CN111126649B - Method and device for generating information - Google Patents
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Abstract
The embodiment of the application discloses a method and a device for generating information. One embodiment of the method comprises the following steps: acquiring at least one piece of information to be displayed and the user information of a target user, wherein the information to be displayed and the user information of the target user are pushed to a terminal of the target user; for information to be displayed in the at least one piece of information to be displayed, acquiring a preset total display cost value and an expected display quantity corresponding to the information to be displayed; inputting the obtained total display cost value and expected display quantity into a pre-trained first prediction model to obtain a click rate threshold value and a single display cost value corresponding to the information to be displayed; acquiring characteristic information of the information to be displayed; and inputting the acquired characteristic information and the user information into a pre-trained second prediction model to obtain a predicted click rate for predicting the click rate of the information to be displayed. The embodiment can improve the accuracy of information generation and is beneficial to improving the pertinence of information pushing.
Description
Technical Field
The embodiment of the application relates to the technical field of computers, in particular to a method and a device for generating information.
Background
With the development of internet technology, users browse information more and more frequently through terminals, in the prior art, for a certain information, the ratio of the expected display amount of the information in a certain time period to the total display amount of the information displayed in the time period is calculated, the calculated ratio is used as the frequency of pushing information, and the information is pushed to the users according to the frequency, so that the actual display amount of the information reaches the expected display amount.
Disclosure of Invention
The embodiment of the application provides a method and a device for generating information.
In a first aspect, an embodiment of the present application provides a method for generating information, the method including: acquiring at least one piece of information to be displayed and the user information of a target user, wherein the information to be displayed and the user information of the target user are pushed to a terminal of the target user; for information to be displayed in the at least one piece of information to be displayed, acquiring a preset total display cost value and an expected display quantity corresponding to the information to be displayed; inputting the obtained total display cost value and expected display quantity into a pre-trained first prediction model to obtain a click rate threshold value and a single display cost value corresponding to the information to be displayed; acquiring characteristic information of the information to be displayed; and inputting the acquired characteristic information and the user information into a pre-trained second prediction model to obtain a predicted click rate for predicting the click rate of the information to be displayed.
In some embodiments, the method further comprises: extracting information to be displayed, of which the corresponding predicted click rate is greater than or equal to a corresponding click rate threshold value and the corresponding single-time display cost value meets a preset condition, from at least one piece of information to be displayed; and pushing the extracted information to be displayed to the terminal.
In some embodiments, the preset conditions include at least one of: the single-display cost value is the maximum value of the obtained single-display cost values; the single-display cost value is arranged at a preset position according to the obtained single-display cost value.
In some embodiments, the first predictive model is trained by: acquiring a first training sample set, wherein the first training sample comprises a sample total display cost value and a sample expected display quantity which are preset for information to be displayed of the sample, and a corresponding mark click rate threshold value and a mark single display cost value; and using a machine learning method, taking the total sample display cost value and the expected sample display quantity in the first training sample set as inputs, taking the marked click rate threshold value corresponding to the total sample display cost value and the expected sample display quantity as expected outputs, and training to obtain a first prediction model.
In some embodiments, the first predictive model is a model trained based on a linear regression model.
In some embodiments, the second predictive model is trained by: acquiring a second training sample set, wherein the second training sample comprises characteristic information of sample display information acquired in advance, sample user information of a sample user browsing the sample display information, and corresponding labeling information for representing whether the sample user clicks the sample display information; and using a machine learning method, taking the characteristic information and the sample user information in the second training sample set as input, taking the input characteristic information and the labeling information corresponding to the sample user information as expected output, and training to obtain a second prediction model.
In a second aspect, an embodiment of the present application provides an apparatus for generating information, the apparatus including: the acquisition unit is configured to acquire at least one piece of information to be displayed and the user information of the target user, wherein the information to be displayed and the user information of the target user are pushed to the terminal of the target user; the generating unit is configured to acquire a preset total display cost value and an expected display quantity corresponding to the information to be displayed for the information to be displayed in the acquired at least one piece of information to be displayed; inputting the obtained total display cost value and expected display quantity into a pre-trained first prediction model to obtain a click rate threshold value and a single display cost value corresponding to the information to be displayed; acquiring characteristic information of the information to be displayed; and inputting the acquired characteristic information and the user information into a pre-trained second prediction model to obtain a predicted click rate for predicting the click rate of the information to be displayed.
In some embodiments, the apparatus further comprises: the extraction unit is configured to extract the information to be displayed, wherein the corresponding predicted click rate is greater than or equal to the corresponding click rate threshold value and the corresponding single-time display cost value accords with the preset condition; and the pushing unit is configured to push the extracted information to be displayed to the terminal.
In some embodiments, the preset conditions include at least one of: the single-display cost value is the maximum value of the obtained single-display cost values; the single-display cost value is arranged at a preset position according to the obtained single-display cost value.
