WO2023116138A1 - Procédé de modélisation pour modèle multitâche, procédé de traitement de contenu promotionnel et appareils associés - Google Patents

Procédé de modélisation pour modèle multitâche, procédé de traitement de contenu promotionnel et appareils associés Download PDF

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WO2023116138A1
WO2023116138A1 PCT/CN2022/124765 CN2022124765W WO2023116138A1 WO 2023116138 A1 WO2023116138 A1 WO 2023116138A1 CN 2022124765 W CN2022124765 W CN 2022124765W WO 2023116138 A1 WO2023116138 A1 WO 2023116138A1
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task
tasks
model
promotion
content
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PCT/CN2022/124765
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English (en)
Chinese (zh)
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吴寅初
佘琪
王长虎
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北京有竹居网络技术有限公司
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0241—Advertisements
    • G06Q30/0251—Targeted advertisements
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0241—Advertisements
    • G06Q30/0277—Online advertisement

Definitions

  • the present disclosure belongs to the technical field of artificial intelligence, and specifically relates to a modeling method of a multi-task model, a promotion content processing method, a device, a device, a computer-readable storage medium, and a computer program product.
  • the application scenarios of artificial intelligence technology are becoming more and more extensive.
  • promotional content such as an advertisement
  • the conversion rate of the promotional content can be predicted based on artificial intelligence technology, and then the promotional content can be pushed to users based on the conversion rate.
  • multi-task learning (muti task learning, MTL) is usually used to build a multi-task model.
  • MTL multi-task learning
  • multiple tasks are selected based on artificial subjective experience for multi-task learning, so that the learned features can be shared between different tasks, thereby improving the learning efficiency and generalization ability of the multi-task model.
  • the purpose of the present disclosure is to provide a modeling method of a multi-task model, a promotion content processing method, a device, a device, a computer-readable storage medium, and a computer program product, which can improve the learning efficiency of a multi-task model.
  • the present disclosure provides a modeling method of a multi-task model, including:
  • the initial task set includes: the conversion rate of promotional content, the playing duration of the promotional content, and the presentation type of the promotional content and at least two of the information on promotion objects in the promotion content;
  • a sample of a multi-task model is generated, and the sample of the multi-task model is used for model training to obtain the multi-task model.
  • the present disclosure provides a method for processing promotional content, including:
  • the inference result of the multi-task model is obtained;
  • the multi-task model is generated based on the features corresponding to each task in the related task set. samples, the set of related tasks is obtained based on the mutual information between different tasks, and the different tasks are the tasks in the initial task set constructed by the features used to construct the tasks;
  • the reasoning results include the conversion rate of the promotion content , the playback duration of the promotion content, the presentation type of the promotion content, or the information of the promotion object in the promotion content;
  • the promotion strategy for the promotion content is adjusted.
  • the present disclosure provides a modeling device for a multi-task model, including:
  • An acquisition module configured to acquire features for constructing tasks, and construct an initial task set according to the features for constructing tasks;
  • the initial task set includes: the conversion rate of the promotion content, the playing time of the promotion content, the At least two of the presentation type of the promotion content and the information of the promotion object in the promotion content;
  • a mutual information determination module configured to determine mutual information between different tasks in the initial task set
  • a related task determination module configured to obtain a set of related tasks according to the mutual information between the different tasks, and the mutual information of the tasks included in the set of related tasks satisfies a first preset condition
  • a training module configured to generate samples of a multi-task model according to features corresponding to each task in the set of related tasks, and use the samples of the multi-task model to perform model training to obtain the multi-task model.
  • the present disclosure provides a device for processing promotional content, which is characterized in that it includes:
  • An acquisition module configured to acquire the attributes of the user's behavior on the promotion content
  • a reasoning module configured to obtain a reasoning result of the multi-task model according to the attribute of the user's behavior on the promotional content and the multi-task model; the multi-task model is generated based on the features corresponding to each task in the related task set The sample of the multi-task model is obtained, and the related task set is obtained based on mutual information between different tasks, and the different tasks are tasks in the initial task set constructed by the features used to construct the task; the reasoning results include The conversion rate of the promotion content, the playing duration of the promotion content, the presentation type of the promotion content, or the information of the promotion object in the promotion content;
  • a processing module configured to adjust a promotion strategy for the promotion content according to the reasoning result.
