CN117151794A - Advertisement task recommendation method and related device - Google Patents
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Abstract
The application relates to an advertisement task recommending method and a related device. In the embodiment of the application, the user characteristic information of the target user is obtained; inputting the user characteristic information into a classification model, and obtaining user category information output by the classification model; acquiring a current online advertisement task set, and labeling each advertisement task in the advertisement task set based on advertisement task characteristic information; matching the labeled advertisement task with user category information to determine a recommended advertisement task; and outputting the recommended advertisement task to the target user. The automatic recommendation of corresponding advertisement tasks aiming at different types of users is realized, so that the advertising benefits are improved.
Description
Technical Field
The application relates to the technical field of computers, in particular to an advertisement task recommending method and a related device.
Background
The integrating wall is a page for displaying various integrating tasks in one application, such as downloading and installing recommended high-quality applications, registering, filling in forms and the like, so that a user can finish the tasks to obtain the integral. The score wall is another novel advertisement profit mode provided for application developers by a third-party advertisement platform besides advertisement bars and screen inserting advertisements.
At present, advertising tasks (office) recommendation of an integral wall is performed manually based on profit/click (cpc) performance of the advertising tasks (office) within 24 hours, and the processing mode can be used only marginally when the office is less, but as the office docking quantity is increased, sorting recommendation needs to be adjusted manually frequently according to business experience and cpc performance of the office, the workload is high, and corresponding recommendation is difficult to be performed for different types of users.
Disclosure of Invention
In view of the foregoing, the present application has been made to provide an advertising task recommendation method and related apparatus that overcomes or at least partially solves the foregoing problems.
In a first aspect, an embodiment of the present application provides an advertisement task recommendation method, including the steps of:
acquiring user characteristic information of a target user;
inputting the user characteristic information into a classification model, and obtaining user category information output by the classification model; the classification model is obtained by training based on a user characteristic information data set in a historical time period;
acquiring a current online advertisement task set, and labeling each advertisement task in the advertisement task set based on advertisement task characteristic information;
matching the labeled advertisement task with user category information to determine a recommended advertisement task;
and outputting the recommended advertisement task to the target user.
In one embodiment, the user characteristic information includes user attribution characteristic information, user behavior characteristic information.
In one embodiment, the training step of the classification model includes:
acquiring a full user characteristic information data set in a first historical time period and an incremental user characteristic information data set in a second historical time period;
pre-training the initial classification model by using the full-scale user characteristic information data set to obtain a pre-trained classification model;
and carrying out parameter adjustment processing on the pre-trained classification model by using the incremental user characteristic information data set to obtain a classification model after training.
In one embodiment, the performing parameter adjustment processing on the pre-trained classification model by using the incremental user feature information data set to obtain a trained classification model includes:
dividing the incremental user characteristic information data set into a plurality of incremental user characteristic information data subsets;
and carrying out parameter adjustment processing on the pre-trained classification model by utilizing the incremental user characteristic information data subsets in batches according to a preset time interval to obtain the classification model after training.
In one embodiment, the classification model is a logistic regression classification model.
In one embodiment, the matching the tagged advertisement task with the user category information to determine a recommended advertisement task includes:
determining the advertisement task label value range corresponding to the target user according to the mapping relation between the user category and the advertisement task label value;
screening candidate advertisement tasks with advertisement task tag values within the advertisement task tag value range from the current advertisement task set;
and sequencing the candidate advertisement tasks according to the advertisement task tag value, and determining the recommended advertisement tasks.
In a second aspect, an embodiment of the present application provides an advertisement task recommendation apparatus, including:
the acquisition module is used for acquiring user characteristic information of the target user;
the user category identification module is used for inputting the user characteristic information into the classification model and acquiring the user category information output by the classification model; the classification model is obtained by training based on a user characteristic information data set in a historical time period;
the advertisement task processing module is used for acquiring the advertisement task set on line currently and labeling each advertisement task in the advertisement task set based on the advertisement task characteristic information;
the matching module is used for matching the labeled advertisement task with the user category information so as to determine a recommended advertisement task;
and the recommending module is used for outputting the recommended advertisement task to the target user.
In one embodiment, the user characteristic information includes user attribution characteristic information, user behavior characteristic information.
In one embodiment, the apparatus further comprises:
the model training module is used for acquiring a full user characteristic information data set in a first historical time period and an incremental user characteristic information data set in a second historical time period; pre-training the initial classification model by using the full-scale user characteristic information data set to obtain a pre-trained classification model; and carrying out parameter adjustment processing on the pre-trained classification model by using the incremental user characteristic information data set to obtain a classification model after training.
