CN114996559A - Method, device, server and medium for generating information push mode - Google Patents
Method, device, server and medium for generating information push mode Download PDFInfo
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Abstract
The embodiment of the disclosure discloses a method, a device, a server and a medium for generating an information push mode. One embodiment of the method comprises: in response to detecting that target data is abnormal, selecting at least one factor from factors associated with the abnormal target data as a key factor, wherein the target data is used for representing the activity degree of a user group, and the factor corresponds to a category to which users in the user group belong; acquiring information associated with users in a user group corresponding to the key factor as feature data; selecting user information from the user information sets corresponding to the key factors to form candidate user information sets; and generating an information pushing mode corresponding to the user side information in the candidate user side information set according to the characteristic data. The implementation mode enriches the information pushing mode, improves the pertinence of information pushing and improves the efficiency.
Description
Technical Field
The embodiment of the disclosure relates to the technical field of computers, in particular to a method, a device, a server and a medium for generating an information push mode.
Background
With the continuous development of internet technology, the information amount increases at a geometric level, and the information push technology is also widely applied.
The existing method for pushing information by a platform often needs to manually determine details such as target people, pushing channels, pushing time, pushing contents and the like. However, the information pushing method based on manual judgment usually consumes a long time and is low in efficiency.
Disclosure of Invention
The embodiment of the disclosure provides a method, a device, a server and a medium for generating an information push mode.
In a first aspect, an embodiment of the present disclosure provides a method for generating an information push manner, where the method includes: in response to the detection of the target data abnormality, selecting at least one factor from factors associated with the abnormal target data as a key factor, wherein the target data is used for representing the activity degree of a user group, and the factor corresponds to a category to which the users in the user group belong; acquiring information associated with users in a user group corresponding to the key factors as characteristic data; selecting user side information from the user side information sets corresponding to the key factors to form candidate user side information sets; and generating an information pushing mode corresponding to the user side information in the candidate user side information set according to the characteristic data.
In some embodiments, the information pushing manner is used for indicating that information is pushed at a specific pushing time; and the method further comprises: in response to determining that the specific pushing time is reached, acquiring user behavior information corresponding to the user side information corresponding to the information pushing mode; and determining whether to correct the specific push time according to the matching of the behavior information and the preset rule.
In some embodiments, the information push manner is used to indicate that information is pushed in a specific push channel; the generating of the information pushing mode corresponding to the user side information in the candidate user side information set according to the feature data includes: generating an optional channel information set according to a preset channel information set and a current cost constraint; determining response probability corresponding to the user side information in the candidate user side information set and the channel information in the selectable channel information set according to the characteristic data; and for the user side information in the candidate user side information set, determining the channel corresponding to the channel information with the highest response probability as the specific pushing channel corresponding to the user side information.
In some embodiments, the set of channel information includes telemarketing channels; and the method further comprises: responding to the determined information pushing mode for indicating that information is pushed in a telemarketing channel, and selecting matched marketing personnel information from a preset marketing personnel information list, wherein the marketing personnel information list comprises identification of marketing personnel and corresponding characteristic labels; and sending an information pushing task corresponding to the information pushing mode to a terminal corresponding to the matched marketing staff information.
In some embodiments, the selecting at least one factor from the factors associated with the abnormal target data as the key factor in response to detecting the target data abnormality includes: acquiring target data in a preset time period; in response to determining that the fluctuation of the target data exceeds a preset fluctuation threshold, determining an influence degree corresponding to each factor associated with the target data; and selecting a target number of factors with influence degrees meeting preset conditions as key factors.
In some embodiments, the information related to the user includes user behavior data and historical information push data within a preset time from the current time; the generating of the information pushing mode corresponding to the user side information in the candidate user side information set according to the feature data includes: generating push channel information by using a pre-trained channel prediction model according to the characteristic data; generating pushing time information by utilizing a pre-trained time prediction model according to the characteristic data; generating push content information by using a pre-trained content prediction model according to the characteristic data; and generating an information pushing mode according to the pushing channel information, the pushing time information and the pushing content information.
In some embodiments, the selecting the ue information from the ue information sets corresponding to the key factors to form the candidate ue information set includes: selecting a matched information pushing target from a preset corresponding relation table according to key factors; inputting the characteristic data into a pre-trained response probability generation model, and generating the response probability of a user corresponding to the characteristic data to the matched information push target; and selecting the user side information with the response probability larger than a preset response threshold value from the user side information sets corresponding to the key factors to form a candidate user side information set.
In some embodiments, the method further comprises: establishing a folder corresponding to the matched information pushing target; generating an information pushing task according to the generated information pushing mode; storing the generated information pushing task to a corresponding folder; in response to determining that there are folders that match the key factor, the information push task in the matching folder is performed.
In some embodiments, the performing the information pushing task in the matched folder includes: dividing the user side information in the information pushing task in the matched folder into a pushing group and a comparison group; pushing information to the user side corresponding to the pushing group in the generated information pushing mode; generating information push evaluation information of a push group and a contrast group; and executing other information pushing tasks under the information pushing target in response to the fact that the information pushing evaluation information indicates that the pushing effect does not meet the requirement.
In a second aspect, an embodiment of the present disclosure provides an apparatus for generating an information pushing manner, where the apparatus includes: the first selecting unit is configured to respond to the detection of the target data abnormity, and select at least one factor from factors associated with the abnormal target data as a key factor, wherein the target data is used for representing the activity degree of the user group, and the factor corresponds to the category to which the users in the user group belong; a first acquisition unit configured to acquire information associated with users in a user group corresponding to the key factor as feature data; a second selecting unit configured to select user side information from the user side information sets corresponding to the key factors to form candidate user side information sets; and the first generating unit is configured to generate an information pushing mode corresponding to the user side information in the candidate user side information set according to the characteristic data.
