CN111353825B - Message transmission method and device - Google Patents

Message transmission method and device Download PDF

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CN111353825B
CN111353825B CN202010161611.XA CN202010161611A CN111353825B CN 111353825 B CN111353825 B CN 111353825B CN 202010161611 A CN202010161611 A CN 202010161611A CN 111353825 B CN111353825 B CN 111353825B
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target object
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CN111353825A (en
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周江
向柳
卢剑锋
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Beijing Aibee Technology Co Ltd
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Abstract

The invention discloses a message transmission method and a message transmission device, which can obtain user identity information and behavior information of various behaviors of a user aiming at a target object; obtaining a user characteristic vector according to the user identity information and the behavior information; predicting the probability that the user does not conduct a first preset type of behavior on the target object in a first preset time period after the current moment according to the user feature vector; predicting behavior data of a second preset type of behavior of the user for the target object in a second preset time period after the current moment according to the user feature vector, wherein the second preset time period is the same as or different from the first preset time period; screening the users according to the predicted probability and the predicted behavior data; and sending a preset message to at least one screened user. The invention avoids the problem of large service system operation burden caused by pushing any message to all users by sending the message to the screened users in a targeted manner.

Description

Message transmission method and device
Technical Field
The present invention relates to the field of information technologies, and in particular, to a method and an apparatus for transmitting a message.
Background
With the continuous development of information technology, service providers can push various service messages to users so that users can know and use services provided by the service providers in time. For example: the video service provider may push a message to the user for an update of a hot episode so that the user may view the updated content of the hot episode after obtaining the message.
Currently, for a certain service message, a service provider pushes the service message to all users, when the number of users is large, the service message is easily pushed to all users, and the service system of the service provider is easily operated and is overloaded, so that the technical problem of service system breakdown is caused, and the normal experience of the users is further affected.
Disclosure of Invention
In view of the above problems, the present invention provides a message transmission method and apparatus for overcoming the above problems or at least partially solving the above problems, and the technical solution is as follows:
a message transmission method, comprising:
obtaining user identity information and behavior information of various behaviors of a user aiming at a target object;
obtaining a user characteristic vector according to the user identity information and the behavior information;
Predicting the probability that the user does not conduct a first preset type of behavior on the target object in a first preset time period after the current moment according to the user feature vector;
predicting behavior data of a second preset type of behavior of the user for the target object in a second preset time period after the current moment according to the user feature vector, wherein the second preset time period is the same as or different from the first preset time period;
screening the users according to the predicted probability and the predicted behavior data;
and sending a preset message to at least one screened user.
Optionally, the obtaining the user identity information and behavior information of multiple behaviors of the user for the target object includes:
obtaining user identity information of a target user and behavior information of the target user on various behaviors of a target object, wherein the target user is a user who does not conduct a third preset type of behavior on the target object in a third preset time period before the current moment, and the third preset type is the same as the second preset type or the third preset type is the same as the first preset type.
Optionally, the method further comprises:
predicting the probability of performing the second preset type of behavior on the target object after the user receives the preset message according to the user feature vector.
Optionally, the predicting, according to the user feature vector, a probability that the user does not perform a first preset type of behavior for the target object in a first preset time period after the current moment includes:
and inputting the user characteristic vector into a pre-trained first behavior probability prediction model, and obtaining the probability that the user predicted by the first behavior probability prediction model does not perform a first preset type of behavior on the target object in a first preset time period after the current moment.
Optionally, the predicting, according to the user feature vector, behavior data of a second preset type of behavior of the user for the target object in a second preset time period after the current moment includes:
and inputting the user characteristic vector into a pre-trained behavior data prediction model to obtain behavior data of a second preset type of behavior of the target object in a second preset time period after the current moment of the user predicted by the data prediction model.
Optionally, predicting, according to the user feature vector, a probability of the user performing the second preset type of behavior with respect to the target object after receiving the preset message includes:
and inputting the user feature vector into a pre-trained second behavior prediction probability model, and obtaining the probability of the second preset type of behavior of the target object after the user predicted by the second behavior prediction probability model receives the preset message.
Optionally, predicting, according to the user feature vector, a probability of the user performing the second preset type of behavior with respect to the target object after receiving the preset message includes:
inputting the user feature vector into a pre-trained message click prediction model, and obtaining the click probability of clicking after the user predicted by the message click prediction model receives the preset message;
inputting the user feature vector into a pre-trained message feedback prediction model, and obtaining feedback probability of feedback after the user predicted by the message feedback prediction model receives the preset message;
and determining the probability of performing the second preset type of behavior on the target object after the user receives the preset message according to the click probability and the feedback probability.
