WO2019019387A1

WO2019019387A1 - Information push suggestion generation method and apparatus, computer device and storage medium

Info

Publication number: WO2019019387A1
Application number: PCT/CN2017/104771
Authority: WO
Inventors: 王强
Original assignee: 上海壹账通金融科技有限公司
Priority date: 2017-07-24
Filing date: 2017-09-30
Publication date: 2019-01-31
Also published as: CN107682388B; CN107682388A

Abstract

Disclosed is an information push suggestion generation method, the method comprising: acquiring a push suggestion analysis request; extracting a suggestion analysis condition from the push suggestion analysis request; querying, in recorded historical push tasks, historical push tasks conforming to the extracted suggestion analysis condition; acquiring push effect scores respectively corresponding to the queried historical push tasks; according to the acquired push effect scores, selecting a pre-set number of historical push tasks from the historical push tasks ; and analysing the selected historical push tasks, and generating a push suggestion corresponding to the push suggestion analysis request.

Description

Information push suggestion generation method, device, computer device and storage medium

This application claims the priority of the Chinese patent application filed on July 24, 2017, the Chinese Patent Office, the application number is 2017106077068, and the invention name is "information push recommendation generation method, device, computer equipment and storage medium". The citations are incorporated herein by reference.

Technical field

The present application relates to the field of information processing technologies, and in particular, to a method, an apparatus, a computer device, and a storage medium for generating information push suggestions.

Background technique

With the rapid development of Internet technology, various information can be pushed to users through the Internet, so that users can obtain relevant information in time. As the demand for message push increases, the amount of information to be pushed through the Internet increases. The push information is also increased.

In the traditional push process, the information is pushed according to the preset push parameters. As the amount of information push increases, the user receives too much push information, and the user has annoyed the push information, resulting in a more information push effect. low.

Summary of the invention

According to various embodiments of the present application, an information push suggestion generation method, apparatus, computer device, and storage medium are provided.

A method for generating information push suggestions, including:

Obtain a push suggestion analysis request;

Extracting the suggested analysis conditions in the push suggestion analysis request;

In the historical push task of the record, the historical push task that meets the extracted analysis condition of the query is queried;

Obtaining a push effect score corresponding to each historical push task that is queried;

Selecting a preset number from the historical push tasks according to the obtained push effect scores Historical push task; and

The selected historical push task is analyzed, and a push suggestion corresponding to the push suggestion analysis request is generated.

An information push suggestion generating device, the device comprising:

Requesting an acquisition module for obtaining a push suggestion analysis request;

a condition extraction module, configured to extract a recommended analysis condition in the push suggestion analysis request;

The task query module is configured to query, in the historical push task of the record, a historical push task that meets the extracted recommended analysis condition;

a score obtaining module, configured to obtain a push effect score corresponding to each historical push task that is queried;

a task selection module, configured to select a preset number of historical push tasks from the historical push tasks according to the obtained push effect scores; and

The suggestion generating module is configured to analyze the selected historical push task, and generate a push suggestion corresponding to the push suggestion analysis request.

A computer device comprising a memory and a processor, wherein the memory stores computer executable instructions that, when executed by the processor, cause the processor to perform the following steps:

Obtain a push suggestion analysis request;

Selecting, according to the obtained push effect score, a preset number of historical push tasks from the historical push tasks; and

Performing an analysis on the selected historical push task, and generating a corresponding to the push suggestion analysis request Push suggestions.

One or more storage media storing computer executable instructions that, when executed by one or more processors, cause the one or more processors to perform the following steps:

Obtain a push suggestion analysis request;

Details of one or more embodiments of the present application are set forth in the accompanying drawings and description below. Other features, objects, and advantages of the invention will be apparent from the description and appended claims.

DRAWINGS

In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings to be used in the embodiments will be briefly described below. Obviously, the drawings in the following description are only some embodiments of the present application, Those skilled in the art can also obtain other drawings based on these drawings without any creative work.

1 is an application environment diagram of a method for generating information push suggestions in an embodiment;

2 is a structural block diagram of a server in an information push suggestion generation system in an embodiment;

3 is a schematic flowchart of a method for generating information push suggestions in an embodiment;

4 is a schematic flow chart of steps of generating a push suggestion according to a historical push task in an embodiment;

5 is a schematic flow chart of steps for generating a push time period suggestion in an embodiment;

6 is a flow chart showing the steps of calculating a push effect score in one embodiment;

7 is a structural block diagram of an information push suggestion generating apparatus in an embodiment;

8 is a structural block diagram of a suggestion generation module in an embodiment;

Figure 9 is a block diagram showing the structure of an information push suggestion generating apparatus in another embodiment.