In some embodiments, the information pushing manner is used for indicating that information is pushed at a specific pushing time; the device also includes: a second obtaining unit configured to obtain user behavior information corresponding to user side information corresponding to the information push manner in response to determining that the specific push time is reached; and the determining unit is configured to determine whether to correct the specific pushing time according to the matching of the behavior information and the preset rule.
In some embodiments, the information pushing manner is used for indicating that information is pushed in a specific pushing channel; the first generation unit is further configured to: generating an optional channel information set according to a preset channel information set and a current cost constraint; determining response probability corresponding to the user side information in the candidate user side information set and the channel information in the selectable channel information set according to the characteristic data; and for the user side information in the candidate user side information set, determining the channel corresponding to the channel information with the highest response probability as the specific pushing channel corresponding to the user side information.
In some embodiments, the set of channel information includes telemarketing channels; the device also includes: the third selecting unit is configured to select matched marketing personnel information from a preset marketing personnel information list in response to the fact that the information pushing mode is determined to be used for indicating that information is pushed in a telephone sales channel, wherein the marketing personnel information list comprises identification of marketing personnel and corresponding feature labels; and the sending unit is configured to send an information pushing task corresponding to the information pushing mode to the terminal corresponding to the matched marketer information.
In some embodiments, the first selecting unit is further configured to: acquiring target data in a preset time period; in response to determining that the fluctuation of the target data exceeds a preset fluctuation threshold, determining an influence degree corresponding to each factor associated with the target data; and selecting a target number of factors with influence degrees meeting preset conditions as key factors.
In some embodiments, the information related to the user includes user behavior data and historical information push data within a preset time from the current time; the first generation unit is further configured to: generating push channel information by using a pre-trained channel prediction model according to the characteristic data; generating pushing time information by utilizing a pre-trained time prediction model according to the characteristic data; generating push content information by using a pre-trained content prediction model according to the characteristic data; and generating an information pushing mode according to the pushing channel information, the pushing time information and the pushing content information.
In some embodiments, the second selecting unit is further configured to: selecting a matched information pushing target from a preset corresponding relation table according to key factors; inputting the characteristic data into a pre-trained response probability generation model, and generating the response probability of a user corresponding to the characteristic data to the matched information push target; and selecting the user side information with the response probability larger than a preset response threshold value from the user side information sets corresponding to the key factors to form a candidate user side information set.
In some embodiments, the apparatus further comprises: an establishing unit configured to establish a folder corresponding to the matched information push target; a second generation unit configured to generate an information push task according to the generated information push manner; a storage unit configured to store the generated information push task to a corresponding folder; and the execution unit is configured to respond to the determination that the folder matched with the key factors exists, and execute the information pushing task in the matched folder.
In some embodiments, the execution unit is further configured to: dividing the user side information in the information pushing task in the matched folder into a pushing group and a comparison group; pushing information to the user side corresponding to the pushing group in the generated information pushing mode; generating information push evaluation information of a push group and a contrast group; and executing other information pushing tasks under the information pushing target in response to the fact that the information pushing evaluation information indicates that the pushing effect does not meet the requirement.
In a third aspect, an embodiment of the present disclosure provides a server, including: one or more processors; a storage device having one or more programs stored thereon; when the one or more programs are executed by the one or more processors, the one or more processors are caused to implement the method as described in any implementation of the first aspect.
In a fourth aspect, embodiments of the present disclosure provide a computer-readable medium on which a computer program is stored, which when executed by a processor implements the method as described in any of the implementations of the first aspect.
According to the method, the device, the server and the medium for generating the information pushing mode, target data abnormity is used as a generation condition of the information pushing mode, and a proper target crowd generates the information pushing mode according to factors causing the target data abnormity, so that the information pushing mode is enriched, the pertinence of information pushing is improved, and the efficiency is improved.
Drawings
Other features, objects and advantages of the disclosure will become more apparent upon reading of the following detailed description of non-limiting embodiments thereof, made with reference to the accompanying drawings in which:
FIG. 1 is an exemplary system architecture diagram in which one embodiment of the present disclosure may be applied;
FIG. 2 is a flow diagram of one embodiment of a method for generating an information push approach in accordance with the present disclosure;
FIG. 3 is a schematic diagram of an application scenario of a method for generating an information push approach according to an embodiment of the present disclosure;
FIG. 4 is a flow diagram of yet another embodiment of a method for generating an information push approach in accordance with the present disclosure;
FIG. 5 is a schematic structural diagram of an embodiment of an apparatus for generating an information push approach according to the present disclosure;
FIG. 6 is a schematic structural diagram of an electronic device suitable for use in implementing embodiments of the present disclosure.
Detailed Description
The present disclosure is described in further detail below with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the relevant invention and not restrictive of the invention. It should be noted that, for convenience of description, only the portions related to the present invention are shown in the drawings.
It should be noted that, in the present disclosure, the embodiments and features of the embodiments may be combined with each other without conflict. The present disclosure will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
Fig. 1 shows an exemplary architecture 100 to which the method for generating an information push manner or the apparatus for generating an information push manner of the present disclosure may be applied.
As shown in fig. 1, the system architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 serves as a medium for providing communication links between the terminal devices 101, 102, 103 and the server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.