A message transmission apparatus comprising: an information obtaining unit, a user feature vector obtaining unit, a first probability obtaining unit, a behavior data obtaining unit, a user screening unit and a message sending unit,
the information obtaining unit is used for obtaining user identity information and behavior information of various behaviors of a user aiming at a target object;
the user characteristic vector obtaining unit is used for obtaining a user characteristic vector according to the user identity information and the behavior information;
the first probability obtaining unit is used for predicting the probability that the user does not conduct a first preset type of behavior on the target object in a first preset time period after the current moment according to the user characteristic vector;
the behavior data obtaining unit is used for predicting behavior data of a second preset type of behavior of the target object in a second preset time period after the current moment of a user according to the user feature vector, wherein the second preset time period is the same as or different from the first preset time period;
the user screening unit is used for screening the user according to the predicted probability and the predicted behavior data;
The message sending unit is used for sending preset messages to at least one screened user.
Optionally, the information obtaining unit is specifically configured to obtain user identity information of a target user and behavior information of multiple behaviors of the target user for a target object, where the target user is a user who does not perform a third preset type of behavior for the target object in a third preset time period before a current time, and the third preset type is the same as the second preset type, or the third preset type is the same as the first preset type.
Optionally, the apparatus may further include: a second probability obtaining unit configured to obtain a second probability,
the second probability obtaining unit is configured to predict, according to the user feature vector, a probability of performing the second preset type of behavior on the target object after the user receives the preset message.
By means of the technical scheme, the message transmission method and the device provided by the invention can obtain the user identity information and the behavior information of various behaviors of the user aiming at the target object; obtaining a user characteristic vector according to the user identity information and the behavior information; predicting the probability that the user does not conduct a first preset type of behavior on the target object in a first preset time period after the current moment according to the user feature vector; predicting behavior data of a second preset type of behavior of the user for the target object in a second preset time period after the current moment according to the user feature vector, wherein the second preset time period is the same as or different from the first preset time period; screening the users according to the predicted probability and the predicted behavior data; and sending a preset message to at least one screened user. The invention avoids the problem of large service system operation burden caused by pushing any message to all users by sending the message to the screened users in a targeted manner.
The foregoing description is only an overview of the present invention, and is intended to be implemented in accordance with the teachings of the present invention in order that the same may be more clearly understood and to make the same and other objects, features and advantages of the present invention more readily apparent.
Drawings
Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to designate like parts throughout the figures. In the drawings:
fig. 1 is a schematic flow chart of a message transmission method according to an embodiment of the present invention;
fig. 2 is a flow chart illustrating another message transmission method according to an embodiment of the present invention;
fig. 3 is a schematic flow chart of another message transmission method according to an embodiment of the present invention;
fig. 4 is a flow chart illustrating another message transmission method according to an embodiment of the present invention;
fig. 5 is a schematic flow chart of another message transmission method according to an embodiment of the present invention;
Fig. 6 is a flow chart illustrating another message transmission method according to an embodiment of the present invention;
fig. 7 is a schematic flow chart of another message transmission method according to an embodiment of the present invention;
fig. 8 is a schematic structural diagram of a message transmission device according to an embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
As shown in fig. 1, a message transmission method provided by an embodiment of the present invention may include:
s100, obtaining user identity information and behavior information of various behaviors of a user aiming at a target object.
The user identity information may include, among other things, morphological information, physiological information, and other information of the user. For example: the user identity information may include information of the user's age, gender, height, weight, blood pressure, body temperature, etc. The target object may be a service provided by a service provider. For example: video, music, novels, etc. The behavior information may include a series of operations performed by the user with respect to the target object. For example: click, play, subscribe, screen capture, download, etc.
It can be understood that, according to the embodiment of the present invention, the application scenario is different, and the information, the target object and the behavior information included in the user identity information may be different. For example: in an optional application scenario of the embodiment of the present invention, the target object may be a store, and the behavior information of the user for multiple behaviors of the target object may include: how many days the user was first transacted at the store, how many days the user was last transacted at the store, the total number of orders the user was at the store, etc.
The user identity information, the target object and the behavior information in the embodiment of the present invention may be determined according to the actual needs of the service provider, which is not further limited herein.
S200, obtaining a user characteristic vector according to the user identity information and the behavior information.
Specifically, the embodiment of the invention can take the user identity information and the behavior information as the user characteristic vector. The embodiment of the invention can obtain the user characteristic vector after sequencing the user identity information and the behavior information according to a specific sequence. For example, when the user identity information includes age and gender, and the behavior information includes play and subscription, the user feature vector may be "gender, age, subscription, play".
S300, predicting the probability that the user does not conduct a first preset type of behavior on the target object in a first preset time period after the current moment according to the user characteristic vector.
The first preset time period may be set according to a requirement of a service provider. The behavior of the first preset type may be set according to the requirements of the service provider. For example: the video service provider needs to predict the probability that the user will not play a certain television play within a week after the current time, and the embodiment of the present invention may set the first preset time to seven days and the first preset type to play. The first preset type of behavior may be a behavior that a user uses a service provided by a service provider, for example: a user uses a video service provided by a video service provider, the video service comprising: video information browsing, video playing, video collection, video downloading, video uploading and other services. If the user does not use the service provided by the service provider in the first preset time period after the current time, the user has lost in the first preset time period after the current time, so the probability that the user does not perform the first preset type of behavior on the target object in the first preset time period after the current time can be understood as follows: user churn probability.