In some embodiments, the first predictive model is trained by: acquiring a first training sample set, wherein the first training sample comprises a sample total display cost value and a sample expected display quantity which are preset for information to be displayed of the sample, and a corresponding mark click rate threshold value and a mark single display cost value; and using a machine learning method, taking the total sample display cost value and the expected sample display quantity in the first training sample set as inputs, taking the marked click rate threshold value corresponding to the total sample display cost value and the expected sample display quantity as expected outputs, and training to obtain a first prediction model.
In some embodiments, the first predictive model is a model trained based on a linear regression model.
In some embodiments, the second predictive model is trained by: acquiring a second training sample set, wherein the second training sample comprises characteristic information of sample display information acquired in advance, sample user information of a sample user browsing the sample display information, and corresponding labeling information for representing whether the sample user clicks the sample display information; and using a machine learning method, taking the characteristic information and the sample user information in the second training sample set as input, taking the input characteristic information and the labeling information corresponding to the sample user information as expected output, and training to obtain a second prediction model.
In a third aspect, an embodiment of the present application provides a server, including: one or more processors; a storage device having one or more programs stored thereon; the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the method as described in any of the implementations of the first aspect.
In a fourth aspect, embodiments of the present application provide a computer readable medium having stored thereon a computer program which, when executed by a processor, implements a method as described in any of the implementations of the first aspect.
According to the method and the device for generating the information, provided by the embodiment of the application, the information to be displayed and the user information of the target user are acquired, then the total display cost value and the expected display quantity corresponding to the information to be displayed are acquired, the click rate threshold value, the single display cost value and the predicted click rate corresponding to the information to be displayed are obtained by using the first prediction model and the second prediction model, so that the accuracy of information generation can be improved, and the pertinence of information pushing by using the data output by the models can be improved.
Drawings
Other features, objects and advantages of the present application will become more apparent upon reading of the detailed description of non-limiting embodiments, made with reference to the accompanying drawings in which:
FIG. 1 is an exemplary system architecture diagram in which an embodiment of the present application may be applied;
FIG. 2 is a flow chart of one embodiment of a method for generating information according to an embodiment of the present application;
FIG. 3 is a schematic diagram of one application scenario of a method for generating information according to an embodiment of the present application;
FIG. 4 is a flow chart of yet another embodiment of a method for generating information according to an embodiment of the present application;
FIG. 5 is a schematic diagram of an embodiment of an apparatus for generating information in accordance with an embodiment of the application;
FIG. 6 is a schematic diagram of a computer system suitable for use with a server implementing an embodiment of the application.
Detailed Description
The application is described in further detail below with reference to the drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the application and are not limiting of the application. It should be noted that, for convenience of description, only the portions related to the present application are shown in the drawings.
It should be noted that, without conflict, the embodiments of the present application and features of the embodiments may be combined with each other. The application will be described in detail below with reference to the drawings in connection with embodiments.
Fig. 1 shows an exemplary system architecture 100 to which a method for generating information or an apparatus for generating information of an embodiment of the present application may be applied.
As shown in fig. 1, a system architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 is used as a medium to provide communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others.
The user may interact with the server 105 via the network 104 using the terminal devices 101, 102, 103 to receive or send messages or the like. Various communication client applications, such as a web browser application, a shopping class application, a search class application, an instant messaging tool, a mailbox client, social platform software, etc., may be installed on the terminal devices 101, 102, 103.
The terminal devices 101, 102, 103 may be hardware or software. When the terminal devices 101, 102, 103 are hardware, they may be various electronic devices with display screens, including but not limited to smartphones, tablets, laptop and desktop computers, and the like. When the terminal devices 101, 102, 103 are software, they can be installed in the above-listed electronic devices. Which may be implemented as multiple software or software modules (e.g., software or software modules for providing distributed services) or as a single software or software module. The present application is not particularly limited herein.
The server 105 may be a server providing various services, such as a background information processing server providing support for information presented on the terminal devices 101, 102, 103. The background information processing server can process the acquired information to be displayed, the user information and the like, and generate processing results (such as a click rate threshold value, a single display cost value and a predicted click rate).
It should be noted that, the method for generating information provided by the embodiment of the present application is generally performed by the server 105, and accordingly, the device for generating information is generally disposed in the server 105.
The server may be hardware or software. When the server is hardware, the server may be implemented as a distributed server cluster formed by a plurality of servers, or may be implemented as a single server. When the server is software, it may be implemented as a plurality of software or software modules (e.g., software or software modules for providing distributed services), or as a single software or software module. The present application is not particularly limited herein.
It should be understood that the number of terminal devices, networks and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
With continued reference to FIG. 2, a flow 200 of one embodiment of a method for generating information in accordance with the present application is shown. The method for generating information comprises the following steps:
step 201, obtaining at least one piece of information to be displayed and user information of a target user, wherein the information to be displayed and the user information of the target user are to be pushed to a terminal of the target user.