The terminal devices 101, 102, 103 interact with a server 105 via a network 104 to receive or send messages or the like. The terminal devices 101, 102, 103 may have various communication client applications installed thereon, such as a web browser application, an information application, a shopping application, a search application, an instant messaging tool, a mailbox client, and the like.
The terminal apparatuses 101, 102, and 103 may be hardware or software. When the terminal devices 101, 102, 103 are hardware, they may be various electronic devices having a display screen and supporting human-computer interaction, including but not limited to smart phones, tablet computers, laptop portable computers, desktop computers, and the like. When the terminal apparatuses 101, 102, 103 are software, they can be installed in the electronic apparatuses listed above. It may be implemented as a plurality of software or software modules (e.g., software or software modules used to provide distributed services) or as a single software or software module. And is not particularly limited herein.
The server 105 may be a server providing various services, such as a background server providing support for information applications on the terminal devices 101, 102, 103. The background server may generate an information pushing manner according to the feature data corresponding to the terminal devices 101, 102, and 103 (for example, send a short message notification to the terminal device 101 at 12: 00), and may also push information according to the generated information pushing manner.
The server may be hardware or software. When the server is hardware, it may be implemented as a distributed server cluster formed by multiple servers, or may be implemented as a single server. When the server is software, it may be implemented as multiple pieces of software or software modules (e.g., software or software modules used to provide distributed services), or as a single piece of software or software module. And is not particularly limited herein.
It should be noted that the method for generating the information push manner provided by the embodiment of the present disclosure is generally executed by the server 105, and accordingly, the apparatus for generating the information push manner is generally disposed in the server 105.
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 an information push style in accordance with the present disclosure is shown. The method for generating the information push mode comprises the following steps:
in response to detecting the target data abnormality, at least one factor is selected as a key factor from the factors associated with the abnormal target data, step 201.
In the present embodiment, an execution subject (such as the server 105 shown in fig. 1) of the method for generating the information push manner may detect whether the target data is abnormal in various ways. The target data can be used for representing the activity degree of the user group. As an example, the target data may include, but is not limited to, at least one of: DAU (day active user), MAU (month active user), PCU (peak current user, highest number of simultaneous online Users), DNU (day New user), ACU (Average current user, Average number of simultaneous online Users), UV (unique viewer, number of independent visitors), PV (Page View, volume of View). In response to detecting the target data abnormality, the execution subject may select at least one factor from among factors associated with the target data of the abnormality as a key factor in various ways. Wherein the factor may correspond to a category to which the user in the user group belongs. Specifically, the execution subject may obtain a preset relationship table in advance, and the preset relationship table may be used to represent a corresponding relationship between the target data and the associated factor. As an example, the target data may be, for example, the number of independent visitors of the current period. The factors associated with the target data may include the number of daily active users, the number of monthly active users, and the number of daily new users. The categories to which the users corresponding to the above factors belong may include daily active users, monthly active users, and new users. As yet another example, the target data may be, for example, the number of daily active users. Factors associated with the target data may include a number of men's users, a number of women's users, a number of young users, a number of middle-aged users, and the like. The category to which the user corresponding to the above-described factors belongs may include men (e.g., a user who selects gender "male" when registering the user), women, young (e.g., a user whose age group is "18-25" when registering the user), and middle (e.g., a user whose age group is "35-45" when registering the user). Therefore, the execution subject can analyze the abnormal target data by using the preset relation table to select the key factors.
In some optional implementations of this embodiment, the executing body may further select, in response to detecting the target data abnormality, at least one factor from factors associated with the abnormal target data as a key factor, according to the following steps:
firstly, target data in a preset time period are obtained.
In these implementations, the execution subject may obtain target data within a preset time period from a local or communicatively connected electronic device (e.g., an information push data monitoring platform). As an example, the preset time period may be, for example, approximately one hour, approximately 15 minutes, or the like.
And a second step of determining the influence degree corresponding to each factor associated with the target data in response to determining that the fluctuation of the target data exceeds a preset fluctuation threshold.
In these implementations, in response to determining that the fluctuation of the target data acquired in the first step exceeds a preset fluctuation threshold, the execution subject may determine the influence degree corresponding to each factor associated with the target data. As an example, the execution main body may first acquire various factors associated with the target data. Then, the execution subject may determine the influence degree of each factor associated with the target data on the target data by using a kini Index (Gini Index), an information gain, or the like.
And thirdly, selecting a target number of factors with influence degrees meeting preset conditions as key factors.
In these implementations, the execution subject may select, as a key factor, a target number of factors that meet a preset condition from the influence determined in the second step. As an example, the execution subject may select 3 factors with the largest influence as the key factors. As still another example, the execution subject described above may have, as a key factor, a factor whose influence degree exceeds a preset influence degree threshold.
Based on the optional implementation manner, the scheme can automatically determine the factors associated with the abnormal target data, so that a powerful reference basis is provided for determining the target user group for information push.
In some optional implementations of the present embodiment, the information pushing manner may be used to indicate that information is pushed at a specific pushing time.
In some optional implementations of the present embodiment, the information pushing manner may be used to indicate that information is pushed in a specific pushing channel.
In this embodiment, the execution main body may acquire information associated with users in a user group corresponding to the key factor as the feature data in various ways. As an example, the user-associated information may include personal attribute information such as a user tag, a birthday, hobbies, and the like. As yet another example, the user-associated information described above may also include user frequent login time, search content, comment content, and the like.