Detailed ways

In order to make the objects, technical solutions, and advantages of the present application more comprehensible, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It is understood that the specific embodiments described herein are merely illustrative of the application and are not intended to be limiting.

FIG. 1 is an application environment diagram of a method for generating information push suggestions in an embodiment. Referring to FIG. 1, the information push suggestion generation method is applied to an information push suggestion generation system. The information push suggestion generation system includes a terminal 110 and a server 120, wherein the terminal 110 is connected to the server 120 through a network. The terminal 110 may be a fixed terminal or a mobile terminal, and the fixed terminal may specifically be at least one of a printer, a scanner, and a monitor, and the mobile terminal may specifically be at least one of a tablet computer, a smart phone, a personal data assistant, and a digital camera. .

FIG. 2 is a schematic diagram showing the internal structure of the server 120 in the information push suggestion generation system of FIG. 1 in an embodiment. As shown in FIG. 2, the server 120 includes a processor, a non-volatile storage medium, an internal memory, and a network interface connected by a system bus. The processor of server 120 is used to provide computing and control capabilities to support the operation of the entire server 120, the memory for storing data, code instructions, etc., and the network interface for network communication with terminal 110. At least one computer executable instruction is stored on the memory, and the computer executable instruction can be executed by the processor to implement the information push suggestion generation method applicable to the server 120 provided in the embodiment of the present application. The memory may include a non-volatile storage medium such as a magnetic disk, an optical disk, or a read-only memory (ROM). For example, in one embodiment, a memory includes a non-volatile storage medium and an internal memory; the non-volatile storage medium stores an operating system, computer-executable instructions, and a database executable by the processor to implement The above-mentioned information push recommendation generation method stores the push task data in the database; the internal memory provides a cache running environment for the operating system and computer executable instructions in the non-volatile storage medium. The information push suggestion generation method can also be applied to the terminal 110.

It will be understood by those skilled in the art that the structure shown in FIG. 2 is only a schematic diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the server to which the solution of the present application is applied. The specific server may include More or fewer components are shown in Figure 2, or some components are combined, or have different component arrangements.

As shown in FIG. 3, in an embodiment, a method for generating an information push suggestion is provided. This embodiment is exemplified by the method applied to the server 120 in FIG. 1, and the method specifically includes the following content:

S302. Acquire a push suggestion analysis request.

In one embodiment, the terminal 110 acquires an operator account input by the operator of the push, generates a proposal analysis page request according to the obtained operator account, and sends a request analysis page request to the server 120.

The server 120 receives the request analysis page request sent by the terminal 110, parses the request analysis page request, and parses the operator account in the request analysis page request to verify whether the extracted operator account has access to the suggestion analysis page. After verifying that the extracted operator account has the right to access the suggestion analysis page, the server 120 obtains the suggestion analysis page data, and returns the obtained suggestion analysis page data to the terminal 110 corresponding to the suggestion analysis page request.

The terminal 110 receives the suggested analysis page data returned by the server 120, and displays a suggestion analysis page according to the suggested analysis page data. The terminal 110 acquires a push suggestion analysis request triggered by the push operator on the suggestion analysis page, and sends the obtained push suggestion analysis request to the server 120. The push suggestion analysis request is used to trigger the server 120 to generate a push suggestion request.

S304. Extract the recommended analysis condition in the push suggestion analysis request.

In one embodiment, after receiving the push suggestion analysis request, the server 120 parses the push suggestion analysis request, and extracts the suggestion analysis condition from the push suggestion analysis request by parsing. The recommended analysis condition may specifically include at least one of a push time, an information type, and a user type.

S306. In the historical push task of the record, query a historical push task that meets the extracted recommended analysis condition.

In one embodiment, a plurality of historical push tasks are recorded in the server 120, and the historical push tasks are push tasks that have been executed. Historical push tasks include push time, type of information, and At least one of the user types. The server 120 queries the historical push tasks that meet the extracted suggestion analysis conditions among the plurality of historical push tasks recorded.

For example, the extracted analysis condition includes the information type, and the information type is the advertisement information type. The historical push task corresponding to the query advertisement information type in the recorded historical push task of the server 120 is the historical push task that meets the extracted suggestion analysis condition.

S308. Acquire a push effect score corresponding to each historical push task that is queried.