  • the present disclosure provides a computer-readable medium on which a computer program is stored, and when the program is executed by a processing device, the steps of the method described in any one of the first aspect or the second aspect of the present disclosure are implemented.
  • an electronic device including:
  • a processing device configured to execute the computer program in the storage device to implement the steps of the method described in any one of the first aspect or the second aspect of the present disclosure.
  • the present disclosure provides a computer program product including instructions, which, when run on a device, cause the device to execute the method described in any implementation manner of the first aspect or the second aspect above.
  • the present disclosure has the following advantages:
  • the present disclosure provides a modeling method of a multi-task model.
  • the features used for constructing tasks are obtained first, an initial task set is constructed based on the features used for constructing tasks, and then the relationship between different tasks in the initial task set is determined.
  • Mutual information Based on the mutual information, the initial task set is screened to obtain a set of related tasks with strong correlation.
  • samples of the multi-task model are generated to train the multi-task model.
  • tasks in the related task set obtained after screening the initial task set in the present disclosure have a stronger correlation. In this way, during the multi-task learning process, the learning efficiency of the multi-task model can be improved.
  • FIG. 1 is a flow chart of a modeling method for a multi-task model provided by an embodiment of the present disclosure
  • FIG. 2 is a schematic diagram of obtaining a set of related tasks provided by an embodiment of the present disclosure
  • FIG. 3 is a schematic diagram of a multi-task model provided by an embodiment of the present disclosure.
  • FIG. 4 is a flowchart of another modeling method for a multi-task model provided by an embodiment of the present disclosure
  • FIG. 5 is a flow chart of a method for processing promotional content provided by an embodiment of the present disclosure
  • FIG. 6 is a schematic diagram of a modeling device for a multi-task model provided by an embodiment of the present disclosure
  • FIG. 7 is a schematic diagram of an apparatus for processing promotional content provided by an embodiment of the present disclosure.
  • FIG. 8 is a schematic structural diagram of an electronic device provided by an embodiment of the present disclosure.
  • first and second in the embodiments of the present disclosure are used for description purposes only, and cannot be interpreted as indicating or implying relative importance or implicitly indicating the quantity of indicated technical features. Thus, a feature defined as “first” and “second” may explicitly or implicitly include one or more of these features.
  • a multi-task model refers to a model constructed based on multi-task learning.
  • the core of multi-task learning is that multiple tasks are trained in parallel and share the learned features with each other.
  • each content can be abstracted into a task, such as predicting conversion rate, predicting potential users, predicting whether users click on the promotion content, etc.
  • a multi-task model can be constructed based on multi-task learning to predict multiple contents.
  • an embodiment of the present disclosure provides a modeling method for a multi-task model, which can be executed by an electronic device.
  • An electronic device may be a server.
  • the server may be a cloud server, for example, a central server in a central cloud computing cluster, or an edge server in an edge cloud computing cluster.
  • the server may also be a server in a local data center.
  • An on-premises data center refers to a data center directly controlled by the user.
  • the modeling method of the multi-task model includes: the electronic device acquires features for constructing tasks, constructs an initial task set according to the features for constructing tasks, and then determines mutual information between different tasks in the initial task set. Based on the mutual information between different tasks, a set of related tasks is obtained, and the mutual information in the set of related tasks satisfies a first preset condition. According to the characteristics corresponding to each task in the related task set, samples of the multi-task model are generated, and the samples of the multi-task model are used for model training to obtain the multi-task model.
  • the electronic device first filters the initial task set, and the tasks in the obtained related task set have a strong correlation. Compared with multiple tasks selected purely relying on subjective experience, the correlation between multiple tasks corresponding to the multi-task model is improved. In this way, during the multi-task learning process, the learning efficiency of the multi-task model can be improved.
  • the multi-task model obtained by using the multi-task model modeling method provided by the embodiments of the present disclosure can be applied to various scenarios.
  • the multi-task model can be used to predict the conversion rate of the promotional content and the playing duration of the promotional content (the time elapsed from the start of the promotional content until the user closes the promotional content).
  • the electronic device may input the attribute of the user's behavior on the promotion content into the multi-task model, and then obtain the above reasoning result. Then the electronic device can adjust the promotion strategy of the promotion content based on the above reasoning result.
  • the conversion rate of the promotional content is greater than or equal to the conversion rate threshold and the playing time of the promotional content is greater than or equal to the duration threshold, the number of promotions of the promotional content is increased.
