CN113177174B - Feature construction method, content display method and related device - Google Patents
Feature construction method, content display method and related device Download PDFInfo
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Abstract
The disclosure relates to a feature construction method, a content display method and a related device, which are used for constructing target user features and target content features by combining non-attribution conversion data, improving the data utilization rate and reducing the waste of content display resources. The feature construction method comprises the following steps: acquiring user conversion data corresponding to target content, wherein the user conversion data comprises non-attribution conversion data; classifying the user conversion data to obtain the user conversion data associated with the same user and the user conversion data corresponding to the same content; constructing target user characteristics according to the user conversion data associated with the same user, and constructing target content characteristics according to the user conversion data corresponding to the same content; the target user features and the target content features are used to train a content display model that is used to determine content displayed to a target user.
Description
Technical Field
The disclosure relates to the field of computer technology, and in particular, to a feature construction method, a content display method and related devices.
Background
The attribution conversion data is data for showing the content on the content platform and attributing the user's subscription, downloading, etc. actions to the content platform. The related art generally analyzes user behavior based on attribution transformation data. However, as various content platforms and applications grow, the reasons for the user's subscription, downloading, etc. behavior become complex. If analysis is only carried out through attribution conversion data, the user behavior track cannot be restored well, so that the subsequent display of corresponding content to a user is affected, and the waste of content display resources is caused.
Disclosure of Invention
This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.
In a first aspect, the present disclosure provides a feature construction method, the method comprising:
acquiring user conversion data corresponding to target content, wherein the user conversion data comprises non-attribution conversion data, and the non-attribution conversion data is user data attributing operation behaviors to a second content platform when the user generates the operation behaviors on the target content under the condition that a first content platform displays the target content, and the second content platform displays content related to the target content;
Classifying the user conversion data to obtain the user conversion data associated with the same user and the user conversion data corresponding to the same content;
constructing target user characteristics according to the user conversion data associated with the same user, and constructing target content characteristics according to the user conversion data corresponding to the same content;
the target user features and the target content features are used to train a content display model that is used to determine content displayed to a target user.
In a second aspect, the present disclosure provides a content display method, the method comprising:
acquiring content information of content to be displayed;
inputting the content information of the content to be displayed into a content display model to determine a target user, wherein the content display model is obtained by training the target user characteristics and the target content characteristics constructed according to the method of the first aspect;
and displaying the content to be displayed to the target user.
In a third aspect, the present disclosure provides a feature build apparatus, the apparatus comprising:
the data acquisition module is used for acquiring user conversion data corresponding to target content, wherein the user conversion data comprises non-attribution conversion data, and the non-attribution conversion data is user data attributing operation behaviors to a second content platform when the user generates the operation behaviors on the target content under the condition that a first content platform displays the target content, and the second content platform displays content related to the target content;
The data classification module is used for classifying the user conversion data to obtain the user conversion data associated with the same user and the user conversion data corresponding to the same content;
the feature construction module is used for constructing target user features according to the user conversion data associated with the same user and constructing target content features according to the user conversion data corresponding to the same content;
the target user features and target content features are used to train a content display model that is used to determine content displayed to the target user.
In a fourth aspect, the present disclosure provides a content display apparatus, the apparatus comprising:
the acquisition module is used for acquiring content information of the content to be displayed;
the determining module is used for inputting the content information of the content to be displayed into a content display model to determine a target user, and the content display model is obtained by training the target user characteristics and the target content characteristics constructed according to the method of the first aspect;
and the display module is used for displaying the content to be displayed to the target user.
In a fifth aspect, the present disclosure provides a computer readable medium having stored thereon a computer program which when executed by a processing device implements the steps of the method described in the first or second aspect.
In a sixth aspect, the present disclosure provides an electronic device, comprising:
a storage device having a computer program stored thereon;
processing means for executing said computer program in said storage means to carry out the steps of the method described in the first or second aspect.
Through the technical scheme, the target user characteristics and the target content characteristics can be built by combining the non-attribution transformation data, and the target user characteristics and the target content characteristics are used for training the content display model, so that the content display model is trained through richer data, the result accuracy of the content display model can be improved, the waste of content display resources is reduced, and the data utilization rate of the non-attribution transformation data can be improved.
Additional features and advantages of the present disclosure will be set forth in the detailed description which follows.
Drawings
The above and other features, advantages, and aspects of embodiments of the present disclosure will become more apparent by reference to the following detailed description when taken in conjunction with the accompanying drawings. The same or similar reference numbers will be used throughout the drawings to refer to the same or like elements. It should be understood that the figures are schematic and that elements and components are not necessarily drawn to scale. In the drawings:
FIG. 1 is a flow chart of a feature construction method according to an exemplary embodiment of the present disclosure;
FIG. 2 is a flow chart of a content display method according to an exemplary embodiment of the present disclosure;
FIG. 3 is a block diagram of a feature build apparatus according to an exemplary embodiment of the present disclosure;
FIG. 4 is a block diagram of a content display apparatus according to an exemplary embodiment of the present disclosure;
fig. 5 is a block diagram of an electronic device, according to an exemplary embodiment of the present disclosure.
Detailed Description
Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present disclosure have been shown in the accompanying drawings, it is to be understood that the present disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein, but are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are for illustration purposes only and are not intended to limit the scope of the present disclosure.