In this embodiment, the execution body (e.g., the server shown in fig. 1) of the method for generating information may acquire, from a remote location or from a local location, at least one piece of information to be presented and user information of the target user to be pushed to the terminal of the target user through a wired connection or a wireless connection. Wherein, the information to be displayed can include, but is not limited to, at least one of the following: pictures, text, audio, video, link addresses, etc. The target user may be a user who is to browse the information to be presented with the terminal that he uses, such as the terminal device shown in fig. 1. The user information of the target user may be used to characterize characteristics of the target user including, but not limited to, at least one of: the gender, age, interests, etc. of the target user.
Step 202, for information to be displayed in the at least one piece of information to be displayed, acquiring a preset total display cost value and an expected display quantity corresponding to the information to be displayed; inputting the obtained total display cost value and expected display quantity into a pre-trained first prediction model to obtain a click rate threshold value and a single display cost value corresponding to the information to be displayed; acquiring characteristic information of the information to be displayed; and inputting the acquired characteristic information and the user information into a pre-trained second prediction model to obtain a predicted click rate for predicting the click rate of the information to be displayed.
In this embodiment, for the information to be displayed in the at least one information to be displayed acquired in step 201, the executing body may execute the following steps for the information to be displayed:
step 2021, obtaining a preset total display cost value and an expected display quantity corresponding to the information to be displayed.
Wherein the total presentation cost value is used to characterize the total cost (e.g., price, point value, etc.) paid by the provider of the information to be presented in order to present the information to the user. The expected display amount is the preset number of users desiring to browse the information to be displayed, or the number of times that the execution body pushes the information to be displayed.
And step 2022, inputting the obtained total display cost value and the expected display quantity into a pre-trained first prediction model to obtain a click rate threshold value and a single display cost value corresponding to the information to be displayed.
Wherein the single presentation cost value is used to characterize the cost paid by a provider of information to be presented (e.g., the owner of the item indicated by the information to be presented provided to the user) for presenting the information to be presented once on the terminal used by the user. The first prediction model is used for representing the corresponding relation between the total display cost value, the expected display quantity and the click rate threshold value and the single display cost value. Specifically, as an example, the first prediction model may be a correspondence table that is preset by a technician in advance based on statistics of a large number of total display cost values, expected display amounts and click rate thresholds, and single display cost values, and stores a plurality of total display cost values, expected display amounts and corresponding click rate thresholds, and single display cost values; the model obtained by training the initial model (such as a generalized linear regression model, a locally weighted linear regression model, a neural network, etc.) by using a machine learning method based on a preset training sample may also be used. By using the first prediction model, different click rate thresholds and single display cost values can be obtained according to different total display cost values and expected display quantities of information to be displayed, so that references are provided for determining whether to push the information to be displayed to a target user.
In some optional implementations of this embodiment, the executing entity or other electronic device may train to obtain the first prediction model according to the following steps:
first, a first set of training samples is obtained. The first training sample comprises a total sample display cost value and an expected sample display quantity which are preset for information to be displayed of the sample, and a corresponding mark click rate threshold value and a mark single display cost value.
And then, using a machine learning method, taking the total display cost value of the samples and the expected display quantity of the samples in the first training sample set as inputs, taking the total display cost value of the input samples, the marked click rate threshold value corresponding to the expected display quantity of the samples and the marked single display cost value as expected outputs, and training to obtain a first prediction model.
Specifically, the first prediction model may be a model obtained by training an initial model. The initial model may be a linear regression model, a neural network model, or the like. The initial model may be provided with initial parameters that may be continuously adjusted during the training process. The execution subject for training the first prediction model may calculate a loss value based on a preset loss function, and determine whether the initial model is trained according to the loss value. Here, it should be noted that the loss value may be used to characterize the difference between the actual output and the desired output. In practice, a predetermined variety of loss functions may be used to calculate the loss value of the actual output relative to the annotated output. For example, a loss value may be calculated using a square loss function.
In some alternative implementations of the present embodiment, the first predictive model may be a model trained based on a linear regression model. The linear regression is a statistical analysis method for determining the quantitative relationship of the interdependence between two or more variables by using regression analysis in mathematical statistics, and the method is simple and widely applied.
In practice, the first predictive model may include two linear regression models, a first linear regression model and a second linear regression model. The first linear regression model may be used to characterize the correspondence of the total display cost value, the expected display amount, and the click rate threshold. The second linear regression model may be used to characterize the correspondence of total display cost, desired display quantity, and single display cost.
As an example, assume that the first linear regression model and the second linear regression model are shown as formula (1) and formula (2), respectively:
f 1 (S,C)＝CTR_th (1)
f 2 (S,C)＝bid (2)，
wherein S is the expected display quantity, C is the total display cost value, CTR_th is the click rate threshold, and bid is the single display cost value. When the first prediction model is trained, the expected display quantity of the sample and the total display cost value of the sample can be used as the input of the initial first linear regression model, the mark click rate threshold is used as the expected output of the initial first linear regression model, and the first linear regression model is trained. Taking the expected display quantity of the sample and the total display cost value of the sample as the input of the initial second linear regression model, taking the marked single display cost value as the expected output of the initial second linear regression model, and training to obtain the second linear regression model.