  • the conversion rate of the promotional content is less than the conversion rate threshold and the playing time of the promotional content is less than the duration threshold, the number of promotions of the promotional content is reduced.
  • this figure is a flowchart of a modeling method for a multi-task model provided by an embodiment of the present disclosure, the method includes:
  • S101 The electronic device acquires features for constructing tasks, and constructs an initial task set according to the features for constructing tasks.
  • An electronic device may obtain initial data, which includes a plurality of characteristics.
  • the initial data may include features such as the conversion rate of the promotional content, the playing time of the promotional content, the presentation type of the promotional content, the information of the promotional objects in the promotional content, and the generation time of the promotional content.
  • Electronic devices can acquire features for building tasks based on initial data.
  • the electronic device may receive a plurality of features configured by a developer as features for building a task.
  • the electronic device can present multiple candidate features to the user through a display device (such as a display screen), such as the features included in the above-mentioned initial data, and then according to the developer's selection operation on the multiple candidate features, the feature selected by the developer As a feature for building tasks.
  • the electronic device constructs an initial task set based on the features for constructing tasks.
  • S102 The electronic device determines mutual information between different tasks in the initial task set.
  • MI Mutual information
  • the electronic device can separately calculate the mutual information between different tasks in the initial task set.
  • the electronic device can calculate the mutual information between different tasks based on the characteristics of the tasks in the initial task set.
  • the electronic device may calculate the mutual information between different tasks in the initial task set based on the following formula:
  • I(X; Y) is the mutual information between task X and task Y in the initial task set; x is the feature corresponding to task X, y is the feature corresponding to task Y; p(x, y) is the task X and task Y The probability that task Y occurs at the same time, p(x) is the probability of task X occurring, and p(y) is the probability of task Y occurring.
  • S103 The electronic device obtains a related task set according to the mutual information between different tasks, and the mutual information of the tasks included in the related task set satisfies a first preset condition.
  • the electronic device may filter the initial task set based on mutual information between different tasks in the initial task set, and then obtain a related task set.
  • the mutual information of tasks included in the set of related tasks satisfies a first preset condition.
  • the first preset condition may be that the mutual information between different tasks is greater than or equal to a mutual information threshold.
  • the electronic device may receive the mutual information threshold configured by the developer, and determine the related task set based on the configured mutual information threshold, so that the developer can adjust the tasks in the related task set within a certain range. .
  • the mutual information threshold may be a value configured by a developer, or may be a default value.
  • the mutual information threshold may be 0.1, 0.2, and so on.
  • S104 The electronic device generates a sample of the multi-task model according to the features corresponding to each task in the related task set, and uses the samples of the multi-task model to perform model training to obtain the multi-task model.
  • the electronic device After determining the related task set, the electronic device can build a multi-task model based on the tasks in the related task set. Since the mutual information between the tasks in the related task set satisfies the first preset condition, it indicates that the tasks in the related task set are highly correlated. Therefore, after constructing a multi-task model based on tasks with strong correlation, the learning efficiency of the multi-task model is higher.
  • the electronic device can generate samples of the multi-task model based on features corresponding to each task in the related task set.
  • the samples of the multi-task model include feature vectors of the samples and labels of the feature vectors of the samples.
  • the initial data includes multiple features, and the features corresponding to the tasks in the related task set are some of the multiple features of the initial data. Based on this, the electronic device can use the feature corresponding to the task in the related task set as the label of the feature vector of the sample, remove the feature corresponding to the task in the above related task set from the initial data, and use the initial data obtained as the feature vector of the sample.
  • the initial data includes feature 1, feature 2, feature 3, feature 4, and feature 5.
  • task 1 corresponds to feature 1
  • task 2 corresponds to feature 2.
  • the electronic device can use feature 1 as the label of the feature vector of the sample used for monitoring task 1, and feature 2 as the label of the feature vector of the sample used for monitoring task 2, remove feature 1 and feature 2 from the initial data, and obtain the following:
  • the initial data of feature 3, feature 4, and feature 5 are used as feature vectors of samples of the multi-task model.
  • the electronic device can use the samples of the multi-task model to perform model training to obtain the multi-task model.
  • FIG. 3 is a schematic diagram of a multi-task model provided by an embodiment of the present disclosure.
  • the multi-task model includes a task exclusive network 310 , an output network 330 , and a shared network 320 corresponding to multiple tasks in the related task set.