In one embodiment, the server 120 further stores a push effect score corresponding to each recorded historical push task. The push effect score is a score reflecting the push effect of the historical push task. The higher the push effect score, the better the push effect. The server 120 queries the historical push task that meets the extracted recommended analysis condition, and obtains the push effect score corresponding to each of the searched historical push tasks.

S310. Select a preset number of historical push tasks from each historical push task according to the obtained push effect score.

In one embodiment, the server 120 compares the pushed effect scores of the historical push tasks, compares the push effect scores of the historical push tasks, selects the historical push task with the highest push effect score, and pushes from the remaining history. The historical push task with the highest push effect score is selected in the task until the number of selected historical push tasks reaches the preset number.

In an embodiment, after obtaining the push effect score, the server 120 calculates the average push score of the searched historical push task according to the obtained push effect score, and selects the historical push task whose push effect score is higher than the push effect average score. .

S312: Analyze the selected historical push task, and generate a push suggestion corresponding to the push suggestion analysis request.

In an embodiment, after selecting the historical push task, the server 120 extracts at least one of the information type, the user type, and the push time in the historical push task, and generates and pushes suggestions according to the extracted information type, user type, and time. Analyze the push recommendations for the request. The push suggestion may include at least one of a recommended push information type, a recommended push time period, and a suggested push user type.

In an embodiment, S312 may specifically include the following content: obtaining selected historical pushes The user types corresponding to the tasks are respectively classified; the selected historical push tasks are classified according to the user type; and the push effect scores corresponding to the history push tasks of the user types are determined, and the user type selection suggestions corresponding to the push suggestion analysis requests are determined.

In one embodiment, the server 120 obtains the user types corresponding to the selected historical push tasks, and classifies the selected historical push tasks according to the acquired user types to obtain historical push tasks for each user type. The server 120 acquires the push effect score corresponding to the historical push task of each user type, and calculates the average push score corresponding to each user type according to the obtained push effect score. The server 120 averages the push effects corresponding to each user type and each user type to generate a user type selection suggestion.

In one embodiment, the server 120 compares the average scores of the push effects corresponding to the user types, and compares and selects the user types with a higher average number of push effects, and selects the user types according to the selected user types and the selected user types. The push effect averages the user type selection suggestions.

In another embodiment, the server 120 selects the user type with the highest average push effect from each user type, and generates a user type selection suggestion according to the selected user type.

In this embodiment, the recommended and analyzed condition in the push recommendation analysis request is extracted, and the historical push task that meets the extracted recommended analysis condition is queried in the recorded historical push task, and the push effect score corresponding to each historical push task according to the query is extracted. The preset number of historical push tasks are selected to ensure that the selected historical push task matches the push suggestion analysis request, and the accuracy of the selected historical push task is improved. The selected historical push task is analyzed, and the push suggestion corresponding to the push suggestion analysis request is generated by analysis, the rationality and accuracy of the obtained push suggestion are determined, and the information push according to the generated push suggestion is improved to improve the push effect of the information push.

As shown in FIG. 4, in an embodiment, S306 further includes a step of generating a push recommendation according to the historical push task, the step specifically including the following:

S402. If the historical push task that meets the extracted suggestion analysis condition is not queried, the push effect score corresponding to each historical push task of the record is obtained.

In an embodiment, after the server 120 does not query the historical push task that meets the extracted recommended analysis condition, the server 120 obtains the push effect score corresponding to each historical push task of the record.

S404, pushing the historical records of the records according to the obtained push effect scores in descending order Tasks are sorted.

In one embodiment, the server 120 compares the recorded push effect scores corresponding to each historical push task, and sorts the recorded historical push tasks according to the size of the push effect score by comparing. The server 120 may specifically sort the recorded historical push tasks in descending order of the push effect scores. The server 120 can also sort the recorded historical push tasks in descending order of the promotion effect scores.

S406. Select a preset number of historical push tasks from the sorted historical push tasks.

In one embodiment, after sorting the historical push tasks of the records according to the order of the push effect scores from high to low, the server 120 selects the previous historical push tasks from the sorted historical push tasks, and selects the presets. The number of historical push tasks.

In one embodiment, the server 120 sorts the recorded historical push tasks according to the push effect scores in descending order, selects the sorted historical push tasks from the sorted historical push tasks, and selects the preset number. Historical push task.

S408: Analyze the selected historical push task, and generate a push suggestion corresponding to the push suggestion analysis request.