In one embodiment, the model training module is further configured to divide the incremental user feature information data set into a plurality of incremental user feature information data subsets; and carrying out parameter adjustment processing on the pre-trained classification model by utilizing the incremental user characteristic information data subsets in batches according to a preset time interval to obtain the classification model after training.
In one embodiment, the classification model is a logistic regression classification model.
In one embodiment, the matching module is further configured to determine an advertisement task tag value range corresponding to the target user according to a mapping relationship between the user category and the advertisement task tag value; screening candidate advertisement tasks with advertisement task tag values within the advertisement task tag value range from the current advertisement task set; and sequencing the candidate advertisement tasks according to the advertisement task tag value, and determining the recommended advertisement tasks.
In a third aspect, an embodiment of the present application provides a computer apparatus, including:
one or more processors;
a memory for storing one or more programs;
the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the advertising task recommendation method of any one of the first aspects.
In a fourth aspect, embodiments of the present application provide a computer-readable storage medium.
The computer readable storage medium has stored thereon a computer program which, when executed by a processor, implements the advertising task recommendation method according to any one of the first aspects.
In the embodiment of the application, the user characteristic information of the target user is obtained; inputting the user characteristic information into a classification model, and obtaining user category information output by the classification model; acquiring a current online advertisement task set, and labeling each advertisement task in the advertisement task set based on advertisement task characteristic information; matching the labeled advertisement task with user category information to determine a recommended advertisement task; and outputting the recommended advertisement task to the target user. The automatic recommendation of corresponding advertisement tasks aiming at different types of users is realized, so that the advertising benefits are improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the description of the embodiments will be briefly described below, it will be apparent that the drawings in the following description are only some embodiments of the present application, and that other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flowchart of an advertisement task recommendation method according to an embodiment of the present application;
fig. 2 is a schematic structural diagram of an advertisement task recommendation device according to a second embodiment of the present application;
fig. 3 is a schematic structural diagram of a computer device according to a third embodiment of the present application.
Detailed Description
The application is described in further detail below with reference to the drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the application and are not limiting thereof. It should be further noted that, for convenience of description, only some, but not all of the structures related to the present application are shown in the drawings.
The integrating wall is a page for displaying various integrating tasks in one application, such as downloading and installing recommended high-quality applications, registering, filling in forms and the like, so that a user can finish the tasks to obtain the integral. The score wall is another novel mobile advertisement profit mode provided for application developers by a third party mobile advertisement platform besides advertisement bars and screen inserting advertisements.
At present, advertising tasks (office) recommendation of an integral wall is performed manually based on profit/click (cpc) performance of the advertising tasks (office) within 24 hours, and the processing mode can be used only marginally when the office is less, but as the office docking quantity is increased, sorting recommendation needs to be adjusted manually frequently according to business experience and cpc performance of the office, the workload is high, and corresponding recommendation is difficult to be performed for different types of users.
In order to overcome the problems or at least partially solve the problems, the embodiment of the application provides an advertisement task recommending method, by which automatic recommendation of corresponding advertisement tasks for different types of users can be realized, so that advertisement benefits are improved. The following is a detailed description of examples.
Example 1
Fig. 1 is a flowchart of an advertisement task recommendation method according to an embodiment of the present application, where the method may be performed by an advertisement task recommendation device, and the advertisement task recommendation device may be implemented by software and/or hardware, and may be configured in a computer device, for example, a server, a personal computer, or the like. The advertisement task recommending method specifically comprises the following steps:
step 101, obtaining user characteristic information of a target user.
The target user is a user who is using the application containing the integral wall, when the target user uses the application to reach a preset time period or a certain set condition is reached, the page of the application is converted into an integral wall page, the page can display an integral task (namely an advertising task) to the target user, and after the target user finishes the integral task to obtain the integral, the target user can continue to use the application if downloading and installing the recommended high-quality application, registering, filling a table and the like. The application may be an exercise APP, a reading APP, a game applet, a web game, video software, and the like. In some applications, the target user may continue to use the application even if the integration task is not completed.
The user characteristic information is information capable of reflecting the type of the user, and can be extracted from the user attribution characteristic information and the user behavior characteristic information. For example, information is extracted from the user source channel, purchase amount material attribute, user mobile phone model, promotion behavior (point of sale), offer activation rate, CPC, and the like.
Step 102, inputting the user characteristic information into a classification model, and obtaining user category information output by the classification model. The classification model is obtained by training based on the user characteristic information data set in the historical time period.