Optionally, based on the method shown in fig. 1, as shown in fig. 2, another message transmission method provided in the embodiment of the present invention, step S300 may include:
s310, inputting the user feature vector into a pre-trained first behavior probability prediction model, and obtaining the probability that the first behavior probability prediction model predicts that the user does not perform a first preset type of behavior on the target object in a first preset time period after the current moment.
The first behavioral probability prediction model may be a deep neural network (Deep Neural Network, DNN) model, among others.
The training process of the first behavior probability prediction model in the embodiment of the invention can comprise the following steps:
obtaining a user characteristic training vector marked with a behavior identifier, wherein the behavior identifier comprises a behavior of which the user does not conduct a first preset type aiming at a target object in a first preset time period, or a behavior of which the user conducts a first preset type aiming at the target object in the first preset time period;
and performing machine learning on the user feature training vector to obtain a first behavior probability prediction model, wherein the input of the first behavior probability prediction model is the user feature vector, and the output of the first behavior probability prediction model is the probability that the user does not perform a first preset type of behavior on the target object in a first preset time period. It will be appreciated that this probability is a predictive value.
It should be noted that in the actual situation, since there may be a difference between the user feature vector and the user feature training vector, it is difficult for the trained first behavior probability prediction model to directly determine, according to the user feature vector, whether the user does not perform the first preset type of behavior for the target object in the first preset time period, and predict the probability that the user does not perform the first preset type of behavior for the target object in the first preset time period. For example: according to the embodiment of the invention, the behavior of the user which does not conduct the first preset type on the target object in the first preset time period can be taken as 1, the behavior of the user which conducts the first preset type on the target object in the first preset time period can be taken as 0, and as the user training vector and the user characteristic vector possibly have differences, the probability that the first behavior probability prediction model predicts that the user does not conduct the first preset type on the target object in the first preset time period after the current moment is between 0 and 1. For example: the probability that the user corresponding to the user feature vector does not perform the first preset type of behavior on the target object in the first preset time period is 0.73, and the probability that the user corresponding to the user feature vector performs the first preset type of behavior on the target object in the first preset time period is 0.27. It can be understood that, for the same user feature vector, the sum of the probability that the user corresponding to the user feature vector does not perform the first preset type of behavior for the target object in the first preset time period and the probability that the user performs the first preset type of behavior for the target object in the first preset time period is 1.
Optionally, the embodiment of the present invention may set a first threshold, and when the probability that the user does not perform the first preset type of behavior on the target object in the first preset time period after the current time according to the user feature vector is predicted to be greater than the first threshold, it may be determined that the user does not perform the first preset type of behavior on the target object in the first preset time period after the current time, otherwise, it may be determined that the user performs the first preset type of behavior on the target object in the first preset time period after the current time.
S400, predicting behavior data of a second preset type of behavior of the user for the target object in a second preset time period after the current moment according to the user feature vector, wherein the second preset time period is the same as or different from the first preset time period.
The second preset time period may be set according to a requirement of a service provider. In general, the first preset time period is the same as the second preset time period. The second preset type of behavior may be set according to the needs of the service provider. For example: the video service provider needs to predict the number of times that the user downloads a certain television show within a week after the current time, and the embodiment of the present invention may set the second preset time to seven days and the second preset type to download. The second preset type of behavior may be a behavior that the user uses a certain service provided by the service provider, where the service may be a service that the service provider most wants the user to use. For example: the video services provided for the video service provider include: video information browsing, video playing, video collection, video downloading, video uploading and other services. The service most desired to be used by the user by the video service provider is video playing, and the second preset type of behavior may be behavior of using the video playing service provided by the video service provider by the user, for example: the video is viewed on a video website. Since the user uses the service that the service provider most wants the user to use, the user can be understood to have a certain value to the service provider, and the behavior data of the user performing the second preset type of behavior for the target object in the second preset time period after the current moment can be understood as: user value.
Optionally, based on the method shown in fig. 1, as shown in fig. 3, another message transmission method provided in the embodiment of the present invention, step S400 may include:
s410, inputting the user feature vector into a pre-trained behavior data prediction model, and obtaining behavior data of a second preset type of behavior of the target object, which is predicted by the data prediction model to be performed by a user in a second preset time period after the current moment.
The behavioral data prediction model may be a deep neural network (Deep Neural Network, DNN) model, among others.
The training process of the behavior data prediction model in the embodiment of the invention can comprise the following steps:
obtaining a user characteristic training vector marked with behavior data, wherein the behavior data is data of a second preset type of behavior of a user aiming at a target object in a second preset time period;
and performing machine learning on the user characteristic training vector to obtain a behavior data prediction model, wherein the input of the behavior data prediction model is the user characteristic vector, and the output of the behavior data prediction model is behavior data of a second preset type of behavior of the user aiming at the target object in a second preset time period.