In one embodiment, the server 120 analyzes the selected historical push task, extracts the corresponding information type, push time, and user type in each selected historical push task, and statistically extracts the extracted information type, push time, and user type corresponding storage. The stored information type, push time, and user type determine the optimal push time period and the most appropriate user type for each information type. The server 120 generates a push recommendation corresponding to the push suggestion analysis request according to the optimal push time period and the most appropriate user type corresponding to each information type. The push suggestion may also include a corresponding sample of the push information copy. The server 120 transmits the generated push advice to the terminal 110.

In this embodiment, when the historical push task that meets the extracted recommended analysis condition is not queried, a historical push task with a higher push effect score is selected from the recorded historical push task, and the selected historical push task is analyzed to generate a push. Suggest. All the historical push tasks recorded are used as the basic task data, and the push effect scores are selected from the recorded historical push tasks according to the push effect scores, and the accuracy of the push suggestions is also ensured.

As shown in FIG. 5, in an embodiment, S312 specifically includes generating a push time period suggestion. Step, the step specifically includes the following contents:

S502. Acquire a push time corresponding to each selected historical push task.

In an embodiment, after selecting a preset number of historical push tasks from the recorded historical push tasks, the server 120 parses the selected historical push tasks, and extracts the push time in each selected historical push task by parsing. .

In one embodiment, the server 120 stores the recorded push logs of each historical push task, and the push log includes the push time corresponding to each historical push task. After selecting the historical push task from the recorded historical push task, the server 120 queries the push log corresponding to each historical push task, and extracts the push time corresponding to the historical push task from the queryed push log.

S504. Sort the selected historical push tasks according to the push time period to which the push time belongs.

In an embodiment, the server 120 stores a preset push time period, and after obtaining the push time corresponding to each selected historical push task, determining a push time period to which the push time corresponding to each history push task belongs, according to the determination. The push time period classifies the selected historical push tasks, and obtains historical push tasks corresponding to each push time period by classification.

S506: Determine, according to the push effect score corresponding to the historical push task of each push time period after the classification, the push time segment recommendation corresponding to the push suggestion analysis request.

In one embodiment, the server 120 acquires the push effect scores of the historical push tasks for the historical push tasks corresponding to the push time segments, and calculates the push of the historical push tasks corresponding to the push time segments according to the push effect scores of the historical push tasks. The average score of the effect. The server 120 compares the average of the push effects of the historical push tasks corresponding to each push time period, and compares and selects the push time periods with the higher average scores of the preset number of push effects. The server 120 generates a push time segment proposal corresponding to the push suggestion analysis request according to the selected push time period.

In one embodiment, the server 120 compares the average of the push effects of the historical push tasks corresponding to each push time period, compares and obtains the push time period corresponding to the highest push effect average score, and generates a push time period according to the obtained push time period. Suggest. The push time period suggestion may further include a sample of the push information copy of the historical push task corresponding to the acquired push time.

In this embodiment, the selected historical push task is classified according to the push time period to which the push time of each historical push task belongs, and the obtained historical push task corresponding to each push time period is historically pushed for each push time period. The push effect score of the task is analyzed to determine the push time. The inter-segment recommendation ensures the accuracy of the push time period recommendation, and ensures that the push of the information according to the push time period can improve the push effect.

As shown in FIG. 6 , in one embodiment, the information push suggesting method further includes the step of calculating a push effect score, and the step specifically includes the following:

S602. Record the executed information push task as a historical push task.

In one embodiment, after performing the information push task, the server 120 records the executed information push task as a historical push task to obtain a recorded history push task.

In one embodiment, the server 120 extracts the task identifier in the executed information push task, and stores the extracted task identifier in association with the executed information push task to obtain a recorded history push task.

S604. Acquire, the push feedback information corresponding to the recorded historical push task.

In an embodiment, when the server 120 performs the information push task, the server 120 pushes the information to be pushed in the information push task to the terminal corresponding to each user account. The terminal 110 returns the push feedback information to the server 120. The push feedback information includes task identification, push arrival information, push click information, and push browsing information.

S606. The push effect data corresponding to the history push task of the record is statistically recorded according to the obtained push feedback information.

In one embodiment, the server 120 parses the push feedback information, and extracts the task identifier, the push arrival information, the push click information, and the push browse information in the push feedback information by parsing. The server 120 acquires the total amount of pushes of the historical push task corresponding to the extracted task identifier, and pushes the historical push task corresponding to the extracted push arrival information, the push browsing information, and the obtained push total statistical task identifier according to the extracted push arrival information. Performance data.