  • the task-exclusive network 310 can be a deep neural network (deep neural networks, DNN), a convolutional neural network (convolutional neural network, CNN) or a self-attention network;
  • the shared network 320 can also be a deep neural network, a volume product neural network or self-attention network.
  • the present disclosure does not specifically limit the types of the task exclusive network 310 and the shared neural network 320 .
  • the shared network 320 and the task-exclusive network 310 can be constructed by using the convolutional neural network.
  • a shared network 320 and a task-exclusive network 310 can be constructed using a deep neural network.
  • the self-attention network can be used to construct the shared network 320 and the task-exclusive network 310 .
  • the output network 330 can use activation functions such as sigmoid and relu to obtain the output corresponding to each task (for example, classification value and regression value).
  • the electronic device may input the feature vector of the sample of the multi-task model into the shared network 320 to obtain the shared component, and then input the shared component to the task-exclusive network of each task in the relevant task set to obtain the eigenvector of each task-exclusive network. output. Then the electronic device can determine the loss value based on the label value of the feature vector of the sample of the multi-task model and the output of the task exclusive network, and update the weight of the task exclusive network based on the loss value to perform model training, and then obtain the multi-task model.
  • the embodiments of the present disclosure provide a modeling method for a multi-task model. Compared with multiple tasks selected purely relying on subjective experience, the related task set obtained after the electronic device screens the initial task set The correlation between tasks is strong. In this way, during the multi-task learning process, the learning efficiency of the multi-task model can be improved. Further, it meets the business needs in various scenarios and realizes more accurate prediction of multiple contents.
  • the embodiment of the present disclosure also provides a modeling method of a multi-task model. Based on the embodiment shown in FIG. The number of positive samples for each task group. As shown in Figure 4, the modeling method of the multi-task model also includes:
  • S401 The electronic device aggregates tasks satisfying a second preset condition in a set of related tasks.
  • the second preset condition may be a plurality of tasks in the set of related tasks whose task correlation is greater than a correlation threshold.
  • the electronic device may aggregate the tasks in the related task set based on the second preset condition, and the aggregated related task set includes multiple task groups.
  • the electronic device can obtain the correlation between the outputs of the task exclusive network 310 corresponding to multiple tasks in the related task set, and then based on the correlation between the outputs of the task exclusive network 310 , the related task set Multiple tasks are aggregated. Specifically, the electronic device may calculate the correlation between the outputs of the task exclusive network 310 corresponding to multiple tasks in the related task set based on the following formula:
  • r mn is the correlation between task m and task n in the related task set;
  • O im is the output of sample i in the task exclusive network 310 corresponding to task m, is the mean value of the output of the task exclusive network 310 corresponding to task m to all samples;
  • O in is the output of sample i in the task exclusive network 310 corresponding to task n, is the mean value of the output of all samples of the task exclusive network 310 corresponding to task n, and K is the total number of samples.
  • the electronic device may compare the correlation r mn between task m and task n to a correlation threshold. When r mn ⁇ the correlation threshold, the task m and the task n are aggregated into one group, so that the electronic device can obtain the aggregated related task set including multiple task groups.
  • the electronic device determines multiple target tasks according to the number of positive samples of tasks in the task group.
  • the aggregated set of related tasks includes multiple task groups. Take multiple task groups including task group G1 and task group G2 as an example, where task group G1 includes task T1, task T2, and task T3, the number of positive samples for task T1 is 100, the number of positive samples for task T2 is 200, and the number of positive samples for task T2 is 200.
  • the number of positive samples of T3 is 300; the task group G2 includes task T4, task T5 and task T6, the number of positive samples of task T4 is 200, the number of positive samples of task T5 is 400, and the number of positive samples of task T6 is 500. It can be seen that the sum of the number of positive samples in the task group G1 is 600, and the sum of the number of positive samples in the task group G2 is 1100.
  • the electronic device may use a sum of 600 positive samples of the task group G1 as the positive sample number threshold.
  • the electronic device can sort task T4, task T5, and task T6 in task group G2, such as adding up one by one in order of the number of positive samples from small to large, until the number of positive samples of tasks in task group G2 The sum is greater than or equal to the above positive sample number threshold 600.
  • the electronic device may determine that when the sum of the number of positive samples of tasks in the task group is greater than or equal to the threshold of the number of positive samples, multiple tasks in the task group are multiple target tasks. In this way, after the electronic device sums the number of positive samples of task T4 and the number of positive samples of task T5, the sum result is equal to the threshold of the number of positive samples, and the electronic device can regard task T4 and task T5 as multiple tasks in the task group. target task.