In some optional implementations of the embodiment, the information associated with the user may include user behavior data and historical information push data within a preset time from the current time. The user behavior data may include, for example, the frequent login time of the user in the last week, the topic with the most browsing times of the user in the last week, the number of comments and the number of likes of the user in the last week, and the like. The history information push data may include history information push effect data and history information push response data. The historical information push effect data may include, for example, data that successfully reaches the user by way of a telephone call, a short message, sending a notification message in an application, and the like. The historical information push response data may include, for example, the user clicking a link included in the short message, clicking data for viewing a notification message sent in the application, and the like.
In this embodiment, the execution body may select the ue information from the corresponding ue information set in various ways to form the candidate ue information set. For example, the execution entity may randomly select the ue information from the corresponding ue information sets to form the candidate ue information sets. As another example, the execution subject may select, according to the feature data obtained in the second step, the matched ue information from the ue information sets corresponding to the key factors selected in the first step to form the candidate ue information set.
And 204, generating an information pushing mode corresponding to the user side information in the candidate user side information set according to the characteristic data.
In this embodiment, according to the feature data obtained in step 202, the executing entity may generate an information pushing manner corresponding to the ue information in the candidate ue information set formed in step 203 in various manners. The information pushing method may include, but is not limited to, at least one of the following: information push channel, information push time and information push content. The information push channel may include, but is not limited to, at least one of the following: telephone notification, short message notification, and in-application message notification.
In some optional implementation manners of this embodiment, based on the information pushing manner used for instructing to push information in a specific pushing channel, according to the feature data, the executing entity may further generate an information pushing manner corresponding to the user-side information in the candidate user-side information set according to the following steps:
the method comprises the steps of firstly, generating an optional channel information set according to a preset channel information set and current cost constraint.
In these implementations, the executing agent may first obtain a preset set of channel information and current cost constraints. As an example, the preset channel information set may include a telephone notification channel, a short message notification channel, and an intra-application message notification channel. Generally speaking, the costs corresponding to the above three channels are reduced in sequence. The executing body can determine whether the current information push cost meets the cost constraint, so that channels (such as telephone notification channels) which do not meet the cost constraint are eliminated, and an optional channel information set is generated.
And secondly, determining the response probability corresponding to the user side information in the candidate user side information set and the channel information in the selectable channel information set according to the characteristic data.
In these implementations, according to the feature data, the execution subject may determine response probabilities corresponding to the user-side information in the candidate user-side information set and the channel information in the selectable channel information set in various manners. As an example, the candidate ue side information set may include a ue side information, B ue side information, and C ue side information. The execution main body can input the characteristic data corresponding to the information of the A user side into a pre-trained channel prediction model, so that the response probability of the short message notification channel and the response probability of the in-application message notification channel corresponding to the information of the A user side are obtained. The channel prediction model may be a model obtained by training using a machine learning method such as XGBOOST. In the same way, the response probability of the short message notification channel and the response probability of the in-application message notification channel corresponding to the information of the B user side and the information of the C user side can be obtained.
And thirdly, determining the channel corresponding to the channel information with the highest response probability as the specific pushing channel corresponding to the user side information for the user side information in the candidate user side information set.
In these implementations, as an example, if the response probability of the short message notification channel corresponding to the a-subscriber-side information is greater than the response probability of the in-application message notification channel, the execution main body may determine the short message notification channel as the specific push channel corresponding to the a-subscriber-side information.
It should be noted that, the specific push channels corresponding to the ue information in the candidate ue information set may be the same or different, and are not limited herein.
Based on the optional implementation mode, the information pushing channel can be selected based on the matching degree of the cost constraint and the user characteristic data, and the alternative channel is subjected to double filtering through the cost constraint and the matching degree with the user side information, so that the information pushing effect is improved on the premise of meeting the cost constraint.
Optionally, the channel information set may include a telemarketing channel. The executing main body can further continue to execute the following steps:
and fourthly, responding to the fact that the information pushing mode is used for indicating that information is pushed in a telephone sales channel, and selecting matched marketing person information from a preset marketing person information list.
In these implementations, the execution subject may select matching marketer information from a preset list of marketer information in response to determining that the information push manner is indicative of pushing information through a telemarketing channel. The marketing staff information list comprises the identification of the marketing staff and the corresponding characteristic label. The identification of the marketer may be, for example, a job number or a name. The characteristic tags may be, for example, areas of excellence or rating stars.
And fifthly, sending an information pushing task corresponding to the information pushing mode to a terminal corresponding to the matched marketing staff information.
In these implementations, the execution subject may send an information push task corresponding to the information push method to the terminal corresponding to the matched marketer information selected in the fourth step. As an example, the execution subject may transmit "sell to a user" as an information push task to a terminal used by a marketer having a job number "003".
Due to the difference of the business level and the professional skill of the marketers, the selection of different marketers has a great influence on the marketing effect of the electric marketing channel. Based on the above optional implementation manner, the scheme can further match suitable marketing personnel for the electricity marketing channel, so that the information pushing effect is further improved.
In some optional implementation manners of this embodiment, based on that the information associated with the user includes user behavior data and historical information push data within a preset time from the current time, according to the feature data, the execution main body may generate an information push manner corresponding to the user side information in the candidate user side information set according to the following steps:
firstly, according to the characteristic data, pushing channel information is generated by using a channel prediction model trained in advance.
In these implementations, the channel prediction model may be consistent with the above description and will not be described herein.
And secondly, generating pushing time information by using a pre-trained time prediction model according to the characteristic data.
In these implementations, the temporal prediction model may be a model trained by a machine learning method such as XGBOOST, for example. As still another example, the time prediction model may be a time period in which the number of responses determined by counting the historical response time periods is the highest.