In this step, user category information of the target user is obtained by inputting the user feature information into the trained classification model. The user category information can reflect user specific categories, which can be set according to actual needs, for example, users can be classified into high value users, potential users, low value users, and the like.
The classification model may be a logistic regression classification model, a decision tree classification model, a K-nearest neighbor classification model, and so on. In this embodiment, the classification model is a logistic regression classification model, where logistic regression fits model parameters by linear expression of user feature information, and then uses a sigmoid function to map the result between [0,1] to represent the probability that the sample belongs to a certain class.
For this classification model, the following steps may be employed for training:
and step A, acquiring a full user characteristic information data set in a first historical time period and an incremental user characteristic information data set in a second historical time period.
The second historical time period may be a time period that is located before the current time and is spaced from the current time by a first preset time period, where the first preset time period may be 5 days, 10 days, and so on.
The first historical time period may be a time period before a first preset time period of the current time interval and a second preset time period of the current time interval, where the second preset time period is longer than the first preset time period, and the second preset time period may be 30 days, 40 days, and the like.
And B, pre-training the initial classification model by using the full user characteristic information data set to obtain a pre-trained classification model.
After carrying out feature engineering treatment and normalization treatment on the full-quantity user feature information data set, splitting the full-quantity user feature information data set into a training data base set and a test data set according to a certain proportion, for example, according to the following steps: 2 splitting into a training data set and a test data set; the training data set is imported into an initial classification model for pre-training, the fitting result is continuously adjusted, and the initial classification model can be constructed by python; and verifying the accuracy and generalization capability of the model by using the test data set, and finally obtaining the pre-trained classification model.
And C, performing parameter adjustment processing on the pre-trained classification model by using the incremental user characteristic information data set to obtain a trained classification model.
And the pre-trained classification model is subjected to parameter adjustment processing by utilizing the incremental user characteristic information data set, so that the performance and generalization capability of the model can be improved. In particular, step C may comprise the following sub-steps:
and C1, dividing the incremental user characteristic information data set into a plurality of incremental user characteristic information data subsets. Each subset of incremental user characteristic information data serves as a training batch.
And C2, carrying out parameter adjustment processing on the pre-trained classification model by utilizing the incremental user characteristic information data subsets in batches according to a preset time interval to obtain a classification model after training.
Step 103, acquiring a current online advertisement task set, and labeling each advertisement task in the advertisement task set based on the advertisement task characteristic information.
In this step, each advertisement task in the advertisement task set is labeled according to the advertisement task feature information, such as class, value, etc., to obtain a label value (score) of each advertisement task. The category may be APP promotion, brand promotion, campaign promotion, social media promotion, and so on.
Step 104, matching the labeled advertisement task with the user category information to determine a recommended advertisement task.
In this step, recommended advertisement tasks are confirmed based on the user category and the tag value of each advertisement task in the current online advertisement task set. The number of recommended advertising tasks may be one or more.
Specifically, the step 104 may include:
and D, determining the advertisement task label value range corresponding to the target user according to the mapping relation between the user category and the advertisement task label value. The mapping relation between the user category and the advertisement task label value can be obtained from a mapping table of the established user category corresponding to the advertisement task label value.
And E, screening candidate advertisement tasks with advertisement task tag values within the advertisement task tag value range from the current advertisement task set.
And F, sequencing the candidate advertisement tasks according to the advertisement task tag value, and determining the recommended advertisement tasks. For example, candidate advertisement tasks may be ranked from high to low according to tag values, and one or N (N is a natural number greater than or equal to 2) advertisement tasks with the highest tag value may be selected as recommended advertisement tasks.
Step 105, outputting the recommended advertisement task to the target user.
In this step, after determining the recommended advertisement tasks, if the recommended advertisement tasks are only one, the recommended advertisement tasks may be directly output to the target user. If the recommended advertisement tasks are N, the recommended advertisement tasks can be output to the target user in batches according to the preset quantity from high to low according to the tag value.
In an embodiment, user characteristic information of a target user is obtained; inputting the user characteristic information into a classification model, and obtaining user category information output by the classification model; acquiring a current online advertisement task set, and labeling each advertisement task in the advertisement task set based on advertisement task characteristic information; matching the labeled advertisement task with user category information to determine a recommended advertisement task; and outputting the recommended advertisement task to the target user. The automatic recommendation of corresponding advertisement tasks aiming at different types of users is realized, so that the advertising benefits are improved.
It should be noted that, for simplicity of description, the method embodiments are shown as a series of acts, but it should be understood by those skilled in the art that the embodiments are not limited by the order of acts, as some steps may occur in other orders or concurrently in accordance with the embodiments. Further, those skilled in the art will appreciate that the embodiments described in the specification are presently preferred embodiments, and that the acts are not necessarily required by the embodiments of the application.