  • the electronic device may randomly select the number of positive samples of multiple tasks in the task group G2 to add up until the number of positive samples of multiple tasks is greater than or equal to the threshold of the number of positive samples, and then the number of randomly selected tasks Determined as multiple target tasks.
  • the electronic device may sum the number of positive samples of task T5 and task T6 in the task group G2, and the sum result is greater than the threshold of the number of positive samples. In this way, the electronic device can determine the task T5 and the task T6 as multiple target tasks.
  • the sum of the number of positive samples of task T4 and task T5 in task group G2 is greater than or equal to the threshold of the number of positive samples means: when subtracting the number of positive samples of either task T4 or task T5 in task group G2 When , the sum of the number of positive samples of the remaining tasks will be less than the threshold of the number of positive samples.
  • the electronic device may remove tasks other than the target task in the task group. Taking the multiple target tasks in the task group G2 as task T4 and task T5 as an example, the electronic device may remove task T6 from the task group G2.
  • the electronic device may generate samples of the multi-task model based on features corresponding to multiple target tasks in the task group.
  • the target tasks included in task group G2 as task T4 and task T5 as an example for this task group G2, the label of the feature vector of the positive sample corresponding to task T4 is used as the label of the task group G2, and the positive sample corresponding to task T5 is The label of the feature vector of the sample is also used as the label of the task group G2.
  • the electronic device can balance the number of positive samples included in multiple task groups to reduce the reduction in the learning efficiency of the multi-task model due to large differences in the number of positive samples.
  • the modeling method of the multi-task model analyzes the statistical information of large-scale data containing multiple characteristics, determines the set of related tasks based on correlation, and realizes the aggregation of highly related tasks.
  • Using highly relevant tasks for multi-task model training can reduce negative transfer and improve the learning efficiency of multi-task models.
  • the structure of the multi-task model is constructed based on the determined set of related tasks, and the task aggregation with model generalization information is completed by combining the Pearson coefficient.
  • the number of positive samples corresponding to the task group with the smallest number of positive samples is used as the positive sample number threshold to screen and combine the tasks of each group, so as to achieve the balance of positive samples in different tasks.
  • the number of positive samples between different tasks can be completely proportional, so that the multi-task model can complete the multi-task learning process without bias.
  • the embodiment of the present disclosure also provides a promotion content processing method, as shown in FIG. 5 , the promotion content processing method includes:
  • S501 The electronic device acquires an attribute of a user's behavior on promotional content.
  • the attributes of the user's behavior on the promotional content may include the duration of the user's viewing of the promotional content, whether the user clicks on the promotional content, the presentation type of the promotional content clicked by the user (such as video type, picture type, etc.), whether the user is Promote content conversion, etc.
  • the electronic device needs to obtain the user's authorization in advance, and the electronic device can only obtain the user's behavior on the promotional content after obtaining the user's authorization to use the corresponding data (such as the above-mentioned attributes of the user's behavior on the promotional content). attributes and other data.
  • S502 The electronic device obtains an inference result of the multi-task model according to the attribute of the user's behavior on the promotion content and the multi-task model.
  • the multi-task model is obtained based on samples of the multi-task model generated by features corresponding to each task in the related task set.
  • the related task set is obtained based on the mutual information between different tasks, and the different tasks are the tasks in the initial task set constructed by the features used to construct the tasks.
  • the reasoning result includes a conversion rate of the promotion content, a playing time of the promotion content, a presentation type of the promotion content, or information of promotion objects in the promotion content.
  • S503 The electronic device adjusts a promotion strategy for the promotion content according to the reasoning result.
  • the electronic device when the inference result shows that the conversion rate of the promotion content is greater than the conversion rate threshold, and the playing time of the promotion content is greater than the duration threshold, the electronic device increases the number of promotions of the promotion content. When the conversion rate of the promotion content is less than the conversion rate threshold and the playing time of the promotion content is less than the time threshold, the electronic device reduces the number of promotions of the promotion content.
  • the presentation type of the promotion content is a preset type (such as a video type)
  • the information of the promotion object in the promotion content is preset information (such as indicating that the promotion object is a game, a virtual item or a physical object)
  • the electronic The device increases the promotion frequency of the promotion content. In this way, the ineffective delivery of promotional content is reduced, and the waste of resources is reduced.