In this example, it is assumed that the above-described initial first linear regression model and initial second linear regression model are represented by the following formulas (3) and (4):
α1×S+α2×C＝CTR_th (3)
α3×S+α4×C＝bid (4)，
wherein α1, α2, α3, α4 are parameters of the initial first linear regression model and the initial second linear regression model, respectively. After the initial first linear regression model and the initial second linear regression model are trained, parameter values of the parameters alpha 1, alpha 2, alpha 3 and alpha 4 can be determined, so that a trained first linear regression model and a trained second linear regression model are obtained. When the execution subject inputs the total display cost value C and the expected display quantity S to the first prediction model, a click rate threshold value CTR_th and a single display cost value bid are respectively calculated by the trained first linear regression model and second linear regression model.
Alternatively, the independent and dependent variables of the first and second linear regression models may be interchanged. Namely, the first linear regression model and the second linear regression model may be represented by the following equations (5) and (6), respectively:
f 1 (CTR_th,bid)＝C (5)
f 2 (CTR_th,bid)＝S (6)，
when the first prediction model is trained, the click rate threshold value and the single display cost value are marked as the input of the initial first linear regression model, the total display cost value of the sample is used as the expected output of the initial first linear regression model, and the first linear regression model is trained. And taking the marked click rate threshold value and the marked single-time display cost value as the input of the initial second linear regression model, taking the expected sample display quantity as the expected output of the initial second linear regression model, and training to obtain the second linear regression model.
In this example, it is assumed that the above-described initial first linear regression model and initial second linear regression model are represented by the following formulas (7) and (8):
β1×CTR_th+β2×bid＝C (7)
β3×CTR_th+β4×bid＝S (8)，
wherein β1, β2, β3, β4 are parameters of the initial first linear regression model and the initial second linear regression model, respectively. After the initial first linear regression model and the initial second linear regression model are trained, parameter values of the parameters beta 1, beta 2, beta 3 and beta 4 can be determined, so that the trained first linear regression model and second linear regression model are obtained.
When the execution subject inputs the total presentation cost value C and the desired presentation quantity S to the first prediction model, the equation set consisting of the above equation (7) and equation (8) may be solved, thereby obtaining the click rate threshold ctr_th and the single presentation cost value bid.
Step 2023, obtaining the feature information of the information to be displayed.
The characteristic information is used for representing the characteristics of the information to be displayed. Features of the information to be presented may include, but are not limited to, at least one of: title, type, link address, etc. of the information to be presented.
Step 2024, inputting the obtained feature information and the user information into a pre-trained second prediction model, so as to obtain a predicted click rate for predicting the click rate of the information to be displayed.
The click rate is also called click through rate (CTR, click Through Rate), which is the result of dividing the actual number of clicks of a certain presentation information by the amount of presentation of the presentation information (e.g., the number of times the presentation information is pushed, the number of terminals receiving the presentation information, etc.). The predicted click rate may be used to characterize the probability that the information to be presented is clicked by the target user, i.e., the greater the predicted click rate, the greater the probability that the information to be presented is clicked by the target user.
The second prediction model is used for representing the corresponding relation between the characteristic information and the user information and the predicted click rate. Specifically, as an example, the second prediction model may be a correspondence table that is preset by a technician in advance based on statistics of feature information, user information, and predicted click rates of a large amount of information to be presented, and stores a plurality of feature information, user information, and corresponding predicted click rates; the model obtained by training an initial model (for example, an FM (Factorization Machine, factorizer) model, an FFM (Field-aware Factorization Machine, field-aware factorizer) model, a neural network model, or the like) by a machine learning method based on a preset training sample may be used. By using the second prediction model, the possibility that the target user clicks the information to be displayed can be accurately predicted, so that the pertinence of pushing the information to be displayed to the target user is improved.
In some optional implementations of this embodiment, the executing entity or other electronic device may train to obtain the second prediction model according to the following steps:
first, a second set of training samples is obtained. The second training sample comprises characteristic information of sample display information acquired in advance, sample user information of a sample user browsing the sample display information, and corresponding labeling information used for representing whether the sample user clicks the sample display information. As an example, the annotation information may be a number, e.g., "0" indicating that the sample user did not click on the sample presentation information and "1" indicating that the sample user clicked on the sample presentation information.
And then, using a machine learning method, taking the characteristic information and the sample user information in the second training sample set as input, taking the input characteristic information and the labeling information corresponding to the sample user information as expected output, and training to obtain a second prediction model. As an example, the output data of the second predictive model may be a value between 0 and 1, the closer the value is to 1, the greater the likelihood that the information to be presented, indicated by the characterizing information, is clicked by the user.