In one embodiment, the push effect data includes push arrival rate, push click rate, arrival rate within 1 hour, click rate within 1 hour, and push browse rate. The server 120 counts the push arrival rate and the arrival rate within one hour according to the push arrival information and the push total amount, and pushes the click rate and the one-hour click rate according to the push click information and the push total amount, and pushes according to the push browsing information and the push total amount. Browsing rate.

S608. Calculate a push effect score of the recorded historical push task according to the statistical push effect data.

In an embodiment, the server 120 obtains the weight corresponding to the push effect data, performs weighting calculation according to the statistically pushed weight effect data of each historical push task and the weight value corresponding to the obtained push effect data, and obtains the historical push task. Push effect score.

In an embodiment, the server 120 respectively obtains the weights corresponding to the push arrival rate, the push click rate, the one hour arrival rate, the one hour click rate, and the push browse rate in the push effect data, according to the obtained weights and The pushed push effect data corresponding to each historical push task is weighted and calculated, and the calculated values are standardized to obtain a push effect score corresponding to each historical push task.

In this embodiment, the historical push record is recorded, and the push effect data of each historical push task is counted according to the pushed push feedback information corresponding to the obtained historical push task, and the push effect score of each historical push task is calculated according to the statistical push effect data. By pushing the feedback information to statistically push the effect data, the authenticity of the push effect data is verified, thereby ensuring the accuracy of the push effect score calculated according to the push effect data.

As shown in FIG. 7, in an embodiment, an information push suggestion generating apparatus 700 is provided. The apparatus specifically includes the following contents: a request obtaining module 702, a condition extracting module 704, a task querying module 706, a score obtaining module 708, and a task. Module 710 and suggestion generation module 712 are selected.

The request obtaining module 702 is configured to obtain a push suggestion analysis request.

The condition extraction module 704 is configured to extract the recommended analysis condition in the push suggestion analysis request.

The task querying module 706 is configured to query, in the recorded historical push task, a historical push task that meets the extracted recommended analysis condition.

The score obtaining module 708 is configured to obtain a push effect score corresponding to each of the searched historical push tasks.

The task selection module 710 is configured to select a preset number of historical push tasks from each historical push task according to the obtained push effect score.

The suggestion generating module 712 is configured to analyze the selected historical push task, and generate a push suggestion corresponding to the push suggestion analysis request.

In an embodiment, the suggestion generating module 712 is further configured to obtain a user type corresponding to each selected historical push task; and classify each selected historical push task according to the user type; According to the push effect score corresponding to the historical push task of each user type, the user type selection suggestion corresponding to the push suggestion analysis request is determined.

In one embodiment, the score acquisition module 708 is further configured to: if the historical push task that meets the extracted suggestion analysis condition is not queried, obtain a push effect score corresponding to each of the recorded historical push tasks.

The task selection module 710 is further configured to sort the recorded historical push tasks according to the obtained push effect scores in descending order; and select a preset number of previous historical push tasks from the sorted historical push tasks.

As shown in FIG. 8, in one embodiment, the suggestion generation module 712 specifically includes the following: a push time acquisition module 712a, a push task assignment module 712b, and a push time suggestion module 712c.

The push time obtaining module 712a is configured to obtain a push time corresponding to each selected historical push task.

The push task assignment module 712b is configured to classify the selected historical push tasks according to the push time period to which the push time belongs.

The push time suggesting module 712c is configured to determine a push time period recommendation corresponding to the push suggestion analysis request according to the push effect score corresponding to the historical push task of each push time period after the classification.

In this embodiment, the push time period to which the push time of each historical push task is selected belongs. The selected historical push task is classified, and the obtained historical push task corresponding to each push time period is analyzed, and the push effect score of the historical push task corresponding to each push time period is analyzed, and the push time period suggestion is determined to ensure the push time period suggestion. The accuracy ensures that the push of information according to the push time period can improve the push effect.

As shown in FIG. 9 , in one embodiment, the information push suggestion generating apparatus 700 specifically includes the following: a task recording module 714 , an information acquiring module 716 , a data statistics module 718 , and a data computing module 720 .

The task record module 714 is configured to record the executed information push task as a historical push task;

The information obtaining module 716 is configured to obtain the push feedback information corresponding to the recorded historical push task.

The data statistics module 718 is configured to statistically record the push effect data corresponding to the historical push task according to the obtained push feedback information;

The data calculation module 720 is configured to calculate a push effect score of the recorded historical push task according to the statistical push effect data.