And thirdly, generating push content information by using a pre-trained content prediction model according to the characteristic data.
In these implementations, the characteristic data may include a category of behavior (e.g., comments, praise, forward, etc.), a duration of the behavior, a frequency of the user's behavior in the near term (e.g., within a week), and the like. The content prediction model can be a model which is obtained by training through a machine learning method such as XGBOOST and is used for predicting the content preference of the user.
And fourthly, generating an information pushing mode according to the pushing channel information, the pushing time information and the pushing content information.
In these implementations, the information push method is used to instruct the push channel indicated by the push channel information generated in the first step to push the content indicated by the push content information generated in the second step at the time indicated by the push time information generated in the second step.
Based on the optional implementation mode, the information pushing mode of pushing the specific content in a specific channel and at a specific time can be generated according to the characteristic data of the user by using a machine learning method, so that the information pushing scheme is enriched, and the information pushing effect can be improved from multiple angles.
In some optional implementations of this embodiment, based on the information pushing manner being used to instruct to push information at a specific pushing time, the executing body may further continue to perform the following steps:
the first step is that in response to the fact that the specific pushing time is determined to be reached, user behavior information corresponding to the user side information corresponding to the information pushing mode is obtained.
In these implementations, the user behavior information described above is typically used to characterize more recent real-time behaviors, such as near 15 minutes, near 1 minute.
And secondly, determining whether to correct the specific pushing time according to the matching of the behavior information and a preset rule.
In these implementations, the execution body may determine whether to modify a particular push time based on a match of the behavior information with a preset rule. The preset rule may include, for example, a preset adjustment behavior. And if the behavior indicated by the behavior information acquired in the first step is matched with the preset adjustment behavior, determining to correct the specific pushing time. For example, it is determined from the feature data that the user X always logs in the browsing information at 19:00, and an information push method of 19:02 push notification is generated. At 19:02, the executive detects that user X is not logged in, at which point the push time may be modified, for example, postponed for 5 minutes.
In the prior art, an asynchronous information pushing system often pushes information according to preset pushing time, so that the real-time practice is difficult to react. Based on the optional implementation manner, the scheme can further verify the information pushing manner according to the real-time behavior of the user when the preset information pushing time is up, and determine whether the pushing manner needs to be adjusted, so that the change of an information pushing scene can be captured as much as possible, and the information pushing effect is improved by adopting a more appropriate manner.
With continued reference to fig. 3, fig. 3 is a schematic diagram of an application scenario of a method for generating an information pushing manner according to an embodiment of the present disclosure. In the application scenario of fig. 3, a user a, a user B, a user C, and a user D may use terminals 3011, 3012, 3013, and 3014 to interact with the backend server 302. The server 303 may be a server for analyzing the effect of pushing information from the backend server 302. The server 303 may be used to monitor various data, such as the number of active daily users. In response to detecting a 30% decrease in the number of active daily users (as shown in graph 304), server 303 may select the number of young users as a key factor from the factors associated with the number of active daily users (as shown in graph 305). Then, the server 303 may acquire, as the feature data, information associated with the user A, B, D in the user group (for example, the young user group) corresponding to the above-described key factor from the server 302 (as shown in fig. 306). Then, the server 303 may select the user side information from the user side information sets (e.g. information sets including the terminal used by the user A, B, D) corresponding to the key factors to form a candidate user side information set (as shown in fig. 307). Finally, based on the feature data, the server 303 can generate an information push method for pushing information to the terminal 3011 and an information push method for pushing information to the terminal 3014 (as shown in fig. 308).
At present, in one of the prior art, pushing strategies such as a target group, a pushing channel, pushing time, pushing content and the like are generally determined manually, which results in long time consumption, low efficiency, and difficulty in performing corresponding adjustment on an information pushing effect. In the method provided by the embodiment of the disclosure, the target data abnormality is used as a generation condition of the information pushing mode, and a suitable target group generation information pushing mode is selected according to the factor causing the target data abnormality, so that the information pushing mode is enriched, the pertinence of information pushing is improved, and the efficiency is improved.
With further reference to fig. 4, a flow 400 of yet another embodiment of a method for generating an information push approach is shown. The process 400 of the method for generating an information push manner includes the following steps:
in response to detecting the target data anomaly, at least one factor is selected as a key factor from the factors associated with the anomalous target data, step 401.
And step 403, selecting a matched information pushing target from a preset corresponding relation table according to the key factors.
In this embodiment, an execution subject (for example, the server 105 shown in fig. 1) of the method for generating the information push manner may select a matching information push target from a preset correspondence table in various manners. The correspondence table may be used to represent a correspondence between the factors and the information push targets. The information push target is generally used to indicate that the target data is developed to a desired direction. As an example, the target data may be the number of daily active users, and when the target data decreases, the information push target may be the reason for slowing down or solving the decrease of the target data; when the target data is raised, the information delivery destination may be a cause for maintaining or assisting the target data to be raised.
In this embodiment, the response probability generation model may be a model obtained by training through a machine learning method such as XGBOOST.
In this embodiment, optionally, a preset number (for example, the top 100, the top 50) of the ue information may be selected from the ue information with the response probability greater than the preset response threshold to form the candidate ue information set, so as to control the capacity of the candidate ue information set.
In some optional implementations of this embodiment, the executing body may further continue to perform the following steps:
and step one, establishing a folder corresponding to the matched information push target.
In these implementations, the execution subject may further establish a folder corresponding to the matched information push target. By way of example, each matching information push target may correspond to a folder.