Example two
Fig. 2 is a schematic structural diagram of an advertisement task recommendation device according to a second embodiment of the present application, where the advertisement task recommendation device may specifically include the following modules:
an obtaining module 201, configured to obtain user characteristic information of a target user.
The user category identification module 202 is configured to input user feature information into a classification model, and obtain user category information output by the classification model; the classification model is obtained by training based on the user characteristic information data set in the historical time period.
The advertisement task processing module 203 is configured to obtain a current online advertisement task set, and tag each advertisement task in the advertisement task set based on advertisement task feature information.
And the matching module 204 is used for matching the labeled advertisement task with the user category information to determine a recommended advertisement task.
And the recommending module 205 is used for outputting the recommended advertisement task to the target user.
In one embodiment, the user characteristic information includes user attribution characteristic information, user behavior characteristic information.
In one embodiment, the apparatus further comprises:
the model training module is used for acquiring a full user characteristic information data set in a first historical time period and an incremental user characteristic information data set in a second historical time period; pre-training the initial classification model by using the full-scale user characteristic information data set to obtain a pre-trained classification model; and carrying out parameter adjustment processing on the pre-trained classification model by using the incremental user characteristic information data set to obtain a classification model after training.
In one embodiment, the model training module is further configured to divide the incremental user feature information data set into a plurality of incremental user feature information data subsets; and carrying out parameter adjustment processing on the pre-trained classification model by utilizing the incremental user characteristic information data subsets in batches according to a preset time interval to obtain the classification model after training.
In one embodiment, the classification model is a logistic regression classification model.
In one embodiment, the matching module 204 is further configured to determine, according to a mapping relationship between the user category and the advertisement task tag value, an advertisement task tag value range corresponding to the target user; screening candidate advertisement tasks with advertisement task tag values within the advertisement task tag value range from the current advertisement task set; and sequencing the candidate advertisement tasks according to the advertisement task tag value, and determining the recommended advertisement tasks.
The advertisement task recommending device provided by the embodiment of the application can execute the advertisement task recommending method provided by any embodiment of the application, and has the corresponding functional modules and beneficial effects of the executing method.
Example III
Fig. 3 is a schematic structural diagram of a computer device according to a third embodiment of the present application. FIG. 3 illustrates a block diagram of an exemplary computer device 12 suitable for use in implementing embodiments of the present application. The computer device 12 shown in fig. 3 is merely an example and should not be construed as limiting the functionality and scope of use of embodiments of the present application.
As shown in FIG. 3, computer device 12 is in the form of a general purpose computing device. Components of computer device 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, a bus 18 that connects the various system components, including the system memory 28 and the processing units 16.
Bus 18 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, and a local bus using any of a variety of bus architectures. By way of example, and not limitation, such architectures include Industry Standard Architecture (ISA) bus, micro channel architecture (MAC) bus, enhanced ISA bus, video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus.
Computer device 12 typically includes a variety of computer system readable media. Such media can be any available media that is accessible by computer device 12 and includes both volatile and nonvolatile media, removable and non-removable media.
The system memory 28 may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM) 30 and/or cache memory 32. The computer device 12 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 34 may be used to read from or write to non-removable, nonvolatile magnetic media (not shown in FIG. 3, commonly referred to as a "hard disk drive"). Although not shown in fig. 3, a magnetic disk drive for reading from and writing to a removable non-volatile magnetic disk (e.g., a "floppy disk"), and an optical disk drive for reading from or writing to a removable non-volatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In such cases, each drive may be coupled to bus 18 through one or more data medium interfaces. Memory 28 may include at least one program product having a set (e.g., at least one) of program modules configured to carry out the functions of embodiments of the application.
A program/utility 40 having a set (at least one) of program modules 42 may be stored in, for example, memory 28, such program modules 42 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment. Program modules 42 generally perform the functions and/or methods of the embodiments described herein.
The computer device 12 may also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), one or more devices that enable a user to interact with the computer device 12, and/or any devices (e.g., network card, modem, etc.) that enable the computer device 12 to communicate with one or more other computing devices. Such communication may occur through an input/output (I/O) interface 22. Moreover, computer device 12 may also communicate with one or more networks such as a Local Area Network (LAN), a Wide Area Network (WAN) and/or a public network, such as the Internet, through network adapter 20. As shown, network adapter 20 communicates with other modules of computer device 12 via bus 18. It should be appreciated that although not shown, other hardware and/or software modules may be used in connection with computer device 12, including, but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, data backup storage systems, and the like.