  • Fig. 6 is a schematic diagram of a multi-task model modeling device according to an exemplary disclosed embodiment. As shown in Fig. 6, the multi-task model modeling device 600 includes:
  • the obtaining module 601 is used to obtain the features used to construct the task, and construct an initial task set according to the features used to construct the task; the initial task set includes: the conversion rate of the promotion content, the playing duration of the promotion content, the At least two of the presentation type of the promotion content and the information of the promotion object in the promotion content;
  • a related task determining module 603, configured to obtain a related task set according to mutual information between the different tasks, and the mutual information of the tasks included in the related task set satisfies a first preset condition
  • the training module 604 is configured to generate a sample of a multi-task model according to features corresponding to each task in the set of related tasks, and use the samples of the multi-task model to perform model training to obtain the multi-task model.
  • the related task determining module 603 is further configured to aggregate tasks satisfying a second preset condition in the set of related tasks.
  • the related task determination module 603 is specifically configured to determine the correlation between the outputs of the task exclusive network corresponding to multiple tasks in the related task set; Multiple tasks are aggregated.
  • the relevant task determination module 603 is further configured to determine multiple target tasks for each task group according to the number of positive samples of tasks in the task group, and one of the number of positive samples of the multiple target tasks is The sum is greater than the threshold of the number of positive samples, and the threshold of the number of positive samples is the minimum value of the sum of the number of positive samples in each task group;
  • the training module 604 is specifically configured to generate a sample of a multi-task model according to the features corresponding to the target task in the task group.
  • the relevant task determination module 603 is specifically configured to add up the number of positive samples of the tasks in the task group from small to large, until the sum of the number of positive samples of the tasks in the task group greater than or equal to the threshold of the number of positive samples; when it is determined that the sum of the number of positive samples of tasks in the task group is greater than or equal to the threshold of the number of positive samples, the multiple tasks in the task group are multiple target tasks.
  • the training module 604 is specifically configured to input the feature vectors of the samples of the multi-task model into the shared network to obtain shared components; input the shared components to the task exclusives of each task in the related task set The network obtains the output corresponding to the task-exclusive network of each task; and trains the multi-task model according to the label value of the feature vector of the sample of the multi-task model and the output of the task-exclusive network.
  • the task exclusive network includes a deep neural network, a convolutional neural network or a self-attention network; the shared network includes a deep neural network, a convolutional neural network or a self-attention network.
  • Fig. 7 is a schematic diagram of a promotional content processing device according to an exemplary disclosed embodiment. As shown in Fig. 7, the promotional content processing device 700 includes:
  • An acquisition module 701, configured to acquire the attribute of the user's behavior on the promotion content
  • the reasoning module 702 is configured to obtain the reasoning result of the multi-task model according to the attribute of the user's behavior on the promotion content and the multi-task model; the multi-task model is generated based on the features corresponding to each task in the related task set The sample of the multi-task model is obtained, the related task set is obtained based on the mutual information between different tasks, and the different tasks are tasks in the initial task set constructed by the features used to construct the task; the reasoning result Including the conversion rate of the promotion content, the playing duration of the promotion content, the presentation type of the promotion content, or the information of the promotion object in the promotion content;
  • the processing module 703 is configured to adjust a promotion strategy for the promotion content according to the reasoning result.
  • FIG. 8 shows a schematic structural diagram of an electronic device 800 suitable for implementing an embodiment of the present disclosure
  • the electronic device is used to realize the functions corresponding to the modeling apparatus 600 of the multi-task model shown in FIG. 6 , Or it is used to implement the functions corresponding to the promotional content processing apparatus 700 shown in FIG. 7 .
  • the electronic device shown in FIG. 8 is only an example, and should not limit the functions and scope of use of the embodiments of the present disclosure.
  • an electronic device 800 may include a processing device (such as a central processing unit, a graphics processing unit, etc.) Various appropriate actions and processes are executed by programs in the memory (RAM) 803 . In the RAM 803, various programs and data necessary for the operation of the electronic device 800 are also stored.
  • the processing device 801, the ROM 802, and the RAM 803 are connected to each other through a bus 804.
  • An input/output (I/O) interface 805 is also connected to the bus 804 .
  • the following devices can be connected to the I/O interface 805: input devices 806 including, for example, a touch screen, touchpad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; including, for example, a liquid crystal display (LCD), speaker, vibration an output device 807 such as a computer; a storage device 808 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 809.