And secondly, generating an information pushing task according to the generated information pushing mode.
In these implementations, the execution subject may generate the information push task in various ways according to the information push way generated in step 406. The information pushing task can be used for indicating information pushing in the information pushing mode.
And thirdly, storing the generated information pushing task to a corresponding folder.
In these implementations, the execution subject may store the information pushing task generated in the second step in a corresponding folder. As an example, the execution subject described above may store an information push task corresponding to the same information push target to a folder corresponding to the information push target.
And fourthly, in response to the fact that the folder matched with the key factors exists, executing the information pushing task in the matched folder.
In these implementations, after step 401 described above, in response to determining that there are folders that match the key factor, the execution principal described above may execute the information push task in the matching folders.
Based on the optional implementation mode, the scheme can automatically generate the information push task and execute the corresponding task according to different information push targets so as to support the target data. Compared with the existing manual push task creation, the method saves the labor cost and improves the efficiency.
Optionally, in response to determining that there is a folder matching the key factor, the executing body may further execute an information pushing task in the matched folder according to the following steps:
and S1, dividing the user terminal information in the information pushing task in the matched folder into a pushing group and a comparison group.
In these implementations, as an example, the information pushing task 1 in the folder of the information pushing target 1 may be used to instruct pushing information to 1000 user terminals. The execution body may divide the 1000 ues into a push group (e.g., 800) and a contrast group (e.g., 200).
And S2, pushing the information to the user terminal corresponding to the pushing group in the generated information pushing mode.
In these implementations, the execution body may push the information to the user side corresponding to the push group divided in step S1 in the generated information push manner.
And S3, generating information pushing evaluation information of the pushing group and the comparison group.
In these implementations, the execution main body may generate the information push evaluation information of the push group and the contrast group after the information push amount is accumulated to a certain level. Wherein, the pushed evaluation information may include, but is not limited to, at least one of the following: the push success rate, the push response rate and the push efficiency.
And S4, responding to the situation that the information pushing evaluation information indicates that the pushing effect does not meet the requirement, executing other information pushing tasks under the information pushing target.
In these implementations, in response to determining that the information push evaluation information indicates that the push effect is not satisfactory, the execution main body may execute other information push tasks under the information push target. For example, the execution subject may execute the information push task 2 or the information push task 3 in the folder belonging to the information push target 1.
Based on the optional implementation manner, the scheme can select the most matched information pushing manner aiming at the effect feedback of information pushing so as to improve the information pushing effect.
As can be seen from fig. 4, the process 400 of the method for generating an information push manner in this embodiment embodies the steps of selecting a matched information push target according to the key factors, and selecting the ue information with a response probability greater than a preset response threshold from the ue information sets corresponding to the key factors to form a candidate ue information set. Therefore, the scheme described in the embodiment can select the target user according to the response of the user to the information push target, so that the information push method is enriched, and the pertinence of information push is improved.
With further reference to fig. 5, as an implementation of the method shown in the above-mentioned figures, the present disclosure provides an embodiment of an apparatus for generating an information pushing manner, where the embodiment of the apparatus corresponds to the embodiment of the method shown in fig. 2 or fig. 4, and the apparatus may be specifically applied to various electronic devices.
As shown in fig. 5, the apparatus 500 for generating an information pushing manner provided in this embodiment includes a first selecting unit 501, a first obtaining unit 502, a second selecting unit 503, and a first generating unit 504. The first selecting unit 501 is configured to, in response to detecting that the target data is abnormal, select at least one factor from factors associated with the abnormal target data as a key factor, where the target data is used to represent an activity level of a user group, and the factor corresponds to a category to which a user in the user group belongs; a first acquisition unit 502 configured to acquire information associated with users in a user group corresponding to the key factor as feature data; a second selecting unit 503 configured to select the user side information from the user side information sets corresponding to the key factors to form candidate user side information sets; the first generating unit 504 is configured to generate an information pushing manner corresponding to the client information in the candidate client information set according to the feature data.
In this embodiment, the apparatus 500 for generating an information push method includes: the detailed processing of the first selecting unit 501, the first obtaining unit 502, the second selecting unit 503 and the first generating unit 504 and the technical effects thereof can refer to the related descriptions of step 201, step 202, step 203 and step 204 in the corresponding embodiment of fig. 2, which are not repeated herein.
In some optional implementations of the present embodiment, the information pushing manner may be used to indicate that information is pushed at a specific pushing time. The apparatus 500 for generating an information push manner may further include: a second obtaining unit (not shown in the figure) configured to obtain user behavior information corresponding to the user side information corresponding to the information push manner in response to determining that the specific push time is reached; and a determining unit (not shown in the figure) configured to determine whether to modify the specific push time according to the matching of the behavior information and the preset rule.
In some optional implementations of the present embodiment, the information pushing manner may be used to indicate that information is pushed in a specific pushing channel. The first generating unit 504 may be further configured to: generating an optional channel information set according to a preset channel information set and a current cost constraint; determining response probability corresponding to the user side information in the candidate user side information set and the channel information in the selectable channel information set according to the characteristic data; and for the user side information in the candidate user side information set, determining the channel corresponding to the channel information with the highest response probability as the specific pushing channel corresponding to the user side information.
In some optional implementations of the embodiment, the channel information set may include a telemarketing channel. The apparatus 500 for generating an information push manner may further include: a third selecting unit (not shown in the figures) configured to select matching marketer information from a preset marketer information list in response to the fact that the information pushing mode is determined to be used for indicating that information is pushed in a telemarketing channel, wherein the marketer information list comprises identification of marketers and corresponding feature labels; and a sending unit (not shown in the figure) configured to send an information push task corresponding to the information push mode to a terminal corresponding to the matched marketer information.