Each of the above-described information push suggestion generating means may be implemented in whole or in part by software, hardware, and a combination thereof. The network interface may be an Ethernet or a wireless network card. Each of the above modules may be embedded in a hardware form or independent of a processor in the server, or may be stored in a memory of the server in a software form, so that the processor calls to perform operations corresponding to the above modules. The processor can be a central processing unit (CPU), a microprocessor, a microcontroller, or the like.

A computer apparatus comprising a memory and a processor, wherein the memory stores computer executable instructions, the computer executable instructions being executed by the processor, causing the processor to perform the step of: obtaining a push suggestion analysis request Extracting the recommended analysis condition in the push suggestion analysis request; in the historical push task of the record, querying the historical push task that meets the extracted recommended analysis condition; obtaining the push effect score corresponding to each of the searched historical push tasks; The push effect score is obtained, and a preset number of historical push tasks are selected from each historical push task; the selected historical push task is analyzed, and a push suggestion corresponding to the push suggestion analysis request is generated.

In an embodiment, after the historical push task that meets the extracted recommended analysis condition is queried in the recorded historical push task, the processor further performs the following steps: if the historical push task that meets the extracted recommended analysis condition is not queried And obtaining the corresponding push effect scores of each historical push task of the record; sorting the historical push tasks of the records according to the obtained push effect scores from high to low; and selecting presets from the sorted historical push tasks The quantity is sorted by the previous historical push task; the selected historical push task is analyzed, and a push suggestion corresponding to the push suggestion analysis request is generated.

In an embodiment, the selected historical push task is analyzed, and the push suggestion corresponding to the push suggestion analysis request is generated, including: obtaining the push time corresponding to each selected historical push task; and selecting according to the push time segment to which the push time belongs Each historical push task is classified; and according to the push effect score corresponding to the historical push task of each push time period after the classification, the push time segment suggestion corresponding to the push suggestion analysis request is determined.

In an embodiment, the selected historical push task is analyzed, and the push suggestion corresponding to the push suggestion analysis request is generated, including: obtaining the user type corresponding to each selected historical push task; and selecting each historical push according to the user type. The task is classified; according to the push effect score corresponding to the historical push task of each user type, the user type selection suggestion corresponding to the push suggestion analysis request is determined.

In an embodiment, the processor further performs the steps of: recording the executed information push task as a historical push task; acquiring the pushed push feedback information corresponding to the recorded historical push task; and performing historical push task according to the obtained push feedback information. Corresponding push effect data; calculating the push effect score of the recorded historical push task according to the statistical push effect data.

In this embodiment, the recommended and analyzed condition in the push recommendation analysis request is extracted, and the historical push task that meets the extracted recommended analysis condition is queried in the recorded historical push task, and the push effect score corresponding to each historical push task according to the query is extracted. The preset number of historical push tasks are selected to ensure that the selected historical push task matches the push suggestion analysis request, and the accuracy of the selected historical push task is improved. Analyze the selected historical push task, analyze and generate the push suggestion corresponding to the push suggestion analysis request, determine the rationality and accuracy of the obtained push suggestion, and ensure the root Pushing information according to the generated push suggestions improves the push effect of information push.

One or more storage media storing computer executable instructions that, when executed by one or more processors, cause one or more processors to perform the steps of: obtaining a push suggestion analysis request; extracting push recommendations The recommended analysis condition in the request is analyzed; in the historical push task of the record, the historical push task that meets the extracted recommended analysis condition is queried; the push effect score corresponding to each historical push task obtained by the query is obtained; and the obtained push effect is obtained according to the obtained The score is selected from a historical push task by a preset number of historical push tasks; the selected historical push task is analyzed, and a push suggestion corresponding to the push suggestion analysis request is generated.

In an embodiment, the processor further performs the steps of: recording the executed information push task as a historical push task; acquiring the pushed push feedback information corresponding to the recorded historical push task; and performing historical push task according to the obtained push feedback information. Corresponding push effect data; according to statistics The push effect data calculates the push effect score of the historical push task recorded.

According to an example of the embodiment, all or part of the processes in the foregoing embodiment may be completed by instructing related hardware by computer executable instructions, which may be stored in a computer readable storage medium. As in the embodiment of the present application, the program may be stored in a storage medium of the computer system and executed by at least one processor in the computer system to implement a flow including an embodiment of the methods as described above. The storage medium includes, but is not limited to, a magnetic disk, a USB flash drive, an optical disk, a read-only memory (ROM), and the like.

The technical features of the above-described embodiments may be arbitrarily combined. For the sake of brevity of description, all possible combinations of the technical features in the above embodiments are not described. However, as long as there is no contradiction between the combinations of these technical features, All should be considered as the scope of this manual.