It should be understood that the various steps recited in the method embodiments of the present disclosure may be performed in a different order and/or performed in parallel. Furthermore, method embodiments may include additional steps and/or omit performing the illustrated steps. The scope of the present disclosure is not limited in this respect.
The term "including" and variations thereof as used herein are intended to be open-ended, i.e., including, but not limited to. The term "based on" is based at least in part on. The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments. Related definitions of other terms will be given in the description below.
It should be noted that the terms "first," "second," and the like in this disclosure are merely used to distinguish between different devices, modules, or units and are not used to define an order or interdependence of functions performed by the devices, modules, or units. It is further noted that references to "one" or "a plurality" in this disclosure are intended to be illustrative rather than limiting, and those of ordinary skill in the art will appreciate that "one or more" is intended to be understood as "one or more" unless the context clearly indicates otherwise.
The names of messages or information interacted between the various devices in the embodiments of the present disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.
The attribution conversion data is data for showing the content on the content platform and attributing the purchasing, subscribing, downloading and other actions generated by the user to the content platform. The non-attribution conversion data is data for showing the content on the content platform and attributing the user's subscription, downloading and other actions to other content platforms. The related art generally analyzes user behavior based on attribution transformation data. However, as various content platforms and applications grow, the reasons for the user's subscription, downloading, etc. behavior become complex. If analysis is only carried out through attribution conversion data, the user behavior track cannot be restored well, so that the subsequent display of corresponding content to a user is affected, and the waste of content display resources is caused.
The inventor finds that compared with attribution conversion data, non-attribution conversion data has obvious improvement in the aspects of user activation behavior, user downloading behavior and the like through a large amount of data analysis.
Therefore, the present disclosure provides a feature construction method to construct a target user feature and a target content feature in combination with non-attribution transformation data, and use the target user feature and the target content feature to train a content display model, so as to train the content display model through richer data, thereby not only improving the accuracy of the result of the content display model, reducing the waste of content display resources, but also improving the data utilization rate of the non-attribution transformation data, and further improving the user transformation rate in the advertisement content scene. User conversion may be understood, among other things, as the conversion of a user clicking on an advertisement to becoming an active user or a registered user.
It should be understood at first that, in the present disclosure, the user may obtain the conversion data by displaying an authorization interface for data obtaining to the user, for example, displaying a prompt box for asking the user whether to agree to upload the user data corresponding to the user. After the user performs data acquisition authorization on the authorization interface, that is, after the user agrees to acquire the user data corresponding to the user, the user conversion data corresponding to the user can be acquired to perform feature construction. That is, the user conversion data in the present disclosure is acquired with the consent of the user authorization.
Fig. 1 is a flow chart illustrating a feature construction method according to an exemplary embodiment of the present disclosure. Referring to fig. 1, the feature construction method includes:
step 101, user conversion data corresponding to target content is obtained, wherein the user conversion data comprises non-attribution conversion data. The non-attribution conversion data is user data attributing an operation behavior to a second content platform which displays content related to the target content when the user generates the operation behavior to the target content in a case where the first content platform displays the target content.
Step 102, classifying the user conversion data to obtain user conversion data associated with the same user and user conversion data corresponding to the same content.
And 103, constructing target user characteristics according to user conversion data associated with the same user, and constructing target content characteristics according to user conversion data corresponding to the same content, wherein the target user characteristics and the target content characteristics are used for training a content display model, and the content display model is used for determining content displayed to the target user.
As already explained above, the user conversion data of the present disclosure is acquired with the consent of the user authorization. Thus, in a possible manner, an authorization interface for data acquisition may be displayed prior to step 101, the authorization interface being used to prompt the user whether to allow acquisition of the user data corresponding to the user. Accordingly, step 101 may be: and responding to the authorization operation of the user in the authorization interface, and acquiring user conversion data corresponding to the target content, wherein the authorization operation is triggered by the user and allows to acquire user data corresponding to the user.
For example, a prompt box is displayed to the user asking the user if he agrees with the content platform to obtain his own corresponding user data, which may include controls displayed as "agree" and "disagree". If the user selects the control displayed as 'consent' through clicking, long pressing and other operations, the user is informed to consent the content platform to acquire the user data corresponding to the user, and therefore the content platform can acquire the user conversion data corresponding to the user to perform feature construction.
By way of example, the user transformation data may be user data characterizing the operational behavior of subscription, download, etc. In a possible manner, user transformation data of different time lengths may be acquired for subsequent feature construction, for example, user transformation data of the past 1 day, 7 days, and 30 days may be acquired for subsequent feature construction, respectively.
In embodiments of the present disclosure, the user conversion data may include non-attribution conversion data. The non-attribution conversion data is user data attribution to other content platforms, so that the content platform is difficult to acquire the non-attribution conversion data, and the non-attribution conversion data cannot be directly acquired from the content platform. For example, non-attribution data corresponding to the target content may be obtained from a third party data platform for collecting non-attribution conversion data corresponding to the target content under user authorization.
It should be appreciated that the non-attribution transformation data obtained from the third party data platform is relatively cluttered, and for ease of subsequent feature construction, the user transformation data may be first categorized to obtain user transformation data associated with the same user and user transformation data corresponding to the same content. For example, the user conversion data associated with the same user may be determined through user equipment information included in the user conversion data, and the user conversion data corresponding to the same content may be determined through content identification information included in the user conversion data.