In some optional implementations of this embodiment, the first selecting unit 501 may be further configured to: acquiring target data in a preset time period; in response to determining that the fluctuation of the target data exceeds a preset fluctuation threshold, determining an influence degree corresponding to each factor associated with the target data; and selecting a target number of factors with influence degrees meeting preset conditions as key factors.
In some optional implementations of the embodiment, the information associated with the user may include user behavior data and historical information push data within a preset time from the current time. The first generating unit 504 may be further configured to: generating push channel information by using a pre-trained channel prediction model according to the characteristic data; generating pushing time information by utilizing a pre-trained time prediction model according to the characteristic data; generating push content information by using a pre-trained content prediction model according to the characteristic data; and generating an information pushing mode according to the pushing channel information, the pushing time information and the pushing content information.
In some optional implementations of this embodiment, the second selecting unit 503 may be further configured to: selecting a matched information push target from a preset corresponding relation table according to key factors; inputting the characteristic data into a pre-trained response probability generation model, and generating the response probability of a user corresponding to the characteristic data to the matched information push target; and selecting the user side information with the response probability larger than a preset response threshold value from the user side information sets corresponding to the key factors to form a candidate user side information set.
In some optional implementation manners of this embodiment, the apparatus 500 for generating an information pushing manner may further include: an establishing unit (not shown in the figure) configured to establish a folder corresponding to the matched information push target; a second generating unit (not shown in the figure) configured to generate an information push task according to the generated information push manner; a storage unit (not shown in the figure) configured to store the generated information push task to a corresponding folder; and the execution unit (not shown in the figure) is configured to execute the information pushing task in the matched folder in response to determining that the folder matched with the key factor exists.
In some optional implementations of this embodiment, the execution unit may be further configured to: dividing the user side information in the information pushing task in the matched folder into a pushing group and a comparison group; pushing information to the user side corresponding to the pushing group in the generated information pushing mode; generating information push evaluation information of a push group and a contrast group; and executing other information pushing tasks under the information pushing target in response to the fact that the information pushing evaluation information indicates that the pushing effect does not meet the requirement.
According to the device provided by the above embodiment of the present disclosure, the target data is abnormal as a generation condition of the information pushing manner, and the first selecting unit 501 and the second selecting unit 503 select a suitable target group to generate the information pushing manner according to a factor causing the target data to be abnormal, so that the information pushing manner is enriched, the pertinence of the information pushing is improved, and the efficiency is improved.
Referring now to FIG. 6, a block diagram of an electronic device (e.g., the server of FIG. 1) 600 suitable for implementing embodiments of the present application is shown. The terminal device in the embodiments of the present application may include, but is not limited to, a mobile terminal such as a mobile phone, a notebook computer, a digital broadcast receiver, a PDA (personal digital assistant), a PAD (tablet computer), a PMP (portable multimedia player), a vehicle terminal (e.g., a car navigation terminal), and the like, and a fixed terminal such as a digital TV, a desktop computer, and the like. The server shown in fig. 6 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present application.
As shown in fig. 6, electronic device 600 may include a processing means (e.g., central processing unit, graphics processor, etc.) 601 that may perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM)602 or a program loaded from a storage means 608 into a Random Access Memory (RAM) 603. In the RAM603, various programs and data necessary for the operation of the electronic apparatus 600 are also stored. The processing device 601, the ROM 602, and the RAM603 are connected to each other via a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.
Generally, the following devices may be connected to the I/O interface 605: input devices 606 including, for example, a touch screen, touch pad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, or the like; an output device 607 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; storage 608 including, for example, magnetic tape, hard disk, etc.; and a communication device 609. The communication means 609 may allow the electronic device 600 to communicate with other devices wirelessly or by wire to exchange data. While fig. 6 illustrates an electronic device 600 having various means, it is to be understood that not all illustrated means are required to be implemented or provided. More or fewer devices may alternatively be implemented or provided. Each block shown in fig. 6 may represent one device or may represent multiple devices as desired.
In particular, according to embodiments of the application, the processes described above with reference to the flow diagrams may be implemented as computer software programs. For example, embodiments of the present application include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated by the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication means 609, or installed from the storage means 608, or installed from the ROM 602. The computer program, when executed by the processing device 601, performs the above-described functions defined in the methods of the embodiments of the present application.
It should be noted that the computer readable medium described in the embodiments of the present disclosure may be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In embodiments of the disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In embodiments of the present disclosure, however, a computer readable signal medium may comprise a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (Radio Frequency), etc., or any suitable combination of the foregoing.
The computer readable medium may be embodied in the electronic device; or may exist separately and not be assembled into the server. The computer readable medium carries one or more programs which, when executed by the server, cause the server to: in response to the detection of the target data abnormality, selecting at least one factor from factors associated with the abnormal target data as a key factor, wherein the target data is used for representing the activity degree of a user group, and the factor corresponds to a category to which a user in the user group belongs; acquiring information associated with users in a user group corresponding to the key factors as characteristic data; selecting user information from the user information sets corresponding to the key factors to form candidate user information sets; and generating an information pushing mode corresponding to the user side information in the candidate user side information set according to the characteristic data.