The above-mentioned embodiments are merely illustrative of several embodiments of the present application, and the description thereof is more specific and detailed, but is not to be construed as limiting the scope of the invention. It should be noted that a number of variations and modifications may be made by those skilled in the art without departing from the spirit and scope of the present application. Therefore, the scope of the invention should be determined by the appended claims.

Claims

A method for generating information push suggestions, including:

Obtain a push suggestion analysis request;

Extracting the suggested analysis conditions in the push suggestion analysis request;

In the historical push task of the record, the historical push task that meets the extracted analysis condition of the query is queried;

Obtaining a push effect score corresponding to each historical push task that is queried;

Selecting, according to the obtained push effect score, a preset number of historical push tasks from the historical push tasks; and

The selected historical push task is analyzed, and a push suggestion corresponding to the push suggestion analysis request is generated.
The method according to claim 1, wherein after the historical push task that meets the extracted recommended analysis condition is queried in the recorded historical push task, the method further includes:

If the historical push task that meets the extracted recommended analysis condition is not queried, the corresponding push effect scores of each historical push task of the record are obtained;

Sorting the historical push tasks of the record according to the obtained push effect scores from high to low;

Selecting a preset number of historical push tasks from the sorted historical push tasks; and

Performing the step of analyzing the selected historical push task to generate a push suggestion corresponding to the push suggestion analysis request.
The method according to claim 2, wherein the analyzing the selected historical push task, generating a push suggestion corresponding to the push suggestion analysis request, comprising:

Obtaining a corresponding push time for each selected historical push task;

Sorting the selected historical push tasks according to the push time period to which the push time belongs; and

According to the push effect score corresponding to the historical push task of each push time period after classification, it is determined The push recommendation analysis request corresponds to a push time period suggestion.
The method according to claim 2, wherein the analyzing the selected historical push task, generating a push suggestion corresponding to the push suggestion analysis request, comprising:

Obtaining the user types corresponding to each selected historical push task;

Sorting the selected historical push tasks according to the user type; and

The user type selection suggestion corresponding to the push suggestion analysis request is determined according to the push effect score corresponding to the historical push task of each user type.
The method of claim 1 further comprising:

Record the executed information push task as a historical push task;

Obtaining the push feedback information corresponding to the historical push task of the record;

And estimating, according to the obtained push feedback information, the push effect data corresponding to the historical push task of the record; and

The push effect score of the historical push task of the record is calculated according to the statistical push effect data.
An information push suggestion generating device, comprising:

Requesting an acquisition module for obtaining a push suggestion analysis request;

a condition extraction module, configured to extract a recommended analysis condition in the push suggestion analysis request;

The task query module is configured to query, in the historical push task of the record, a historical push task that meets the extracted recommended analysis condition;

a score obtaining module, configured to obtain a push effect score corresponding to each historical push task that is queried;

a task selection module, configured to select a preset number of historical push tasks from the historical push tasks according to the obtained push effect scores; and

The suggestion generating module is configured to analyze the selected historical push task, and generate a push suggestion corresponding to the push suggestion analysis request.
The device according to claim 6, wherein the score obtaining module is further configured to: if the historical push task that meets the extracted recommended analysis condition is not queried, obtain the record Push performance scores corresponding to each historical push task; and

The task selection module is further configured to sort the historical push tasks of the record according to the obtained push effect scores in descending order; select a preset number of previous history from the sorted historical push tasks. Push the task.
The device according to claim 7, wherein the suggestion generating module is further configured to acquire a push time corresponding to each selected historical push task; and to select the selected historical push task according to a push time period to which the push time belongs Performing classification; determining a push time period recommendation corresponding to the push suggestion analysis request according to the push effect score corresponding to the historical push task of each push time period after the classification.
The apparatus according to claim 7, wherein the suggestion generating module comprises:

a push time obtaining module, configured to obtain a push time corresponding to each selected historical push task;

a push task assignment module, configured to classify the selected historical push tasks according to a push time period to which the push time belongs; and

The push time suggesting module is configured to determine a push time period recommendation corresponding to the push suggestion analysis request according to a push effect score corresponding to the historical push task of each push time period after the classification.
The device according to claim 6, wherein the device further comprises:

a task record module, configured to record an executed information push task as a historical push task;

An information obtaining module, configured to obtain push feedback information corresponding to the recorded historical push task;

a data statistics module, configured to collect, according to the obtained push feedback information, push performance data corresponding to the historical push task of the record; and