Specifically, the second prediction model may be a model obtained by training an initial model. The initial model may include an FM model, a neural network model, and the like. The initial model may be provided with initial parameters that may be continuously adjusted during the training process. The execution subject for training the first prediction model may calculate a loss value based on a preset loss function, and determine whether the initial model is trained according to the loss value. Here, it should be noted that the loss value may be used to characterize the difference between the actual output and the desired output. In practice, a predetermined variety of loss functions may be used to calculate the loss value of the actual output relative to the annotated output. For example, a logarithmic loss function, a cross entropy loss function, or the like may be employed to calculate the loss value.
With continued reference to fig. 3, fig. 3 is a schematic diagram of an application scenario of the method for generating information according to the present embodiment. In the application scenario of fig. 3, the server 301 first obtains, locally, three information to be presented 3021, 3022, 3023 to be pushed to the terminal of the target user, and user information 304 of the target user 303. The user information 304 includes information such as the sex, age, and the like of the target user 303. Then, the server 301 acquires a preset total display cost value 3051 (e.g., "1000"), 3052 (e.g., "2000"), 3053 (e.g., "2500"), and a desired display amount 3061 (e.g., "10000"), 3062 (e.g., "20000"), 3063 (e.g., "10000") corresponding to each piece of information to be displayed. Next, the server 301 inputs the obtained total display cost value and the desired display amount into the first predictive model 307 trained in advance, respectively, to obtain click rate thresholds 3071 (for example, "10%"), 3072 (for example, "5.9%"), 3073 (for example, "16%") and single display cost value 3081 (for example, "1.2"), 3082 (for example, "0.9"), 3083 (for example, "1.5") corresponding to each piece of information to be displayed. Then, the server 301 acquires feature information (for example, 3091, 3092, 3093 in the figure) of each information to be presented, and inputs the acquired feature information and the user information 304 into the pre-trained second prediction model 310, to obtain predicted click rates 3101 (for example, "11%"), 3102 (for example, "5%"), 3103 (for example, "13%") for predicting the click rate of each information to be presented.
According to the method provided by the embodiment of the application, the click rate threshold value, the single display cost value and the predicted click rate corresponding to the information to be displayed are obtained by using the first prediction model and the second prediction model, so that the accuracy of information generation can be improved, and the pertinence of information pushing can be improved.
With further reference to fig. 4, a flow 400 of yet another embodiment of a method for generating information is shown. The flow 400 of the method for generating information comprises the steps of:
step 401, obtaining at least one piece of information to be displayed and user information of a target user, wherein the information to be displayed and the user information of the target user are to be pushed to a terminal of the target user.
In this embodiment, step 401 is substantially identical to step 201 in the corresponding embodiment of fig. 2, and will not be described herein.
Step 402, for information to be displayed in the obtained at least one piece of information to be displayed, obtaining a preset total display cost value and an expected display quantity corresponding to the information to be displayed; inputting the obtained total display cost value and expected display quantity into a pre-trained first prediction model to obtain a click rate threshold value and a single display cost value corresponding to the information to be displayed; acquiring characteristic information of the information to be displayed; and inputting the acquired characteristic information and the user information into a pre-trained second prediction model to obtain a predicted click rate for predicting the click rate of the information to be displayed.
In this embodiment, step 402 is substantially identical to step 202 in the corresponding embodiment of fig. 2, and will not be described herein.
Step 403, extracting the information to be displayed, wherein the corresponding predicted click rate is greater than or equal to the corresponding click rate threshold value and the corresponding single-time display cost value accords with the preset condition, from the at least one information to be displayed.
In this embodiment, an execution body (for example, a server shown in fig. 1) of the method for generating information may determine, from the at least one information to be displayed, information to be displayed that the corresponding predicted click rate is greater than or equal to the corresponding click rate threshold and the corresponding single-time display cost value meets a preset condition. The click rate threshold value can be used for comparing with a predicted click rate, and when the predicted click rate corresponding to certain information to be displayed is greater than or equal to the click rate threshold value, the possibility that the information to be displayed is clicked by a target user is higher is indicated. The preset condition may be a condition preset by a technician for selecting information to be displayed from at least one information to be displayed.
In some alternative implementations of the present embodiment, the preset conditions may include, but are not limited to, at least one of:
the single-display cost value is the maximum value of the obtained single-display cost values;
The single-display cost value is arranged at a preset position according to the obtained single-display cost value.
The preset position may be a single position or a plurality of positions. For example, the preset position may be a single presentation cost value of the top N (N is a preset positive integer) bits after sorting the obtained single cost values from large to small.
And step 404, pushing the extracted information to be displayed to the terminal.
In this embodiment, the executing body may push the information to be displayed determined in step 403 to the terminal. So that the information to be displayed is displayed on the terminal.