In a possible manner, the user conversion data may further include attribution conversion data, where the attribution conversion data is user data attributing operation behavior to the first content platform when the user generates operation behavior to the target content in the case where the first content platform presents the target content, and the attribution conversion data associated with the same user may be associated with non-attribution conversion data to obtain target user conversion data, and then the target user conversion data may be classified to obtain user conversion data associated with the same user and user conversion data corresponding to the same content.
For example, attribution transformation data corresponding to the target content may be obtained from the first content platform under user authorization, and then features may be co-constructed in conjunction with non-attribution transformation data corresponding to the target content. The above has already explained that the non-attribution transformation data obtained from the third data platform is disordered, so that in order to facilitate the subsequent feature construction, the non-attribution transformation data corresponding to the same user can be associated with attribution transformation data first, so as to classify the non-attribution transformation data and attribution transformation data corresponding to the same user, and obtain target user transformation data.
In a possible manner, the user equipment information used by the same user is considered to be the same, so that the non-attribution conversion data corresponding to the same user can be associated with attribution conversion data through the user equipment information to obtain target user conversion data. Of course, in other possible manners, the non-attribution conversion data corresponding to the same user may be associated with attribution conversion data through other user information to obtain target user conversion data, which is not limited by the embodiments of the present disclosure.
After the association processing, the non-attribution conversion data and attribution conversion data corresponding to the same user can be associated, but a plurality of data corresponding to the same user can exist in the associated data, so that in order to facilitate subsequent feature construction, the target user conversion data can be further classified to obtain user conversion data associated with the same user and user conversion data corresponding to the same content. For example, the target user conversion data may be classified by the user equipment information to obtain user conversion data associated with the same user, and the target user conversion data may be classified by the content identification information to obtain user conversion data corresponding to the same content.
Therefore, under the condition of user authorization, the subsequent feature construction can be performed by combining the attribution transformation data and the non-attribution transformation data, and compared with a mode of performing feature construction only through attribution transformation data, richer features can be obtained, so that the result accuracy of the content display model is improved through training the content display model through richer data, the waste of content display resources is reduced, and the user transformation rate can be improved under an advertising scene.
After obtaining the user conversion data associated with the same user and the user conversion data corresponding to the same content, the target user characteristic can be constructed according to the user conversion data associated with the same user, and the target content characteristic can be constructed according to the user conversion data corresponding to the same content.
In a possible manner, the target user feature is constructed according to the user conversion data associated with the same user, and may be: constructing at least one of the following target user features according to non-attribution conversion data associated with the same user: list class user features, numeric class user features, and recency features.
For example, list class user features may be used to characterize content platform features or content features that the same user accessed prior to generating an operational behavior, numeric class user features may be used to characterize the number features of operational behaviors generated by the same user, and recency features may be used to characterize the time interval features between the time of generation of the last operational behavior and the current time.
By way of example, table 2 shows possible target user characteristics constructed from non-attribution transformation data associated with the same user, including list class user characteristics, numeric class user characteristics, and recency characteristics.
TABLE 2
It should be understood that the target user characteristics shown in table 2 are merely illustrative, and that more target user characteristics may be constructed according to actual needs in the practice of the present disclosure.
For example, in the case of user authorization, if the user purchases item A, by building list-like user features on non-attribution transformation data, it can be determined which content platforms the user accessed before purchasing item A, or which item information was browsed, i.e., which content the user is interested in. By constructing numeric class user features on the non-attribution transformation data, the number of items A purchased by the user can be determined, i.e., the degree of interest of the user in the items A can be determined. By constructing the recency characteristic for the non-attribution transformation data, the time interval between the time the user purchased item A this time and the time the user purchased item A last time can be determined, i.e., which content the user has recently been interested in can be determined. Therefore, under the condition of user authorization, the behavior track of the user purchasing the article A can be better restored by constructing different types of user features, so that corresponding content can be accurately displayed to the user later, and the waste of content display resources is reduced.
In a possible manner, according to user conversion data corresponding to the same content, the construction target content features may be: according to non-attribution conversion data corresponding to the same content, the following target content characteristics are constructed: list class content features and/or numeric class content features. The list content features are used for representing user features for generating business operation behaviors aiming at the same content, and the numerical value content features are used for representing quantity features of operation behaviors corresponding to the same content.
By way of example, table 3 shows possible target content features, including list-class content features and numeric-class content features, constructed from non-attribution transformation data corresponding to the same content.
TABLE 3 Table 3
It should be understood that the target content features shown in table 3 are merely illustrative, and that more target content features may be constructed according to actual needs in the practice of the present disclosure.
By means of the method, under the condition of user authorization, the target content characteristics can be built according to the content side data in the non-attribution transformation data, so that different types of content characteristics are built, the behavior track of the user for the target content is better restored, corresponding content can be displayed to the user more accurately, and waste of content display resources is reduced.
In a possible manner, the building of the target user feature from the user conversion data associated with the same user may also be: and determining a plurality of content data of which the same user generates operation behaviors according to the non-attribution conversion data associated with the same user, and determining the co-occurrence content characteristics corresponding to the user according to the plurality of content data so as to obtain target user characteristics. Likewise, according to the user conversion data corresponding to the same content, the construction target content features may also be: and determining a plurality of user identification data which generate operation behaviors for the same content according to the non-attribution conversion data corresponding to the same content, and determining co-occurrence user characteristics corresponding to the content according to the plurality of user identification data so as to obtain target content characteristics.