The data calculation module is configured to calculate a push effect score of the historical push task of the record according to the statistical push effect data.
A computer device comprising a memory having stored therein computer executable instructions, the computer executable instructions being executed by the processor such that the processor performs the following steps:

Obtain a push suggestion analysis request;

Extracting the suggested analysis conditions in the push suggestion analysis request;

In the historical push task of the record, the historical push task that meets the extracted analysis condition of the query is queried;

Obtaining a push effect score corresponding to each historical push task that is queried;

Selecting, according to the obtained push effect score, a preset number of historical push tasks from the historical push tasks; and

The selected historical push task is analyzed, and a push suggestion corresponding to the push suggestion analysis request is generated.
The computer device according to claim 11, wherein in the recorded historical push task, after querying the historical push task that meets the extracted recommended analysis condition, the processor further performs the following steps:

If the historical push task that meets the extracted recommended analysis condition is not queried, the corresponding push effect scores of each historical push task of the record are obtained;

Sorting the historical push tasks of the record according to the obtained push effect scores from high to low;

Selecting a preset number of historical push tasks from the sorted historical push tasks; and

Performing the step of analyzing the selected historical push task to generate a push suggestion corresponding to the push suggestion analysis request.
The computer device according to claim 12, wherein the analyzing the selected historical push task, generating a push suggestion corresponding to the push suggestion analysis request, comprising:

Obtaining a corresponding push time for each selected historical push task;

Sorting the selected historical push tasks according to the push time period to which the push time belongs; and

The push time segment recommendation corresponding to the push suggestion analysis request is determined according to the push effect score corresponding to the historical push task of each push time period after the classification.
The computer device according to claim 12, wherein the analyzing the selected historical push task, generating a push suggestion corresponding to the push suggestion analysis request, comprising:

Obtaining the user types corresponding to each selected historical push task;

Sorting the selected historical push tasks according to the user type; and

The user type selection suggestion corresponding to the push suggestion analysis request is determined according to the push effect score corresponding to the historical push task of each user type.
The method of claim 11 wherein said processor further performs the following steps:

Record the executed information push task as a historical push task;

Obtaining the push feedback information corresponding to the historical push task of the record;

And estimating, according to the obtained push feedback information, the push effect data corresponding to the historical push task of the record; and

The push effect score of the historical push task of the record is calculated according to the statistical push effect data.
One or more storage media storing computer executable instructions that, when executed by one or more processors, cause the one or more processors to perform the following steps:

Obtain a push suggestion analysis request;

Extracting the suggested analysis conditions in the push suggestion analysis request;

In the historical push task of the record, the historical push task that meets the extracted analysis condition of the query is queried;

Obtaining a push effect score corresponding to each historical push task that is queried;

Selecting, according to the obtained push effect score, a preset number of historical push tasks from the historical push tasks; and

The selected historical push task is analyzed, and a push suggestion corresponding to the push suggestion analysis request is generated.
The storage medium according to claim 16, wherein in the recorded historical push task, after querying the historical push task that meets the extracted recommended analysis condition, the processor further performs the following steps:

If the historical push task that meets the extracted recommended analysis condition is not queried, the corresponding push effect scores of each historical push task of the record are obtained;

Sorting the historical push tasks of the record according to the obtained push effect scores from high to low;

Selecting a preset number of historical push tasks from the sorted historical push tasks; and

Performing the step of analyzing the selected historical push task to generate a push suggestion corresponding to the push suggestion analysis request.
The computer device according to claim 17, wherein the analyzing the selected historical push task, generating a push suggestion corresponding to the push suggestion analysis request, comprising:

Obtaining a corresponding push time for each selected historical push task;

Sorting the selected historical push tasks according to the push time period to which the push time belongs; and

The push time segment recommendation corresponding to the push suggestion analysis request is determined according to the push effect score corresponding to the historical push task of each push time period after the classification.
The storage medium according to claim 17, wherein the analyzing the selected historical push task, and generating a push suggestion corresponding to the push suggestion analysis request, comprising:

Obtaining the user types corresponding to each selected historical push task;

Sorting the selected historical push tasks according to the user type; and

The user type selection suggestion corresponding to the push suggestion analysis request is determined according to the push effect score corresponding to the historical push task of each user type.
The storage medium of claim 16, wherein the processor further performs the following steps:

Record the executed information push task as a historical push task;

Obtaining the push feedback information corresponding to the historical push task of the record;

And estimating, according to the obtained push feedback information, the push effect data corresponding to the historical push task of the record; and

The push effect score of the historical push task of the record is calculated according to the statistical push effect data.