As can be seen from fig. 4, compared to the corresponding embodiment of fig. 2, the process 400 of the method for generating information in this embodiment highlights the steps of extracting information to be displayed from at least one information to be displayed and pushing the information to be displayed. Therefore, the scheme described in the embodiment can push information to the terminal of the target user more pertinently based on the output results of the first prediction model and the second prediction model.
With further reference to fig. 5, as an implementation of the method shown in the above figures, the present application provides an embodiment of an apparatus for generating information, which corresponds to the method embodiment shown in fig. 2, and which is particularly applicable to various electronic devices.
As shown in fig. 5, the apparatus 500 for generating information of the present embodiment includes: an obtaining unit 501 configured to obtain at least one to-be-presented information to be pushed to a terminal of a target user and user information of the target user; the generating unit 502 is configured to acquire a preset total display cost value and an expected display quantity corresponding to the information to be displayed for the information to be displayed in the acquired at least one piece of information to be displayed; inputting the obtained total display cost value and expected display quantity into a pre-trained first prediction model to obtain a click rate threshold value and a single display cost value corresponding to the information to be displayed; acquiring characteristic information of the information to be displayed; and inputting the acquired characteristic information and the user information into a pre-trained second prediction model to obtain a predicted click rate for predicting the click rate of the information to be displayed.
In this embodiment, the obtaining unit 501 may obtain, from a remote location or from a local location, at least one information to be displayed and user information of the target user to be pushed to the terminal of the target user through a wired connection manner or a wireless connection manner. Wherein, the information to be displayed can include, but is not limited to, at least one of the following: pictures, text, audio, video, link addresses, etc. The target user may be a user who is to browse the information to be presented with the terminal that he uses, such as the terminal device shown in fig. 1. The user information of the target user may be used to characterize characteristics of the target user including, but not limited to, at least one of: the gender, age, interests, etc. of the target user.
In this embodiment, for the information to be displayed in the at least one information to be displayed acquired by the acquiring unit 501, the generating unit 502 may perform the following steps for the information to be displayed:
step 5021, obtaining a preset total display cost value and an expected display quantity corresponding to the information to be displayed.
The total display cost value is used for representing the cost paid by the provider of the information to be displayed for displaying the information to be displayed to the user. The expected display amount is the preset number of users desiring to browse the information to be displayed, or the number of times that the execution body pushes the information to be displayed.
And 5022, inputting the obtained total display cost value and the expected display quantity into a pre-trained first prediction model to obtain a click rate threshold value and a single display cost value corresponding to the information to be displayed.
The first prediction model is used for representing the corresponding relation between the total display cost value, the expected display quantity, the click rate threshold value and the single display cost value. Specifically, as an example, the first prediction model may be a correspondence table that is preset by a technician in advance based on statistics of a large number of total display cost values, expected display amounts and click rate thresholds, and single display cost values, and stores a plurality of total display cost values, expected display amounts and corresponding click rate thresholds, and single display cost values; the model obtained by training the initial model (such as a generalized linear regression model, a locally weighted linear regression model, a neural network, etc.) by using a machine learning method based on a preset training sample may also be used. By using the first prediction model, different click rate thresholds and single display cost values can be obtained according to different total display cost values and expected display quantities of information to be displayed, so that references are provided for determining whether to push the information to be displayed to a target user.
Step 5023, obtaining the characteristic information of the information to be displayed.
The characteristic information is used for representing the characteristics of the information to be displayed. Features of the information to be presented may include, but are not limited to, at least one of: title, type, link address, etc. of the information to be presented.
And 5024, inputting the acquired characteristic information and the user information into a pre-trained second prediction model to obtain a predicted click rate for predicting the click rate of the information to be displayed.
The click rate is also called click through rate (CTR, click Through Rate), which is the result of dividing the actual number of clicks of a certain presentation information by the amount of presentation of the presentation information (e.g., the number of times the presentation information is pushed, the number of terminals receiving the presentation information, etc.). The predicted click rate may be used to characterize the probability that the information to be presented is clicked by the target user, i.e., the greater the predicted click rate, the greater the probability that the information to be presented is clicked by the target user.
The second prediction model is used for representing the corresponding relation between the characteristic information and the user information and the predicted click rate. Specifically, as an example, the second prediction model may be a correspondence table that is preset by a technician in advance based on statistics of feature information, user information, and predicted click rates of a large amount of information to be presented, and stores a plurality of feature information, user information, and corresponding predicted click rates; the model obtained by training an initial model (for example, an FM (Factorization Machine, factorizer) model, an FFM (Field-aware Factorization Machine, field-aware factorizer) model, a neural network model, or the like) by a machine learning method based on a preset training sample may be used. By using the second prediction model, the possibility that the target user clicks the information to be displayed can be accurately predicted, so that the pertinence of pushing the information to be displayed to the target user is improved.