The processing unit 16 executes various functional applications and data processing by running programs stored in the system memory 28, for example, implementing the advertising task recommendation method provided by the embodiment of the present application.
Example IV
The fourth embodiment of the present application further provides a computer readable storage medium, on which a computer program is stored, where the computer program when executed by a processor implements each process of the advertisement task recommendation method described above, and the same technical effects can be achieved, so that repetition is avoided, and no further description is given here.
The computer readable storage medium may include, for example, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium would include the following: 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 this document, 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.
Note that the above is only a preferred embodiment of the present application and the technical principle applied. It will be understood by those skilled in the art that the present application is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the application. Therefore, while the application has been described in connection with the above embodiments, the application is not limited to the embodiments, but may be embodied in many other equivalent forms without departing from the spirit or scope of the application, which is set forth in the following claims.
Claims (10)
1. An advertising task recommendation method, comprising:
acquiring user characteristic information of a target user;
inputting the user characteristic information into a classification model, and obtaining user category information output by the classification model; the classification model is obtained by training based on a user characteristic information data set in a historical time period;
acquiring a current online advertisement task set, and labeling each advertisement task in the advertisement task set based on advertisement task characteristic information;
matching the labeled advertisement task with user category information to determine a recommended advertisement task;
and outputting the recommended advertisement task to the target user.
2. The method according to claim 1, characterized in that: the user characteristic information comprises user attribution characteristic information and user behavior characteristic information.
3. The method of claim 1, wherein the training step of the classification model comprises:
acquiring a full user characteristic information data set in a first historical time period and an incremental user characteristic information data set in a second historical time period;
pre-training the initial classification model by using the full-scale user characteristic information data set to obtain a pre-trained classification model;
and carrying out parameter adjustment processing on the pre-trained classification model by using the incremental user characteristic information data set to obtain a classification model after training.
4. A method according to claim 3, wherein the performing parameter adjustment processing on the pre-trained classification model using the incremental user feature information dataset to obtain a trained classification model comprises:
dividing the incremental user characteristic information data set into a plurality of incremental user characteristic information data subsets;
and carrying out parameter adjustment processing on the pre-trained classification model by utilizing the incremental user characteristic information data subsets in batches according to a preset time interval to obtain the classification model after training.
5. The method according to claim 4, wherein: the classification model is a logistic regression classification model.
6. The method of any of claims 1 to 5, wherein matching the tagged advertising campaign with user category information to determine a recommended advertising campaign comprises:
determining the advertisement task label value range corresponding to the target user according to the mapping relation between the user category and the advertisement task label value;
screening candidate advertisement tasks with advertisement task tag values within the advertisement task tag value range from the current advertisement task set;
and sequencing the candidate advertisement tasks according to the advertisement task tag value, and determining the recommended advertisement tasks.
7. An advertising mission recommendation device, comprising:
the acquisition module is used for acquiring user characteristic information of the target user;
the user category identification module is used for inputting the user characteristic information into the classification model and acquiring the user category information output by the classification model; the classification model is obtained by training based on a user characteristic information data set in a historical time period;
the advertisement task processing module is used for acquiring the advertisement task set on line currently and labeling each advertisement task in the advertisement task set based on the advertisement task characteristic information;
the matching module is used for matching the labeled advertisement task with the user category information so as to determine a recommended advertisement task;
and the recommending module is used for outputting the recommended advertisement task to the target user.
8. The apparatus as recited in claim 7, further comprising:
the model training module is used for acquiring a full user characteristic information data set in a first historical time period and an incremental user characteristic information data set in a second historical time period; pre-training the initial classification model by using the full-scale user characteristic information data set to obtain a pre-trained classification model; and carrying out parameter adjustment processing on the pre-trained classification model by using the incremental user characteristic information data set to obtain a classification model after training.
9. A computer device, the computer device comprising:
one or more processors;
a memory for storing one or more programs;
the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the advertising mission recommendation method as claimed in any one of claims 1 to 6.
10. A computer-readable storage medium, characterized by: the computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the advertising task recommendation method as claimed in any one of claims 1 to 6.
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| CN202311193136.4A CN117151794A (en) | 2023-09-14 | 2023-09-14 | Advertisement task recommendation method and related device |
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| CN117689426A (en) * | 2024-01-31 | 2024-03-12 | 湖南创研科技股份有限公司 | Multi-channel advertisement effect evaluation method and system |
| CN117689426B (en) * | 2024-01-31 | 2024-06-18 | 湖南创研科技股份有限公司 | Multi-channel advertisement effect evaluation method and system |
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