It should be noted that in the actual situation, since there may be a difference between the user feature vector and the user feature training vector, it is difficult for the trained behavior data prediction model to directly determine, according to the user feature vector, behavior data of a second preset type of behavior of the user with respect to the target object in the second preset time period, and the behavior data of the second preset type of behavior of the user with respect to the target object in the second preset time period may be predicted. For example: the behavior data corresponding to the user feature training vector for training the behavior data prediction model with the highest similarity to the user feature vector is 5.3, and the behavior data predicted by the behavior data prediction model for the user feature vector may be 5.38.
Optionally, according to the embodiment of the invention, integers can be reserved for the behavior data according to preset rules according to different application scenes. The preset rule may be to round the data after the decimal point. For example: when the behavior data prediction model predicts that the behavior data of the video downloaded by the user in the second preset time period after the current time is 5.78, the behavior data prediction model can output that the behavior data of the video downloaded by the user in the second preset time period after the current time is 6.
It should be understood that fig. 1 shows only an alternative execution sequence of step S300 and step S400, and step S400 may be executed before step S300 or may be executed simultaneously with step S300, and the execution sequence of step S300 and step S400 is not limited in this embodiment of the present invention.
S500, screening the users according to the predicted probability and the predicted behavior data.
Specifically, the embodiment of the invention can screen out the users of which the predicted probability meets the preset probability condition and the predicted behavior data meets the preset behavior data condition. The preset probability condition may be that the predicted probability is greater than a preset probability threshold. The preset behavior data condition may be that the predicted behavior data is greater than a preset behavior data threshold. The preset probability threshold and the preset behavior data threshold may be set according to the needs of the service provider. For example: when the preset probability threshold is 0.5 and the preset behavior data threshold is 7, the embodiment of the invention can screen out the users with the predicted probability greater than 0.5 and the predicted behavior data greater than 7.
S600, sending a preset message to at least one screened user.
Wherein the preset message may be content that can be presented on the user's mobile device. Specifically, the preset message may be content related to the target object. For example: TV play update, medicine taking reminding, member expiration reminding and the like.
The message transmission method provided by the embodiment of the invention can obtain the user identity information and the behavior information of various behaviors of the user aiming at the target object; obtaining a user characteristic vector according to the user identity information and the behavior information; predicting the probability that the user does not conduct a first preset type of behavior on the target object in a first preset time period after the current moment according to the user feature vector; predicting behavior data of a second preset type of behavior of the user for the target object in a second preset time period after the current moment according to the user feature vector, wherein the second preset time period is the same as or different from the first preset time period; screening the users according to the predicted probability and the predicted behavior data; and sending a preset message to at least one screened user. According to the embodiment of the invention, the message is sent to the screened user in a targeted manner, so that the user with the requirement can obtain the message, and the problem of large service system operation burden caused by pushing any message to all users is avoided.
It can be appreciated that in the message transmission method provided by the embodiment of the invention, the user is screened through the user identity information and the behavior information aiming at the target object, and the preset message is sent to the screened user, so that the user who really has a demand on the target object can obtain the preset message, message harassment caused by the user who has no demand or has weaker demand on the target object is avoided, and the use experience of the user is further improved. In order to intuitively understand the beneficial effects of a message transmission method provided by the embodiment of the present invention, an optional scenario of the embodiment of the present invention is described herein: aiming at a certain TV play of a weekly update collection, the embodiment of the invention can screen out the interesting users of the TV play to push the messages according to the behavior information such as subscription, clicking play, complete watching of feature content, and watching of highlight gathering of the TV play of the user, thereby saving resources of a service system and avoiding message harassment to other users while realizing accurate message push.
In an optional application scenario of the embodiment of the present invention, the market may use the probability predicted in step S300 as the user loss probability, and evaluate the user value by using the behavior data predicted in step S400, so as to screen out the users worth sending coupons according to the user loss probability and the user value, and send coupons to these users.
Optionally, based on the method shown in fig. 1, as shown in fig. 4, another message transmission method provided in the embodiment of the present invention, step S100 may include:
s110, obtaining user identity information of a target user and behavior information of the target user on various behaviors of a target object, wherein the target user is a user who does not conduct a third preset type of behavior on the target object in a third preset time period before the current moment, and the third preset type is the same as the second preset type or the third preset type is the same as the first preset type.
It can be understood that, in order to make the execution results of steps S300 to S400 more accurate, it is necessary to take, as the user feature vector, a behavior of the target user that is not performed for the target object in a third preset time period before the current time. For ease of understanding, this is illustrated by way of example: if a user subscribes to the update of a television play, but the user only watches the first episode after subscribing to the television play and has not watched the television play for 20 days, and the third preset time period is set to 14 days and the third preset type is set to play according to the needs of a service provider, the embodiment of the invention can obtain the behavior information that the user does not play the television play in 14 days, and uses the behavior information as a user feature vector. According to the embodiment of the invention, the target user does not conduct the behavior of the third preset type aiming at the target object in the third preset time period before the current moment to serve as the user target feature vector, so that the execution results of the following steps S300 to S400 are more accurate.