Computer program code for carrying out operations for embodiments of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as "C", Python, 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 latter scenario, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in the embodiments of the present disclosure may be implemented by software or hardware. The described units may also be provided in a processor, and may be described as: a processor comprises a first selection unit, a first acquisition unit, a second selection unit and a first generation unit. For example, the first selecting unit may be further described as "a unit that selects at least one factor as a key factor from factors associated with abnormal target data in response to detection of an abnormality of the target data, wherein the target data is used for representing the activity degree of the user group, and the factors correspond to categories to which the users in the user group belong".
The foregoing description is only exemplary of the preferred embodiments of the disclosure and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the invention in the embodiments of the present disclosure is not limited to the specific combinations of the above-mentioned features, and other embodiments in which the above-mentioned features or their equivalents are combined arbitrarily without departing from the spirit of the invention are also encompassed. For example, the above features and (but not limited to) technical features with similar functions disclosed in the embodiments of the present disclosure are mutually replaced to form the technical solution.
Claims (12)
1. A method for generating an information push approach, comprising:
in response to the detection of target data abnormity, selecting at least one factor from factors associated with the abnormal target data as a key factor, wherein the target data is used for representing the activity degree of a user group, and the factor corresponds to a category to which users in the user group belong;
acquiring information associated with users in a user group corresponding to the key factors as feature data;
selecting user information from the user information sets corresponding to the key factors to form candidate user information sets;
and generating an information pushing mode corresponding to the user side information in the candidate user side information set according to the characteristic data.
2. The method of claim 1, wherein the information pushing manner is used to indicate that information is pushed at a specific pushing time; and
the method further comprises the following steps:
in response to determining that the specific pushing time is reached, acquiring user behavior information corresponding to the user side information corresponding to the information pushing mode;
and determining whether to correct the specific pushing time or not according to the matching of the behavior information and a preset rule.
3. The method of claim 1, wherein the information push manner is used to indicate that information is pushed in a specific push channel; and
the generating an information pushing mode corresponding to the client information in the candidate client information set according to the feature data includes:
generating an optional channel information set according to a preset channel information set and a current cost constraint;
determining response probability corresponding to the user side information in the candidate user side information set and the channel information in the optional channel information set according to the characteristic data;
and for the user side information in the candidate user side information set, determining the channel corresponding to the channel information with the highest response probability as the specific pushing channel corresponding to the user side information.
4. The method of claim 3, wherein the set of channel information includes a telemarketing channel; and
the method further comprises the following steps:
responding to the fact that the information pushing mode is used for indicating that information is pushed through a telephone selling channel, and selecting matched marketing person information from a preset marketing person information list, wherein the marketing person information list comprises identification of marketing persons and corresponding feature labels;
and sending an information pushing task corresponding to the information pushing mode to a terminal corresponding to the matched marketing staff information.
5. The method of claim 1, wherein said selecting at least one factor from among factors associated with anomalous target data as a key factor in response to detecting a target data anomaly comprises:
acquiring target data in a preset time period;
in response to determining that the fluctuation of the target data exceeds a preset fluctuation threshold, determining an influence degree corresponding to each factor associated with the target data;
and selecting a target number of factors with influence degrees meeting preset conditions as the key factors.
6. The method of claim 1, wherein the user-associated information comprises user behavior data and historical information push data within a current preset time period; and
the generating an information pushing mode corresponding to the user side information in the candidate user side information set according to the feature data includes:
generating push channel information by using a pre-trained channel prediction model according to the characteristic data;
generating pushing time information by utilizing a pre-trained time prediction model according to the characteristic data;
generating push content information by utilizing a pre-trained content prediction model according to the characteristic data;
and generating an information pushing mode according to the pushing channel information, the pushing time information and the pushing content information.
7. The method according to one of claims 1 to 6, wherein the selecting the user equipment information from the user equipment information sets corresponding to the key factors to form a candidate user equipment information set comprises:
selecting a matched information pushing target from a preset corresponding relation table according to the key factors;
inputting the characteristic data into a pre-trained response probability generation model, and generating a response probability of a user corresponding to the characteristic data to the matched information push target;
and selecting the user side information with the response probability larger than a preset response threshold value from the user side information sets corresponding to the key factors to form the candidate user side information sets.
8. The method of claim 7, wherein the method further comprises:
establishing a folder corresponding to the matched information pushing target;
generating an information pushing task according to the generated information pushing mode;
storing the generated information pushing task to a corresponding folder;
in response to determining that there are folders that match the key factor, performing an information push task in the matching folders.
9. The method of claim 8, wherein the performing of the information push task in the matched folder comprises:
dividing the user side information in the information pushing task in the matched folder into a pushing group and a contrast group;
pushing information to the user side corresponding to the pushing group in the generated information pushing mode;
generating information pushing evaluation information of the pushing group and the comparison group;
and executing other information pushing tasks under the information pushing target in response to the fact that the information pushing evaluation information indicates that the pushing effect does not meet the requirement.
10. An apparatus for generating an information push approach, comprising:
the first selecting unit is configured to respond to the detection of target data abnormity, and select at least one factor from factors associated with the abnormal target data as a key factor, wherein the target data is used for representing the activity degree of a user group, and the factor corresponds to a category to which users in the user group belong;
a first acquisition unit configured to acquire information associated with a user in a user group corresponding to the key factor as feature data;
a second selecting unit configured to select user side information from the user side information sets corresponding to the key factors to form candidate user side information sets;
the first generating unit is configured to generate an information pushing mode corresponding to the client information in the candidate client information set according to the feature data.
11. A server, comprising:
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 recited in any of claims 1-9.
12. A computer-readable medium, on which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1-9.
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