  • the communication means 809 may allow the electronic device 800 to communicate with other devices wirelessly or by wire to exchange data. While FIG. 8 shows electronic device 800 having various means, it is to be understood that implementing or having all of the means shown is not a requirement. More or fewer means may alternatively be implemented or provided.
  • embodiments of the present disclosure include a computer program product, which includes a computer program carried on a non-transitory computer readable medium, where the computer program includes program code for executing the method shown in the flowchart.
  • the computer program may be downloaded and installed from a network via communication means 809, or from storage means 808, or from ROM 802.
  • the processing device 801 the above-mentioned functions defined in the methods of the embodiments of the present disclosure are executed.
  • the computer-readable medium mentioned above in 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 electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to, electrical connections with one or more wires, portable computer diskettes, hard disks, random access memory (RAM), read-only memory (ROM), erasable Programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination of the above.
  • a computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.
  • a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave carrying computer-readable program code therein. Such propagated data signals may take many forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the foregoing.
  • a computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which can transmit, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device .
  • Program code embodied on a computer readable medium may be transmitted by any appropriate medium, including but not limited to wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.
  • the client and the server can communicate using any currently known or future network protocols such as HTTP (HyperText Transfer Protocol, Hypertext Transfer Protocol), and can communicate with digital data in any form or medium
  • HTTP HyperText Transfer Protocol
  • the communication eg, communication network
  • Examples of communication networks include local area networks (“LANs”), wide area networks (“WANs”), internetworks (e.g., the Internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed network of.
  • the above-mentioned computer-readable medium may be included in the above-mentioned electronic device, or may exist independently without being incorporated into the electronic device.
  • the above-mentioned computer-readable medium carries one or more programs, and when the above-mentioned one or more programs are executed by the electronic device, the electronic device:
  • the initial task set includes: the conversion rate of promotional content, the playing duration of the promotional content, and the presentation type of the promotional content and at least two of the information on promotion objects in the promotion content;
  • a related task set is obtained, and the mutual information of the tasks included in the related task set satisfies a first preset condition;
  • the inference result of the multi-task model is obtained;
  • the multi-task model is generated based on the features corresponding to each task in the related task set. samples, the set of related tasks is obtained based on the mutual information between different tasks, and the different tasks are the tasks in the initial task set constructed by the features used to construct the tasks;
  • the reasoning results include the conversion rate of the promotion content , the playback duration of the promotion content, the presentation type of the promotion content, or the information of the promotion object in the promotion content;
  • the promotion strategy for the promotion content is adjusted.
  • Computer program code for carrying out operations of the present disclosure may be written in one or more programming languages, or combinations thereof, including but not limited to object-oriented programming languages—such as Java, Smalltalk, C++, and Includes conventional procedural programming languages - such as "C" 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.
  • the remote computer may be connected to the user computer through any kind of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (for example, using an Internet service provider to connected via the Internet).
  • LAN local area network
  • WAN wide area network
  • Internet service provider for example, using an Internet service provider to connected via the Internet.
  • each block in a flowchart or block diagram may represent a module, program segment, or portion of code that contains one or more logical functions for implementing specified executable instructions.
  • 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 they may sometimes be executed in the reverse order, depending upon the functionality involved.
  • each block of the block diagrams and/or flowchart illustrations, and combinations of blocks in the block diagrams and/or flowchart illustrations can be implemented by a dedicated hardware-based system that performs the specified functions or operations , or may be implemented by a combination of dedicated hardware and computer instructions.
  • the modules involved in the embodiments described in the present disclosure may be implemented by software or by hardware. Wherein, the name of the module does not constitute a limitation of the module itself under certain circumstances, for example, the first obtaining module may also be described as "a module for obtaining at least two Internet Protocol addresses".
  • FPGAs Field Programmable Gate Arrays
  • ASICs Application Specific Integrated Circuits
  • ASSPs Application Specific Standard Products
  • SOCs System on Chips
  • CPLD Complex Programmable Logical device
  • a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device.
  • a machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium.
  • a machine-readable medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing.
  • machine-readable storage media would include one or more wire-based electrical connections, portable computer disks, hard disks, Random Access Memory (RAM), Read Only Memory (ROM), Erasable Programmable Read Only Memory (EPROM or flash memory), optical fiber, compact disk read only memory (CD-ROM), optical storage, magnetic storage, or any suitable combination of the foregoing.