Further, in order to prevent a large number of users from performing a second preset type of behavior on a target object after receiving a preset message to cause a sudden increase in access traffic of a short-time service system in a short time, resulting in a crash of the service system, another message transmission method may be provided in an embodiment of the present invention, as shown in fig. 5, where the method may further include:
s700, predicting the probability of performing the second preset type of behavior on the target object after the user receives the preset message according to the user feature vector.
Specifically, as shown in fig. 6, in another message transmission method provided in the embodiment of the present invention, step S700 may include:
s710, inputting the user feature vector into a pre-trained second behavior prediction probability model, and obtaining the probability of the second behavior prediction probability model predicting the second preset type of behavior of the target object after the user receives the preset message.
The second behavior prediction probability model may be a Click-Through-Rate (CTR) model based on a deep learning algorithm. The Deep learning algorithm can be a gradient lifting iterative decision tree (GradientBoostingDecisionTree, GBDT) algorithm, a factorizer (Factorization Machine, FM) algorithm, a generalized linear Deep neural network (Wide Linear and Deep Netural Network) algorithm and the like.
The embodiment of the invention can combine whether the previous user performs the second preset type of behavior on the target object after receiving the message and performs machine learning on the corresponding user characteristic training vector to obtain a second behavior prediction probability model. It should be noted that in the actual situation, because there may be a difference between the user feature vector and the user feature training vector, it is difficult for the trained second behavior prediction probability model to directly determine whether to perform the second preset type of behavior on the target object after the user receives the preset message, so that the probability of performing the second preset type of behavior on the target object after the user receives the preset message may be predicted. For example: the embodiment of the invention can predict whether the probability of playing the television play is carried out after the user receives the preset message of the television play update.
Optionally, as shown in fig. 7, in another message transmission method provided by the embodiment of the present invention, step S700 may include:
s720, inputting the user feature vector into a pre-trained message click prediction model, and obtaining click probability of the message click prediction model for predicting click after the user receives the preset message.
The message Click prediction model may be a Click-Through-Rate (CTR) model based on a deep learning algorithm. The Deep learning algorithm may be a gradient boosting iterative decision tree (Gradient Boosting Decision Tree, GBDT) algorithm, a factorizer (Factorization Machine, FM) algorithm, a generalized linear Deep neural network (Wide Linear and Deep Netural Network, wide & Deep) algorithm, and the like.
The embodiment of the invention can combine the prior art that whether the user clicks after receiving the preset message and the corresponding user feature vector to perform machine learning to obtain the message clicking prediction model. It should be noted that in actual situations, because there may be a difference between the user feature vector and the user feature training vector, it is difficult for the trained message click prediction model to directly determine whether the user clicks after receiving the preset message, and the click probability of clicking after receiving the preset message may be predicted.
S730, inputting the user feature vector into a pre-trained message feedback prediction model, and obtaining feedback probability of the message feedback prediction model for predicting feedback after the user receives the preset message.
The message feedback prediction model may be a Click-Through-Rate (CTR) model based on a deep learning algorithm. The Deep learning algorithm may be a gradient boosting iterative decision tree (Gradient Boosting Decision Tree, GBDT) algorithm, a factorizer (Factorization Machine, FM) algorithm, a generalized linear Deep neural network (Wide Linear and Deep Netural Network, wide & Deep) algorithm, and the like.
The embodiment of the invention can combine the feedback of the user after receiving the preset message and the machine learning of the corresponding user characteristic training vector to obtain the message feedback prediction model. It should be noted that in actual situations, because there may be a difference between the user feature vector and the user feature training vector, it is difficult for the trained message feedback prediction model to directly determine whether the user performs feedback after receiving the preset message, so that the feedback probability of the user performing feedback after receiving the preset message may be predicted.
S740, determining the probability of performing the second preset type of behavior on the target object after the user receives the preset message according to the click probability and the feedback probability.
Specifically, the embodiment of the invention can multiply the click probability with the feedback probability to obtain a product, and the product is used as the probability of performing the second preset type of behavior on the target object after the user receives the preset message.
For ease of understanding, this is illustrated by way of example: the embodiment of the invention can obtain the probability that the user clicks the preset message after receiving the preset message updated by the television play, and obtain the probability that the user feeds back after receiving the preset message, wherein the feedback can be that the user returns to the television play page, and then the probability that the user plays the television play is calculated according to the clicking probability and the feedback probability.
It can be appreciated that, because the present invention can selectively send messages to the screened users, benefits can be brought to service providers in addition to reducing the burden of the service system, and the maximum benefits can be obtained by taking the mall distribution coupon as an example to calculate how the coupon is distributed.