For example, under the condition of user authorization, after the click action of a certain user on the advertisement 1 and the advertisement 2 on the content platform is obtained, the co-occurrence content characteristics corresponding to the user can be determined according to the content data corresponding to the advertisement 1 and the advertisement 2, for example, the content information characteristics are firstly extracted from the content data corresponding to the advertisement 1 and the advertisement 2, then the sum of the content information characteristics of the advertisement 1 and the advertisement 2 is taken as the co-occurrence content characteristics, or the same content information characteristics of the advertisement 1 and the advertisement 2 are taken as the co-occurrence content characteristics, and the like, so as to obtain the target user characteristics corresponding to the user. Or under the condition of user authorization, the first user and the second user are obtained to click the advertisement 3, and the co-occurrence user characteristics corresponding to the advertisement 3 can be determined according to the user identification data of the first user and the second user, for example, the user information characteristics are firstly extracted from the user identification data of the first user and the second user, then the sum of the user information characteristics of the first user and the second user is taken as the co-occurrence user characteristics, or the same user information characteristics of the first user and the second user are taken as the co-occurrence user characteristics, and the like, so that the target content characteristics corresponding to the advertisement 3 are obtained.
By means of the method, under the condition of user authorization, the co-occurrence feature can be built according to the plurality of content data of which the same user generates operation behaviors and the plurality of user identification data associated with the same content in the non-attribution conversion data, so that the feature for training the content display model is built from the dimension of multiple users or multiple contents, the accuracy of the result of the content display model is improved, corresponding content is displayed to the user more accurately, the waste of content display resources is reduced, the data utilization rate of the non-attribution conversion data is further improved, and the user conversion rate can be improved under the advertising content scene.
Based on the same inventive concept, the present disclosure further provides a content display method, including:
step 201, obtaining content information of content to be displayed;
step 202, inputting content information of content to be displayed into a content display model to determine a target user, wherein the content display model is obtained by training target user characteristics and target content characteristics constructed according to any characteristic construction method provided by the disclosure;
and 203, displaying the content to be displayed to the target user.
For example, the content information is used to represent basic content such as text and pictures of a content page, and in the case of determining the target content, the content information of the target content may be acquired, and then the content information is input into a pre-trained content display model to obtain a target user to display the target content.
It should be understood that, in the related art, after the content display model parameters are initialized, the content information features of the content in the attribution transformation data are generally input into the content display model to be estimated, so as to obtain an estimated user, and then the estimated user is compared with the user associated with the content in the attribution transformation data to calculate the loss function. And then back-propagating according to the calculation result of the loss function to update the model parameters. And repeatedly executing the processes of inputting the content information characteristics in the attribution conversion data into the model for estimation to obtain an estimated user, comparing the estimated user with the user associated with the content in the attribution conversion data to calculate a loss function, and carrying out back propagation according to the calculation result of the loss function so as to update the model parameters until the loss function is not obviously reduced. And then, in the model application stage, the content information of the target content can be input into the model to obtain the target user of the target content to be displayed.
However, the foregoing has already demonstrated that the analysis of the attribution transformation data in this way cannot better restore the user behavior track, so as to affect the accuracy of the finally determined target user, and cannot better realize the push display of the target content, resulting in waste of content display resources.
Therefore, the present disclosure proposes a new content display manner, in which, under the condition of user authorization, a target user feature and a target content feature can be built in combination with non-attribution conversion data, and the target user feature and the target content feature are used for training a content display model, so that the content display model is trained through richer data, the accuracy of the result of the content display model can be improved, the waste of content display resources can be reduced, the data utilization rate of the non-attribution conversion data can be improved, and the user conversion rate can be improved in the advertisement content scene. Wherein, the relevant content of the target user feature and the target content feature is described above, and is not repeated here. Specifically, according to the test, compared with the content display model trained only by attribution conversion data, AUC (area under the curve) of the content display model trained by combining non-attribution conversion data in the embodiment of the disclosure can be improved by 0.2%, and advertisements can be more accurately pushed to users in advertisement content scenes, so that the conversion rate of the users is improved.
Based on the same inventive concept, the present disclosure also provides a feature construction apparatus, which may be part or all of an electronic device through software, hardware, or a combination of both. Referring to fig. 3, the feature constructing apparatus 300 includes:
A data acquisition module 301, configured to acquire user conversion data corresponding to a target content, where the user conversion data includes non-attribution conversion data, where the non-attribution conversion data is user data that, when a user generates an operation behavior on the target content in a case where a first content platform displays the target content, attribution the operation behavior to a second content platform, where the second content platform displays content related to the target content;
the data classification module 302 is configured to classify the user conversion data to obtain the user conversion data associated with the same user and the user conversion data corresponding to the same content;
the feature construction module 303 is configured to construct a target user feature according to the user conversion data associated with the same user, and construct a target content feature according to the user conversion data corresponding to the same content;
the target user features and target content features are used to train a content display model that is used to determine content displayed to the target user.