  • RAM Random Access Memory
  • ROM Read Only Memory
  • EPROM Erasable Programmable Read Only Memory
  • CD-ROM compact disk read only memory
  • magnetic storage or any suitable combination of the foregoing.
  • Example 1 provides a modeling method of a multi-task model, acquiring features used to construct tasks, and constructing an initial task set according to the features used to construct tasks;
  • the initial The task set includes: at least two of the conversion rate of the promotion content, the playing duration of the promotion content, the presentation type of the promotion content, and the information of the promotion object in the promotion content; determine the different tasks in the initial task set Mutual information between different tasks; according to the mutual information between different tasks, a related task set is obtained, and the mutual information of the tasks included in the related task set satisfies the first preset condition; according to each task in the related task set Generate a sample of the multi-task model corresponding to the feature, and use the sample of the multi-task model to perform model training to obtain the multi-task model.
  • Example 2 provides the method of Example 1, the method further comprising:
  • Example 3 provides the method of Example 2, the aggregating the tasks satisfying the second preset condition in the set of related tasks includes:
  • Example 4 provides the method of Example 3, the aggregated set of related tasks includes multiple task groups, and the method further includes:
  • a plurality of target tasks are determined according to the number of positive samples of the tasks in the task grouping, the sum of the number of positive samples of the multiple target tasks is greater than the threshold of the number of positive samples, and the threshold of the number of positive samples is The minimum value of the sum of the number of positive samples in each task group;
  • a sample of a multi-task model is generated according to features corresponding to the target task in the task group.
  • Example 5 provides the method of Example 4, wherein determining multiple target tasks according to the number of positive samples of tasks in the task grouping includes:
  • the multiple tasks in the task group are multiple target tasks.
  • Example 6 provides the method of Example 1, and performing model training using samples of the multi-task model includes:
  • Example 7 provides the method of Example 6, the task-exclusive network includes a deep neural network, a convolutional neural network, or a self-attention network; the shared network includes a deep neural network, a volume product neural network or self-attention network.
  • Example 8 provides a method for processing promotional content, including: acquiring the attributes of the user's behavior on the promotional content; model to obtain the inference result of the multi-task model; the multi-task model is obtained based on samples of the multi-task model generated by the features corresponding to each task in the related task set, and the related task set is based on the interaction between different tasks
  • the information is obtained, the different tasks are the tasks in the initial task set constructed by the characteristics of the task;
  • the reasoning results include the conversion rate of the promotion content, the playing time of the promotion content, and the presentation type of the promotion content Or multiple types of information about the promotion object in the promotion content; according to the reasoning result, adjust the promotion strategy for the promotion content.

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Abstract

La présente divulgation se rapporte au domaine technique de l'intelligence artificielle. Sont décrits un procédé de modélisation pour un modèle multitâche, un procédé de traitement de contenu promotionnel et des appareils associés. Le procédé de modélisation pour un modèle multitâche consiste à : tout d'abord, acquérir des caractéristiques pour construire des tâches, et construire un ensemble de tâches initial selon les caractéristiques pour construire des tâches ; puis déterminer des informations mutuelles entre différentes tâches dans l'ensemble de tâches initial, et obtenir, sur la base des informations mutuelles entre les différentes tâches, un ensemble de tâches associées ayant une corrélation relativement élevée, des informations mutuelles entre des tâches comprises dans l'ensemble de tâches associées satisfaisant une première condition prédéfinie ; et ensuite, générer des échantillons d'un modèle multitâche selon des caractéristiques correspondant aux tâches dans l'ensemble de tâches associées, de façon à entraîner le modèle multitâche à l'aide des échantillons du modèle multitâche. De cette manière, des tâches ayant une corrélation relativement élevée peuvent être acquises au moyen du procédé. Par conséquent, le procédé peut améliorer l'efficacité d'apprentissage d'un modèle multitâche pendant un processus d'apprentissage multitâche.
PCT/CN2022/124765 2021-12-21 2022-10-12 Procédé de modélisation pour modèle multitâche, procédé de traitement de contenu promotionnel et appareils associés WO2023116138A1 (fr)

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CN114860411B (zh) * 2022-05-17 2023-05-05 北京百度网讯科技有限公司 多任务学习方法、装置、电子设备和存储介质

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