In an optional application scenario of the embodiment of the present invention, the market may use the click probability in step S720 as the coupon capturing rate of the user for capturing the coupon after sending the coupon to the user, and the feedback probability in step S730 as the verification rate of the user using the coupon after sending the coupon to the user, and finally determine the probability of the user consuming from the coupon to the market according to the coupon capturing rate and the verification rate.
According to the embodiment of the invention, the probability of the second preset type of behavior for the target object after the user receives the preset message is predicted, so that the access flow of the service system can be estimated in advance, the service system is upgraded and reformed in advance, and the service system is prevented from collapsing.
When the embodiment of the invention is applied to a scene of distributing coupons in a mall, the preference degree of users on different types of coupons and the income maximization of users to the consumption of the mall through the coupons also need to be considered in the limited various types of coupons. The embodiment of the invention can realize the optimal distribution of coupons through the following formula:
the constraint conditions are as follows:i∈I。
wherein,for the selected user group, wherein the user group comprises a user number u, I is the total number of coupons, I is one type of coupons, and C i For the total number of coupons of one type, +.>For whether to send a coupon of type i to user u +.>For the purpose of sending the benefit of the type i coupon to user u, wherein +.>The step S400 may be performed for predicting behavior data of the user performing the second preset type of behavior on the target object in a second preset period of time after the current time, or the step S700 may be performed for predicting probability of performing the second preset type of behavior on the target object after the user receives the preset message.
Corresponding to the above method embodiment, the embodiment of the present invention further provides a message transmission device, where the structure of the message transmission device is shown in fig. 8, and the message transmission device may include: an information obtaining unit 100, a user feature vector obtaining unit 200, a first probability obtaining unit 300, a behavior data obtaining unit 400, a user screening unit 500, and a message transmitting unit 600.
The information obtaining unit 100 is configured to obtain user identity information and behavior information of multiple behaviors of a user with respect to a target object.
The user identity information may include, among other things, morphological information, physiological information, and other information of the user. For example: the user identity information may include information of the user's age, gender, height, weight, blood pressure, body temperature, etc. The target object may be a service provided by a service provider. For example: video, music, novels, etc. The behavior information may include a series of operations performed by the user with respect to the target object. For example: click, play, subscribe, screen capture, download, etc.
The user feature vector obtaining unit 200 is configured to obtain a user feature vector according to the user identity information and the behavior information.
Specifically, the embodiment of the invention can take the user identity information and the behavior information as the user characteristic vector. The embodiment of the invention can obtain the user characteristic vector after sequencing the user identity information and the behavior information according to a specific sequence.
The first probability obtaining unit 300 is configured to predict, according to the user feature vector, a probability that the user does not perform a first preset type of behavior for the target object in a first preset time period after the current time.
The first preset time period may be set according to a requirement of a service provider. The behavior of the first preset type may be set according to the requirements of the service provider. The first preset type of behavior may be a behavior that a user uses a service provided by a service provider.
Optionally, the first probability obtaining unit 300 is specifically configured to input the user feature vector into a pre-trained first behavior probability prediction model, and obtain a probability that the user predicted by the first behavior probability prediction model does not perform a first preset type of behavior for the target object in a first preset time period after the current time.
The first behavioral probability prediction model may be a deep neural network (Deep Neural Network, DNN) model, among others.
Optionally, the embodiment of the present invention may set a first threshold, and when the probability that the user does not perform the first preset type of behavior on the target object in the first preset time period after the current time according to the user feature vector is predicted to be greater than the first threshold, it may be determined that the user does not perform the first preset type of behavior on the target object in the first preset time period after the current time, otherwise, it may be determined that the user performs the first preset type of behavior on the target object in the first preset time period after the current time.
The behavior data obtaining unit 400 is configured to predict, according to the user feature vector, behavior data of a second preset type of behavior of the target object in a second preset time period after the current time, where the second preset time period is the same as or different from the first preset time period.
The second preset time period may be set according to a requirement of a service provider. In general, the first preset time period is the same as the second preset time period. The second preset type of behavior may be set according to the needs of the service provider.
The second preset type of behavior may be a behavior that the user uses a certain service provided by the service provider, where the service may be a service that the service provider most wants the user to use.
Optionally, the behavior data obtaining unit 400 is specifically configured to input the user feature vector into a pre-trained behavior data prediction model, and obtain behavior data of a second preset type of behavior of the target object, where the behavior data is predicted by the data prediction model and is performed by the user for a second preset time period after the current time.
The behavioral data prediction model may be a deep neural network (Deep Neural Network, DNN) model, among others.
The user screening unit 500 is configured to screen the user according to the predicted probability and the predicted behavior data.
Specifically, the embodiment of the invention can screen out the users of which the predicted probability meets the preset probability condition and the predicted behavior data meets the preset behavior data condition. The preset probability condition may be that the predicted probability is greater than a preset probability threshold. The preset behavior data condition may be that the predicted behavior data is greater than a preset behavior data threshold. The preset probability threshold and the preset behavior data threshold may be set according to the needs of the service provider.