Optionally, the apparatus 300 further includes:
the display module is used for displaying an authorization interface for data acquisition before acquiring user conversion data corresponding to target content, wherein the authorization interface is used for prompting a user whether to allow acquisition of user data corresponding to the user;
The data acquisition module is used for responding to the authorization operation of the user in the authorization interface to acquire the user conversion data corresponding to the target content, wherein the authorization operation is triggered by the user and allows to acquire the user data corresponding to the user.
Optionally, the feature construction module 303 is configured to:
constructing at least one of the following target user features according to the non-attribution transformation data associated with the same user: list class user features, numeric class user features, and recency features.
Optionally, the feature construction module 303 is configured to:
according to the non-attribution conversion data corresponding to the same content, the following target content characteristics are constructed: list content characteristics and/or numerical content characteristics, wherein the list content characteristics are used for representing user characteristics of business operation behaviors generated aiming at the same content, and the numerical content characteristics are used for representing quantity characteristics of the operation behaviors corresponding to the same content.
Optionally, the feature construction module 303 is configured to:
according to the non-attribution conversion data associated with the same user, determining a plurality of content data of the same user, which have generated the operation behaviors, and according to the plurality of content data, determining co-occurrence content characteristics corresponding to the user so as to obtain the target user characteristics;
Constructing target content characteristics according to the user conversion data corresponding to the same content, including:
and determining a plurality of user identification data which generate the operation behaviors for the same content according to the non-attribution conversion data corresponding to the same content, and determining co-occurrence user characteristics corresponding to the content according to the plurality of user identification data so as to obtain the target content characteristics.
Optionally, the user conversion data further includes attribution conversion data, the attribution conversion data being user data attributing an operation behavior to the first content platform when the user generates the operation behavior to the target content in a case where the first content platform is presented with the target content, the apparatus 300 further includes:
the association module is used for associating the attribution conversion data corresponding to the same user with the non-attribution conversion data so as to obtain target user conversion data;
the data classification module 302 is configured to:
and classifying the target user conversion data to obtain the user conversion data associated with the same user and the user conversion data corresponding to the same content.
Based on the same inventive concept, the present disclosure also provides a content display apparatus that may be part or all of an electronic device by means of software, hardware, or a combination of both. Referring to fig. 4, the content display apparatus 400 includes:
An acquisition module 401, configured to acquire content information of a content to be displayed;
a determining module 402, configured to input content information of the content to be displayed into a content display model to determine a target user, where the content display model is obtained by training a target user feature and a target content feature constructed according to the method of the first aspect;
and the display module 403 is configured to display the content to be displayed to the target user.
It should be understood that the electronic device may comprise, in a possible manner, the feature construction means as shown in fig. 3 and the content display means as shown in fig. 4. The target user characteristics and the target content characteristics can be constructed through the characteristic construction device and are used for training a content display model in the content display device.
The specific manner in which the various modules perform the operations in the apparatus of the above embodiments have been described in detail in connection with the embodiments of the method, and will not be described in detail herein.
Based on the same inventive concept, the present disclosure also provides a computer readable medium having stored thereon a computer program which, when executed by a processing device, implements the steps of any one of the above-described feature construction methods or any one of the above-described content display methods.
Based on the same inventive concept, the present disclosure also provides an electronic device, including:
a storage device having a computer program stored thereon;
and the processing device is used for executing the computer program in the storage device to realize the steps of any one of the feature construction methods or any one of the content display methods.
Referring now to fig. 5, a schematic diagram of an electronic device 500 suitable for use in implementing embodiments of the present disclosure is shown. The terminal devices in the embodiments of the present disclosure may include, but are not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and the like, and stationary terminals such as digital TVs, desktop computers, and the like. The electronic device shown in fig. 5 is merely an example and should not be construed to limit the functionality and scope of use of the disclosed embodiments.
As shown in fig. 5, the electronic device 500 may include a processing means (e.g., a central processing unit, a graphics processor, etc.) 501, which may perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 502 or a program loaded from a storage means 508 into a Random Access Memory (RAM) 503. In the RAM 503, various programs and data required for the operation of the electronic apparatus 500 are also stored. The processing device 501, the ROM 502, and the RAM 503 are connected to each other via a bus 504. An input/output (I/O) interface 505 is also connected to bus 504.
In general, the following devices may be connected to the I/O interface 505: input devices 506 including, for example, a touch screen, touchpad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; an output device 507 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; storage 508 including, for example, magnetic tape, hard disk, etc.; and communication means 509. The communication means 509 may allow the electronic device 500 to communicate with other devices wirelessly or by wire to exchange data. While fig. 5 shows an electronic device 500 having various means, it is to be understood that not all of the illustrated means are required to be implemented or provided. More or fewer devices may be implemented or provided instead.
In particular, according to embodiments of the present disclosure, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a non-transitory computer readable medium, the computer program comprising program code for performing the method shown in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication means 509, or from the storage means 508, or from the ROM 502. The above-described functions defined in the methods of the embodiments of the present disclosure are performed when the computer program is executed by the processing device 501.
It should be noted that the computer readable medium described in the present disclosure may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but 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 of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this disclosure, a computer-readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present disclosure, however, the computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with the computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, fiber optic cables, RF (radio frequency), and the like, or any suitable combination of the foregoing.
In some implementations, communications may be made using any currently known or future developed network protocol, such as HTTP (HyperText Transfer Protocol ), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), the internet (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 networks.
The computer readable medium may be contained in the electronic device; or may exist alone without being incorporated into the electronic device.
