one device or may represent multiple devices as desired.
In particular, according to an embodiment 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 via the communication means 609, or may be installed from the storage means 608, or may be installed from the ROM 602. The computer program, when executed by the processing device 601, performs the above-described functions defined in the methods of embodiments of the present disclosure. It should be noted that the computer readable medium described in the embodiments of the present disclosure may 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 embodiments of the disclosure, 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 embodiments of the present disclosure, however, a computer readable signal medium may comprise 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: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
The computer readable medium may be embodied in the electronic device; or may exist separately without being assembled into the electronic device. The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: acquiring user information of a candidate user, wherein the user information comprises behavior information of the candidate user and information used for representing purchasing power of the candidate user; acquiring the weight corresponding to each piece of user information, and determining the score of the candidate user based on each piece of user information and the weight corresponding to each piece of user information; determining whether the candidate user is a target user based on the score of the candidate user; and in response to determining that the candidate user is the target user, executing a preset operation aiming at the candidate user, wherein the operation comprises pushing target information.
Computer program code for carrying out operations for embodiments of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
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 disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in the embodiments of the present disclosure may be implemented by software or hardware. The described units may also be provided in a processor, and may be described as: a processor includes an acquisition unit, a first determination unit, a second determination unit, and an execution unit. Here, the names of these units do not constitute a limitation to the unit itself in some cases, and for example, the acquisition unit may also be described as a "unit that acquires user information of a candidate user".
The foregoing description is only exemplary of the preferred embodiments of the disclosure and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the invention in the embodiments of the present disclosure is not limited to the specific combination of the above-mentioned features, but also encompasses other embodiments in which any combination of the above-mentioned features or their equivalents is made without departing from the inventive concept as defined above. For example, the above features and (but not limited to) technical features with similar functions disclosed in the embodiments of the present disclosure are mutually replaced to form the technical solution.
Claims (10)
1. A method for pushing information, comprising:
acquiring user information of a candidate user, wherein the user information comprises behavior information of the candidate user and information used for representing purchasing power of the candidate user;
acquiring the weight corresponding to each piece of user information, and determining the score of the candidate user based on each piece of user information and the weight corresponding to each piece of user information;
determining whether the candidate user is a target user based on the score of the candidate user;
and in response to determining that the candidate user is the target user, executing a preset operation aiming at the candidate user, wherein the operation comprises pushing target information.
2. The method of claim 1, wherein the information characterizing the purchasing power of the candidate user comprises a price interval to which a price of the candidate user's terminal device belongs; and
the acquiring of the user information of the candidate user includes:
acquiring a user agent corresponding to a candidate user, and searching for the network access model of the terminal equipment of the candidate user from the user agent;
acquiring the brand name and the model of the terminal equipment by utilizing the network access model;
and acquiring the price of the terminal equipment by using the brand name and the model, and determining a price interval to which the price of the terminal equipment belongs.
3. The method of claim 2, wherein the price interval corresponds to a preset numerical value; and
determining the score of the candidate user based on the user information and the weight corresponding to each user information includes:
determining the product of a numerical value corresponding to a price interval to which the price of the terminal equipment belongs and the corresponding weight as a first sub-score;
determining a second sub-score of the candidate user based on the behavior information of the candidate user;
determining a sum of the first sub-score and the second sub-score as a score of the candidate user.
4. The method of claim 1, wherein the information characterizing the purchasing power of the candidate user comprises a price interval to which a price of a house indicated by the location of the candidate user belongs; and
the acquiring of the user information of the candidate user includes:
acquiring position information of the position of a candidate user;
and acquiring the price of the house indicated by the position of the candidate user by using the position information, and determining a price interval to which the price of the house belongs.
5. The method of claim 4, wherein the price interval corresponds to a preset numerical value; and
determining the score of the candidate user based on the user information and the weight corresponding to each user information includes:
determining the product of the numerical value corresponding to the price interval to which the price of the house belongs and the corresponding weight as a third sub-score;
determining a second sub-score of the candidate user based on the behavior information of the candidate user;
determining a sum of the third sub-score and the second sub-score as the score of the candidate user.
6. The method of claim 1, wherein the determining whether the candidate user is a target user based on the score of the candidate user comprises:
acquiring scores of users indicated by user identifications in a preset user identification set, wherein the user identification set comprises the user identifications of the candidate users;
sequencing each user identifier in the user identifier set from front to back according to the sequence of scores from large to small;
determining whether the user identification of the candidate user exists in the top twenty percent of the sorting result by utilizing the twenty-eight law;
and if so, determining the candidate user as a target user.
7. The method according to one of claims 1 to 6, wherein the weight is determined by:
constructing a judgment matrix by taking the user information as each factor in a criterion layer in the analytic hierarchy process;
determining the maximum eigenvalue and eigenvector of the judgment matrix;
performing consistency check on the judgment matrix based on the maximum eigenvalue;
and if the judgment matrix is a consistency matrix, determining each numerical value in the characteristic vector as the weight of the corresponding factor respectively.
8. An apparatus for pushing information, comprising:
an acquisition unit configured to acquire user information of a candidate user, wherein the user information includes behavior information of the candidate user and information for characterizing purchasing power of the candidate user;
a first determining unit configured to acquire weights corresponding to respective user information and determine scores of the candidate users based on the respective user information and the weights corresponding to each user information;
a second determination unit configured to determine whether the candidate user is a target user based on the score of the candidate user;
the execution unit is configured to execute a preset operation aiming at the candidate user in response to the fact that the candidate user is determined to be the target user, wherein the operation comprises pushing target information.
9. An electronic device, comprising:
one or more processors;
a storage device having one or more programs stored thereon,
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-7.
10. A computer-readable medium, on which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1-7.
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