The message sending unit 600 is configured to send a preset message to at least one filtered user.
Wherein the preset message may be content that can be presented on the user's mobile device. Specifically, the preset message may be content related to the target object.
The message transmission device provided by the embodiment of the invention can obtain the user identity information and the behavior information of various behaviors of the user aiming at the target object; obtaining a user characteristic vector according to the user identity information and the behavior information; predicting the probability that the user does not conduct a first preset type of behavior on the target object in a first preset time period after the current moment according to the user feature vector; predicting behavior data of a second preset type of behavior of the user for the target object in a second preset time period after the current moment according to the user feature vector, wherein the second preset time period is the same as or different from the first preset time period; screening the users according to the predicted probability and the predicted behavior data; and sending a preset message to at least one screened user. According to the embodiment of the invention, the message is sent to the screened user in a targeted manner, so that the user with the requirement can obtain the message, and the problem of large service system operation burden caused by pushing any message to all users is avoided.
Optionally, the information obtaining unit 100 is specifically configured to obtain user identity information of a target user and behavior information of multiple behaviors of the target user for a target object, where the target user is a user who does not perform a third preset type of behavior for the target object in a third preset time period before a current time, and the third preset type is the same as the second preset type, or the third preset type is the same as the first preset type.
Optionally, the message transmission device provided by the embodiment of the present invention may further include: and a second probability obtaining unit.
The second probability obtaining unit is configured to predict, according to the user feature vector, a probability of performing the second preset type of behavior on the target object after the user receives the preset message.
Optionally, the second probability obtaining unit is specifically configured to input the user feature vector into a pre-trained second behavior prediction probability model, and obtain a probability of the second preset type of behavior of the target object after the user predicted by the second behavior prediction probability model receives the preset message.
The second behavior prediction probability model may be a Click-Through-Rate (CTR) model based on a deep learning algorithm. The Deep learning algorithm can be a gradient lifting iterative decision tree (GradientBoostingDecisionTree, GBDT) algorithm, a factorizer (Factorization Machine, FM) algorithm, a generalized linear Deep neural network (Wide Linear and Deep Netural Network) algorithm and the like.
Optionally, the second probability obtaining unit includes: the device comprises a click probability obtaining subunit, a feedback probability obtaining subunit and a second probability obtaining subunit.
The click probability obtaining subunit is configured to input the user feature vector into a pre-trained message click prediction model, and obtain a click probability of clicking the message click prediction model after the user predicted by the message click prediction model receives the preset message.
The message Click prediction model may be a Click-Through-Rate (CTR) model based on a deep learning algorithm. The Deep learning algorithm may be a gradient boosting iterative decision tree (Gradient Boosting Decision Tree, GBDT) algorithm, a factorizer (Factorization Machine, FM) algorithm, a generalized linear Deep neural network (Wide Linear and Deep Netural Network, wide & Deep) algorithm, and the like.
The feedback probability obtaining subunit is configured to input the user feature vector into a pre-trained message feedback prediction model, and obtain feedback probability that the user predicted by the message feedback prediction model performs feedback after receiving the preset message.
The message feedback prediction model may be a Click-Through-Rate (CTR) model based on a deep learning algorithm. The Deep learning algorithm may be a gradient boosting iterative decision tree (Gradient Boosting Decision Tree, GBDT) algorithm, a factorizer (Factorization Machine, FM) algorithm, a generalized linear Deep neural network (Wide Linear and Deep Netural Network, wide & Deep) algorithm, and the like.
The second probability obtaining subunit is configured to determine, according to the click probability and the feedback probability, a probability of performing the second preset type of behavior with respect to the target object after the user receives the preset message.
Specifically, the second probability obtaining subunit may multiply the click probability with the feedback probability to obtain a product, and use the product as the probability of performing the second preset type of behavior on the target object after the user receives the preset message.
Optionally, another message transmission device provided by the embodiment of the present invention may further include: a dispensing unit.
The distribution unit is used for executing an algorithm for optimal distribution of coupons.
The message transmission apparatus includes a processor and a memory, the above-mentioned information obtaining unit 100, the user feature vector obtaining unit 200, the first probability obtaining unit 300, the behavior data obtaining unit 400, the user screening unit 500, the message transmitting unit 600, and the like are stored in the memory as program units, and the above-mentioned program units stored in the memory are executed by the processor to realize the corresponding functions.
The processor includes a kernel, and the kernel fetches the corresponding program unit from the memory. The kernel can set one or more kernel parameters to send the message to the screened user in a targeted manner.
The embodiment of the invention provides a storage medium on which a program is stored, which when executed by a processor implements the message transmission method.
The embodiment of the invention provides a processor which is used for running a program, wherein the message transmission method is executed when the program runs.
The embodiment of the invention provides equipment, which comprises at least one processor, at least one memory and a bus, wherein the at least one memory and the bus are connected with the processor; the processor and the memory complete communication with each other through a bus; the processor is configured to invoke the program instructions in the memory to perform the message transmission method described above. The device herein may be a server, PC, PAD, cell phone, etc.