The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: acquiring user conversion data corresponding to target content, wherein the user conversion data comprises non-attribution conversion data, and the non-attribution conversion data is user data attributing operation behaviors to a second content platform when the user generates the operation behaviors on the target content under the condition that a first content platform displays the target content, and the second content platform displays content related to the target content; classifying the user conversion data to obtain the user conversion data associated with the same user and the user conversion data corresponding to the same content; constructing target user characteristics according to the user conversion data associated with the same user, and constructing target content characteristics according to the user conversion data corresponding to the same content; the target user features and the target content features are used to train a content display model that is used to determine content displayed to a target user.
Computer program code for carrying out operations of the present disclosure may be written in one or more programming languages, including, but not limited to, an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's 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, through the Internet using an Internet service provider).
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The modules described in the embodiments of the present disclosure may be implemented in software or hardware. The name of a module does not in some cases define the module itself.
The functions described above herein may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: a Field Programmable Gate Array (FPGA), an Application Specific Integrated Circuit (ASIC), an Application Specific Standard Product (ASSP), a system on a chip (SOC), a Complex Programmable Logic Device (CPLD), and the like.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on 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 accordance with one or more embodiments of the present disclosure, example 1 provides a feature construction method, the method comprising:
acquiring user conversion data corresponding to target content, wherein the user conversion data comprises non-attribution conversion data, and the non-attribution conversion data is user data attributing operation behaviors to a second content platform when the user generates the operation behaviors on the target content under the condition that a first content platform displays the target content, and the second content platform displays content related to the target content;
classifying the user conversion data to obtain the user conversion data associated with the same user and the user conversion data corresponding to the same content;
constructing target user characteristics according to the user conversion data associated with the same user, and constructing target content characteristics according to the user conversion data corresponding to the same content;
the target user features and the target content features are used to train a content display model that is used to determine content displayed to a target user.
In accordance with one or more embodiments of the present disclosure, example 2 provides the method of example 1, further comprising, prior to obtaining the user conversion data corresponding to the target content:
Displaying an authorization interface for data acquisition, wherein the authorization interface is used for prompting whether a user is allowed to acquire user data corresponding to the user;
the obtaining the user conversion data corresponding to the target content includes:
and responding to the authorization operation of the user in the authorization interface, and acquiring user conversion data corresponding to the target content, wherein the authorization operation is triggered by the user and allows to acquire user data corresponding to the user.
According to one or more embodiments of the present disclosure, example 3 provides the method of example 1, constructing target user features from the user conversion data associated with the same user, comprising:
constructing at least one of the following target user features according to the non-attribution transformation data associated with the same user: list class user features, numeric class user features, and recency features.
According to one or more embodiments of the present disclosure, example 4 provides the method of any one of examples 1 to 3, constructing a target content feature from the user conversion data corresponding to the same content, including:
according to the non-attribution conversion data corresponding to the same content, the following target content characteristics are constructed: list content characteristics and/or numerical content characteristics, wherein the list content characteristics are used for representing user characteristics of business operation behaviors generated aiming at the same content, and the numerical content characteristics are used for representing quantity characteristics of the operation behaviors corresponding to the same content.
According to one or more embodiments of the present disclosure, example 5 provides the method of any one of examples 1-3, constructing target user features from the user conversion data associated with the same user, comprising:
according to the non-attribution conversion data associated with the same user, determining a plurality of content data of the same user, which have generated the operation behaviors, and according to the plurality of content data, determining co-occurrence content characteristics corresponding to the user so as to obtain the target user characteristics;
constructing target content characteristics according to the user conversion data corresponding to the same content, including:
and determining a plurality of user identification data which generate the operation behaviors for the same content according to the non-attribution conversion data corresponding to the same content, and determining co-occurrence user characteristics corresponding to the content according to the plurality of user identification data so as to obtain the target content characteristics.
According to one or more embodiments of the present disclosure, example 6 provides the method of any one of examples 1-3, the user conversion data further including attribution conversion data, the attribution conversion data being user data attributing an operational behavior to the first content platform when the user produces the operational behavior to the target content if the first content platform is exposed to the target content, the method further comprising:
Associating the attribution conversion data corresponding to the same user with the non-attribution conversion data to obtain target user conversion data;
classifying the user conversion data to obtain the user conversion data associated with the same user and the user conversion data corresponding to the same content, including:
and classifying the target user conversion data to obtain the user conversion data associated with the same user and the user conversion data corresponding to the same content.
Example 7 provides a content display method according to one or more embodiments of the present disclosure, the method comprising:
acquiring content information of content to be displayed;
inputting content information of the content to be displayed into a content display model to determine a target user, wherein the content display model is obtained through training of target user characteristics and target content characteristics constructed according to the method described in the example 1;
and displaying the content to be displayed to the target user.
According to one or more embodiments of the present disclosure, example 8 provides a feature build apparatus, the apparatus comprising:
the data acquisition module is used for acquiring user conversion data corresponding to target content, wherein the user conversion data comprises non-attribution conversion data, and the non-attribution conversion data is user data attributing operation behaviors to a second content platform when the user generates the operation behaviors on the target content under the condition that a first content platform displays the target content, and the second content platform displays content related to the target content;
The data classification module is used for classifying the user conversion data to obtain the user conversion data associated with the same user and the user conversion data corresponding to the same content;
the feature construction module is used for constructing target user features according to the user conversion data associated with the same user and constructing target content features according to the user conversion data corresponding to the same content;
the target user features and target content features are used to train a content display model that is used to determine content displayed to the target user.