In some optional implementations of this embodiment, the apparatus 500 may further include: an extracting unit (not shown in the figure) configured to extract, from at least one piece of information to be displayed, for which the corresponding predicted click rate is greater than or equal to the corresponding click rate threshold value and the corresponding single-time display cost value meets a preset condition; a pushing unit (not shown in the figure) configured to push the extracted information to be presented to the terminal.
In some optional implementations of this embodiment, the preset conditions may include at least one of: the single-display cost value is the maximum value of the obtained single-display cost values; the single-display cost value is arranged at a preset position according to the obtained single-display cost value.
In some optional implementations of this embodiment, the first predictive model is trained by: acquiring a first training sample set, wherein the first training sample comprises a sample total display cost value and a sample expected display quantity which are preset for information to be displayed of the sample, and a corresponding mark click rate threshold value and a mark single display cost value; and using a machine learning method, taking the total sample display cost value and the expected sample display quantity in the first training sample set as inputs, taking the marked click rate threshold value corresponding to the total sample display cost value and the expected sample display quantity as expected outputs, and training to obtain a first prediction model.
In some embodiments, the first predictive model is a model trained based on a linear regression model.
In some embodiments, the second predictive model is trained by: acquiring a second training sample set, wherein the second training sample comprises characteristic information of sample display information acquired in advance, sample user information of a sample user browsing the sample display information, and corresponding labeling information for representing whether the sample user clicks the sample display information; and using a machine learning method, taking the characteristic information and the sample user information in the second training sample set as input, taking the input characteristic information and the labeling information corresponding to the sample user information as expected output, and training to obtain a second prediction model.
According to the device provided by the embodiment of the application, the click rate threshold value, the single display cost value and the predicted click rate corresponding to the information to be displayed are obtained by using the first prediction model and the second prediction model, so that the accuracy of information generation can be improved, and the pertinence of information pushing can be improved.
Referring now to FIG. 6, there is illustrated a schematic diagram of a computer system 600 suitable for use with a server embodying embodiments of the present application. The server illustrated in fig. 6 is merely an example, and should not be construed as limiting the functionality and scope of use of embodiments of the present application.
As shown in fig. 6, the computer system 600 includes a Central Processing Unit (CPU) 601, which can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 602 or a program loaded from a storage section 608 into a Random Access Memory (RAM) 603. In the RAM 603, various programs and data required for the operation of the system 600 are also stored. The CPU 601, ROM 602, and RAM 603 are connected to each other through a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.
The following components are connected to the I/O interface 605: an input portion 606 including a keyboard, mouse, etc.; an output portion 607 including a Liquid Crystal Display (LCD) or the like, a speaker or the like; a storage section 608 including a hard disk and the like; and a communication section 609 including a network interface card such as a LAN card, a modem, or the like. The communication section 609 performs communication processing via a network such as the internet. The drive 610 is also connected to the I/O interface 605 as needed. Removable media 611 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is installed as needed on drive 610 so that a computer program read therefrom is installed as needed into storage section 608.
In particular, according to embodiments of the present disclosure, the processes described above with reference to 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 shown in the flowcharts. In such an embodiment, the computer program may be downloaded and installed from a network through the communication portion 609, and/or installed from the removable medium 611. The above-described functions defined in the method of the present application are performed when the computer program is executed by a Central Processing Unit (CPU) 601.
It should be noted that the computer readable medium according to the present application may be a computer readable signal medium or a computer readable medium, or any combination of the two. The computer readable medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable 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 context of this document, a computer readable 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 application, however, the computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with the computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable 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.
Computer program code for carrying out operations of the present application 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 kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).
The flowcharts 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 application. 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 involved in the embodiments of the present application may be implemented in software or in hardware. The described units may also be provided in a processor, for example, described as: a processor includes an acquisition unit and a generation unit. The names of these units do not in any way constitute a limitation of the unit itself, for example, the acquisition unit may also be described as "a unit that acquires at least one piece of information to be presented to the terminal of the target user and user information of the target user".
As another aspect, the present application also provides a computer-readable medium that may be contained in the server described in the above embodiment; or may exist alone without being assembled into the server. The computer readable medium carries one or more programs which, when executed by the server, cause the server to: acquiring at least one piece of information to be displayed and the user information of a target user, wherein the information to be displayed and the user information of the target user are pushed to a terminal of the target user; for information to be displayed in the at least one piece of information to be displayed, acquiring a preset total display cost value and an expected display quantity corresponding to the information to be displayed; inputting the obtained total display cost value and expected display quantity into a pre-trained first prediction model to obtain a click rate threshold value and a single display cost value corresponding to the information to be displayed; acquiring characteristic information of the information to be displayed; and inputting the acquired characteristic information and the user information into a pre-trained second prediction model to obtain a predicted click rate for predicting the click rate of the information to be displayed.
The above description is only illustrative of the preferred embodiments of the present application and of the principles of the technology employed. It will be appreciated by persons skilled in the art that the scope of the application referred to in the present application is not limited to the specific combinations of the technical features described above, but also covers other technical features formed by any combination of the technical features described above or their equivalents without departing from the inventive concept described above. Such as the above-mentioned features and the technical features disclosed in the present application (but not limited to) having similar functions are replaced with each other.