The present application also provides a computer program product adapted to perform, when executed on a data processing device, a program initialized with the method steps of: (steps of method claim, independent + dependent).
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In one typical configuration, the device includes one or more processors (CPUs), memory, and a bus. The device may also include input/output interfaces, network interfaces, and the like.
The memory may include volatile memory, random Access Memory (RAM), and/or nonvolatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM), among other forms in computer readable media, the memory including at least one memory chip. Memory is an example of a computer-readable medium.
Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises an element.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The foregoing is merely exemplary of the present application and is not intended to limit the present application. Various modifications and changes may be made to the present application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc. which are within the spirit and principles of the present application are intended to be included within the scope of the claims of the present application.

Claims (8)

1. A method of message transmission, comprising:
obtaining user identity information and behavior information of various behaviors of a user aiming at a target object;
obtaining a user characteristic vector according to the user identity information and the behavior information;
predicting the probability that the user does not conduct a first preset type of behavior on the target object in a first preset time period after the current moment according to the user feature vector;
predicting behavior data of a second preset type of behavior of the user for the target object in a second preset time period after the current moment according to the user feature vector, wherein the second preset time period is the same as or different from the first preset time period;
screening the users according to the predicted probability and the predicted behavior data;
sending a preset message to at least one screened user;
predicting the probability of performing the second preset type of behavior on the target object after the user receives the preset message according to the user feature vector.
2. The method according to claim 1, wherein obtaining the user identity information and behavior information of the plurality of behaviors of the user with respect to the target object comprises:
Obtaining user identity information of a target user and behavior information of the target user on various behaviors of a target object, wherein the target user is a user who does not conduct a third preset type of behavior on the target object in a third preset time period before the current moment, and the third preset type is the same as the second preset type or the third preset type is the same as the first preset type.
3. The method of claim 1, wherein predicting, from the user feature vector, a probability that a user does not conduct a first preset type of behavior for the target object for a first preset time period after a current time, comprises:
and inputting the user characteristic vector into a pre-trained first behavior probability prediction model, and obtaining the probability that the user predicted by the first behavior probability prediction model does not perform a first preset type of behavior on the target object in a first preset time period after the current moment.
4. The method according to claim 1, wherein predicting behavior data of a second preset type of behavior of the user for the target object in a second preset time period after the current moment according to the user feature vector comprises:
And inputting the user characteristic vector into a pre-trained behavior data prediction model to obtain behavior data of a second preset type of behavior of the target object in a second preset time period after the current moment of the user predicted by the data prediction model.
5. The method according to claim 1, wherein predicting the probability of the second preset type of behavior for the target object after the user receives the preset message according to the user feature vector comprises:
and inputting the user feature vector into a pre-trained second behavior prediction probability model, and obtaining the probability of the second preset type of behavior of the target object after the user predicted by the second behavior prediction probability model receives the preset message.
6. The method according to claim 1, wherein predicting the probability of the second preset type of behavior for the target object after the user receives the preset message according to the user feature vector comprises:
inputting the user feature vector into a pre-trained message click prediction model, and obtaining the click probability of clicking after the user predicted by the message click prediction model receives the preset message;
Inputting the user feature vector into a pre-trained message feedback prediction model, and obtaining feedback probability of feedback after the user predicted by the message feedback prediction model receives the preset message;
and determining the probability of performing the second preset type of behavior on the target object after the user receives the preset message according to the click probability and the feedback probability.
7. A message transmission apparatus, comprising: the system comprises an information obtaining unit, a user characteristic vector obtaining unit, a first probability obtaining unit, a behavior data obtaining unit, a user screening unit, a message sending unit and a second probability obtaining unit;
the information obtaining unit is used for obtaining user identity information and behavior information of various behaviors of a user aiming at a target object;
the user characteristic vector obtaining unit is used for obtaining a user characteristic vector according to the user identity information and the behavior information;
the first probability obtaining unit is used for predicting the probability that the user does not conduct a first preset type of behavior on the target object in a first preset time period after the current moment according to the user characteristic vector;
The behavior data obtaining unit is used for predicting behavior data of a second preset type of behavior of the target object in a second preset time period after the current moment of a user according to the user feature vector, wherein the second preset time period is the same as or different from the first preset time period;
the user screening unit is used for screening the user according to the predicted probability and the predicted behavior data;
the message sending unit is used for sending preset messages to at least one screened user;
the second probability obtaining unit is configured to predict, according to the user feature vector, a probability of performing the second preset type of behavior on the target object after the user receives the preset message.
8. The apparatus of claim 7, wherein the information obtaining unit is specifically configured to obtain user identity information of a target user and behavior information of multiple behaviors of the target user with respect to a target object, where the target user is a user who does not perform a third preset type of behavior with respect to the target object in a third preset period of time before a current time, and the third preset type is the same as the second preset type or the third preset type is the same as the first preset type.
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