Example 9 provides the apparatus of example 8, according to one or more embodiments of the disclosure, further comprising:
the display module is used for displaying an authorization interface for data acquisition before acquiring user conversion data corresponding to target content, wherein the authorization interface is used for prompting a user whether to allow acquisition of user data corresponding to the user;
the data acquisition module is used for responding to the authorization operation of the user in the authorization interface to acquire the user conversion data corresponding to the target content, wherein the authorization operation is triggered by the user and allows to acquire the user data corresponding to the user.
In accordance with one or more embodiments of the present disclosure, example 10 provides the apparatus of example 8, the feature construction module to:
constructing at least one of the following target user features according to the non-attribution transformation data associated with the same user: list class user features, numeric class user features, and recency features.
According to one or more embodiments of the present disclosure, example 11 provides the apparatus of any one of examples 8-10, the feature construction module to:
according to the non-attribution conversion data corresponding to the same content, the following target content characteristics are constructed: list content characteristics and/or numerical content characteristics, wherein the list content characteristics are used for representing user characteristics of business operation behaviors generated aiming at the same content, and the numerical content characteristics are used for representing quantity characteristics of the operation behaviors corresponding to the same content.
According to one or more embodiments of the present disclosure, example 12 provides the apparatus of any one of examples 8-10, the feature build module to:
according to the non-attribution conversion data associated with the same user, determining a plurality of content data of the same user, which have generated the operation behaviors, and according to the plurality of content data, determining co-occurrence content characteristics corresponding to the user so as to obtain the target user characteristics;
Constructing target content characteristics according to the user conversion data corresponding to the same content, including:
and determining a plurality of user identification data which generate the operation behaviors for the same content according to the non-attribution conversion data corresponding to the same content, and determining co-occurrence user characteristics corresponding to the content according to the plurality of user identification data so as to obtain the target content characteristics.
According to one or more embodiments of the present disclosure, example 13 provides the apparatus of any one of examples 8-10, the user conversion data further including attribution conversion data, the attribution conversion data being user data attributing an operational behavior to the first content platform when the user generates the operational behavior to the target content if the first content platform is exposed to the target content, the apparatus further comprising:
the association module is used for associating the attribution conversion data corresponding to the same user with the non-attribution conversion data so as to obtain target user conversion data;
the data classification module is used for:
and classifying the target user conversion data to obtain the user conversion data associated with the same user and the user conversion data corresponding to the same content.
Example 14 provides a content display apparatus according to one or more embodiments of the present disclosure, the apparatus comprising:
the acquisition module is used for acquiring content information of the content to be displayed;
a determining module, configured to input content information of the content to be displayed into a content display model to determine a target user, where the content display model is obtained by training a target user feature and a target content feature constructed according to the method described in example 1;
and the display module is used for displaying the content to be displayed to the target user.
According to one or more embodiments of the present disclosure, example 15 provides a computer-readable medium having stored thereon a computer program which, when executed by a processing device, implements the steps of the method of any of examples 1-7.
Example 16 provides an electronic device according to one or more embodiments of the present disclosure, comprising:
a storage device having a computer program stored thereon;
processing means for executing the computer program in the storage means to implement the steps of the method of any one of examples 1-7.
The foregoing description is only of the preferred embodiments of the present disclosure and description of the principles of the technology being employed. It will be appreciated by persons skilled in the art that the scope of the disclosure referred to in this disclosure is not limited to the specific combinations of features described above, but also covers other embodiments which may be formed by any combination of features described above or equivalents thereof without departing from the spirit of the disclosure. Such as those described above, are mutually substituted with the technical features having similar functions disclosed in the present disclosure (but not limited thereto).
Moreover, although operations are depicted in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order. In certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while several specific implementation details are included in the above discussion, these should not be construed as limiting the scope of the present disclosure. Certain features that are described in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable subcombination.
Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are example forms of implementing the claims. The specific manner in which the various modules perform the operations in the apparatus of the above embodiments have been described in detail in connection with the embodiments of the method, and will not be described in detail herein.
Claims (10)
1. A method of feature construction, the method comprising:
acquiring user conversion data corresponding to target content, wherein the user conversion data comprises non-attribution conversion data, and the non-attribution conversion data is user data attributing operation behaviors to a second content platform when the user generates the operation behaviors on the target content under the condition that a first content platform displays the target content, and the second content platform displays content related to the target content;
classifying the user conversion data to obtain the user conversion data associated with the same user and the user conversion data corresponding to the same content;
constructing a target user characteristic according to the user conversion data associated with the same user, and constructing a target content characteristic according to the user conversion data corresponding to the same content, wherein the target user characteristic comprises at least one of a list user characteristic, a numerical user characteristic and a recency characteristic, the list user characteristic is used for representing content platform characteristics or content characteristics accessed before generating operation behaviors, the numerical user characteristic is used for representing the quantity characteristics of the generated operation behaviors, and the recency characteristic is used for representing the time interval characteristic between the generation time and the current time of the latest operation behaviors;
The target user features and the target content features are used to train a content display model that is used to determine content displayed to a target user.