Claims (10)
1. A method for generating information, comprising:
acquiring at least one piece of information to be displayed and pushed to a terminal of a target user and user information of the target user;
for information to be displayed in the at least one piece of information to be displayed, acquiring a preset total display cost value and an expected display quantity corresponding to the information to be displayed; inputting the obtained total display cost value and expected display quantity into a pre-trained first prediction model to obtain a click rate threshold value and a single display cost value corresponding to the information to be displayed;
acquiring characteristic information of the information to be displayed; inputting the acquired characteristic information and the user information into a pre-trained second prediction model to obtain a predicted click rate for predicting the click rate of the information to be displayed;
Extracting information to be displayed, of which the corresponding predicted click rate is greater than or equal to a corresponding click rate threshold value and the corresponding single-time display cost value meets a preset condition, from the at least one information to be displayed; the preset conditions include at least one of the following: the single-display cost value is the maximum value of the obtained single-display cost values, and the single-display cost values are arranged at preset positions according to the obtained single-display cost value;
pushing the extracted information to be displayed to the terminal.
2. The method of claim 1, wherein the first predictive model is trained by:
acquiring a first training sample set, wherein the first training sample comprises a sample total display cost value and a sample expected display quantity which are preset for information to be displayed of the sample, and a corresponding mark click rate threshold value and a mark single display cost value;
and using a machine learning method, taking the total display cost value of the samples and the expected display quantity of the samples in the first training sample set as inputs, taking the total display cost value of the input samples, a marked click rate threshold value corresponding to the expected display quantity of the samples and a marked single display cost value as expected outputs, and training to obtain a first prediction model.
3. The method according to claim 1 or 2, wherein the first predictive model is a model trained based on a linear regression model.
4. The method according to claim 1 or 2, wherein the second predictive model is trained by:
acquiring a second training sample set, wherein the second training sample comprises characteristic information of sample display information acquired in advance, sample user information of a sample user browsing the sample display information, and corresponding labeling information for representing whether the sample user clicks the sample display information;
and using a machine learning method, taking the characteristic information and the sample user information in the second training sample set as input, taking the input characteristic information and the labeling information corresponding to the sample user information as expected output, and training to obtain a second prediction model.
5. An apparatus for generating information, comprising:
the acquisition unit is configured to acquire at least one piece of information to be displayed and pushed to a terminal of a target user and user information of the target user;
the generating unit is configured to acquire a preset total display cost value and an expected display quantity corresponding to the information to be displayed for the information to be displayed in the acquired at least one piece of information to be displayed; inputting the obtained total display cost value and expected display quantity into a pre-trained first prediction model to obtain a click rate threshold value and a single display cost value corresponding to the information to be displayed;
Acquiring characteristic information of the information to be displayed; inputting the acquired characteristic information and the user information into a pre-trained second prediction model to obtain a predicted click rate for predicting the click rate of the information to be displayed;
the extraction unit is configured to extract the information to be displayed, wherein the corresponding predicted click rate is greater than or equal to the corresponding click rate threshold value and the corresponding single-time display cost value accords with a preset condition; the preset conditions include at least one of the following: the single-display cost value is the maximum value of the obtained single-display cost values, and the single-display cost values are arranged at preset positions according to the obtained single-display cost value;
and the pushing unit is configured to push the extracted information to be displayed to the terminal.
6. The apparatus of claim 5, wherein the first predictive model is trained by:
acquiring a first training sample set, wherein the first training sample comprises a sample total display cost value and a sample expected display quantity which are preset for information to be displayed of the sample, and a corresponding mark click rate threshold value and a mark single display cost value;
And using a machine learning method, taking the total display cost value of the samples and the expected display quantity of the samples in the first training sample set as inputs, taking the total display cost value of the input samples, a marked click rate threshold value corresponding to the expected display quantity of the samples and a marked single display cost value as expected outputs, and training to obtain a first prediction model.
7. The apparatus of claim 5 or 6, wherein the first predictive model is a model trained based on a linear regression model.
8. The apparatus of claim 5 or 6, wherein the second predictive model is trained by:
acquiring a second training sample set, wherein the second training sample comprises characteristic information of sample display information acquired in advance, sample user information of a sample user browsing the sample display information, and corresponding labeling information for representing whether the sample user clicks the sample display information;
and using a machine learning method, taking the characteristic information and the sample user information in the second training sample set as input, taking the input characteristic information and the labeling information corresponding to the sample user information as expected output, and training to obtain a second prediction model.
9. A server, comprising:
one or more processors;
a storage device having one or more programs stored thereon,
when executed by the one or more processors, causes the one or more processors to implement the method of any of claims 1-4.
10. A computer readable medium having stored thereon a computer program, wherein the program when executed by a processor implements the method of any of claims 1-4.
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