2. The method of claim 1, wherein prior to obtaining user conversion data corresponding to the target content, the method further comprises:
displaying an authorization interface for data acquisition, wherein the authorization interface is used for prompting whether a user is allowed to acquire user data corresponding to the user;
the obtaining the user conversion data corresponding to the target content includes:
and responding to the authorization operation of the user in the authorization interface, and acquiring user conversion data corresponding to the target content, wherein the authorization operation is triggered by the user and allows to acquire user data corresponding to the user.
3. The method according to claim 1 or 2, wherein constructing a target content feature from the user conversion data corresponding to the same content comprises:
according to the non-attribution conversion data corresponding to the same content, the following target content characteristics are constructed: list content characteristics and/or numerical content characteristics, wherein the list content characteristics are used for representing user characteristics of business operation behaviors generated aiming at the same content, and the numerical content characteristics are used for representing quantity characteristics of the operation behaviors corresponding to the same content.
4. The method according to claim 1 or 2, wherein constructing target user features from the user conversion data associated with the same user comprises:
according to the non-attribution conversion data associated with the same user, determining a plurality of content data of the same user, which have generated the operation behaviors, and according to the plurality of content data, determining co-occurrence content characteristics corresponding to the user so as to obtain the target user characteristics;
constructing target content characteristics according to the user conversion data corresponding to the same content, including:
and determining a plurality of user identification data which generate the operation behaviors for the same content according to the non-attribution conversion data corresponding to the same content, and determining co-occurrence user characteristics corresponding to the content according to the plurality of user identification data so as to obtain the target content characteristics.
5. The method according to claim 1 or 2, wherein the user conversion data further includes attribution conversion data, which is user data attributing an operational behavior to the first content platform when the user generates the operational behavior to the target content in a case where the first content platform exhibits the target content, the method further comprising:
Associating the attribution conversion data corresponding to the same user with the non-attribution conversion data to obtain target user conversion data;
classifying the user conversion data to obtain the user conversion data associated with the same user and the user conversion data corresponding to the same content, including:
and classifying the target user conversion data to obtain the user conversion data associated with the same user and the user conversion data corresponding to the same content.
6. A content display method, the method comprising:
acquiring content information of content to be displayed;
inputting content information of the content to be displayed into a content display model to determine a target user, wherein the content display model is obtained by training target user characteristics and target content characteristics constructed according to the method of claim 1;
and displaying the content to be displayed to the target user.
7. A feature creation apparatus, the apparatus comprising:
the data acquisition module is used for acquiring user conversion data corresponding to target content, wherein the user conversion data comprises non-attribution conversion data, and the non-attribution conversion data is user data attributing operation behaviors to a second content platform when the user generates the operation behaviors on the target content under the condition that a first content platform displays the target content, and the second content platform displays content related to the target content;
The data classification module is used for classifying the user conversion data to obtain the user conversion data associated with the same user and the user conversion data corresponding to the same content;
the feature construction module is used for constructing target user features according to the user conversion data associated with the same user and constructing target content features according to the user conversion data corresponding to the same content, wherein the target user features comprise at least one of list user features, numerical user features and recency features, the list user features are used for representing content platform features or content features accessed before operation behaviors are generated, the numerical user features are used for representing quantity features of the generated operation behaviors, and the recency features are used for representing time interval features between the generation time and the current time of the latest operation behaviors;
the target user features and target content features are used to train a content display model that is used to determine content displayed to the target user.
8. A content display apparatus, characterized in that the apparatus comprises:
the acquisition module is used for acquiring content information of the content to be displayed;
The determining module is used for inputting the content information of the content to be displayed into a content display model to determine a target user, and the content display model is obtained by training the target user characteristics and the target content characteristics constructed according to the method of claim 1;
and the display module is used for displaying the content to be displayed to the target user.
9. A computer readable medium on which a computer program is stored, characterized in that the program, when being executed by a processing device, carries out the steps of the method according to any one of claims 1-6.
10. An electronic device, comprising:
a storage device having a computer program stored thereon;
processing means for executing said computer program in said storage means to carry out the steps of the method according to any one of claims 1-6.
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| CN110059925A (en) * | 2019-03-15 | 2019-07-26 | 微梦创科网络科技（中国）有限公司 | A kind of advertisement data processing method and device |
| CN110287180A (en) * | 2019-06-25 | 2019-09-27 | 上海诚数信息科技有限公司 | A kind of air control modeling method based on deep learning |
| CN110569429A (en) * | 2019-08-08 | 2019-12-13 | 阿里巴巴集团控股有限公司 | method, device and equipment for generating content selection model |
| CN111737575A (en) * | 2020-06-19 | 2020-10-02 | 北京字节跳动网络技术有限公司 | Content distribution method and device, readable medium and electronic equipment |
| CN111949890A (en) * | 2020-09-27 | 2020-11-17 | 平安科技（深圳）有限公司 | Data recommendation method, equipment, server and storage medium based on medical field |
| CN112434818A (en) * | 2020-11-19 | 2021-03-02 | 脸萌有限公司 | Model construction method, device, medium and electronic equipment |
| CN112633945A (en) * | 2020-12-31 | 2021-04-09 | 北京达佳互联信息技术有限公司 | Landing page delivery method, delivery data processing method, device, equipment and medium |
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| WO2022245279A1 (en) | 2022-11-24 |
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