CN112148352A - Component configuration method, device, equipment and computer readable medium - Google Patents
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Abstract
The application relates to a component configuration method, a device, equipment and a computer readable medium. The method comprises the following steps: acquiring first behavior data of a target object, wherein the first behavior data is generated by the operation of the target object on an internet platform; determining a first behavior feature of the target object according to the first behavior data; and configuring a target component matched with the first behavior characteristic, and showing the target component to the target object. The method and the device can intelligently analyze the favorite features of the user according to the behavior data generated by the user on the e-commerce platform, and then intelligently display the page assembly matched with the favorite features of the user, so that the problems that the assembly configuration mode is single, the user demand is not matched, and the user click conversion rate is low are solved.
Description
Technical Field
The present application relates to the field of internet technologies, and in particular, to a method, an apparatus, a device, and a computer-readable medium for configuring a component.
Background
With the rapid development of internet technology, the e-commerce shopping platform has become a main way for many users to purchase goods. The e-commerce shopping platform needs to not only recommend goods to attract users to meet the preferences of the users, but also provide page components to different users enough to arouse visual impact and interest, so that the users generate click impulses.
Currently, in the related art, the page style presents the content to the user in a feed stream and continuously updates, the updated content is provided according to the program, the output mode of the component is single and is not matched with the user requirement, and therefore the click conversion rate of the user is low.
In view of the above problems, no effective solution has been proposed.
Disclosure of Invention
The application provides a component configuration method, a device, equipment and a computer readable medium, which are used for solving the technical problems that a component configuration mode is single and is not matched with user requirements.
According to an aspect of an embodiment of the present application, there is provided a component configuration method including: acquiring first behavior data of a target object, wherein the first behavior data is generated by the operation of the target object on an internet platform; determining a first behavior feature of the target object according to the first behavior data; and configuring a target component matched with the first behavior characteristic, and showing the target component to the target object.
Optionally, determining the first behavior feature of the target object according to the first behavior data comprises: identifying the first behavior data by using a first neural network model; and determining a first behavior feature of the target object according to a recognition result of the first neural network model on the first behavior data, wherein the first neural network model is obtained by training the second neural network model by using training data with marking information, the marking information is used for marking the user behavior feature corresponding to the training data, and the recognition result is used for indicating the incidence relation between the first behavior data and the first behavior feature.
Optionally, before the identifying the first behavior data by using the first neural network model, the method further includes obtaining the first neural network model as follows: initializing each parameter in the second neural network model through the training data to obtain a third neural network model; under the condition that the identification accuracy of the third neural network model on the test data reaches a target threshold value, taking the third neural network model as a first neural network model; and under the condition that the recognition accuracy of the third neural network model on the test data does not reach the target threshold, continuing to train the third neural network model by using the training data to adjust the numerical values of all the parameters in the third neural network model until the recognition accuracy of the third neural network model on the test data reaches the target threshold.
Optionally, before the third neural network model is used as the first neural network model, the method further includes training the third neural network model until the third neural network model converges as follows: inputting each training data into a third neural network model to obtain a training predicted value of the user behavior characteristic; determining a loss value according to the difference between the plurality of training predicted values and the actual user behavior characteristics corresponding to the training data; and correcting the third neural network model by using the plurality of loss values until the precision of the output result of the third neural network model reaches the target threshold value.
Optionally, configuring the target component matched with the first behavior feature comprises: acquiring a target code block matched with the first behavior feature, wherein the target code block is an independent code block in a code warehouse; executing a target code block in a target frame corresponding to a target page to generate a first component, wherein the target page is a page currently browsed by a target object, and the target frame is a technical frame applied by the target page; and under the condition that the first component is matched with the target display screen, taking the first component as the target component, and displaying the target component on a target page, wherein the target display screen is a display screen on a terminal used by the target object.
Optionally, in a case where the first component is adapted to the target display screen, before the first component is taken as the target component, the method further includes adjusting the first component as follows: acquiring a first resolution of a target display screen; calculating a scaling coefficient between the first resolution and a preset resolution, wherein the preset resolution is the resolution of a display screen used by the first component in the original design; the first component is adjusted according to the scaling factor.
Optionally, in the case that the target component operates abnormally, the method further includes: masking the target code block to remove the target component; and sending the target code block and the exception information to an exception handling queue for handling.
According to another aspect of the embodiments of the present application, there is provided a component configuration apparatus including: the data acquisition module is used for acquiring first behavior data of the target object, wherein the first behavior data is generated by the operation of the target object on the Internet platform; the characteristic determining module is used for determining a first behavior characteristic of the target object according to the first behavior data; and the component configuration module is used for configuring the target component matched with the first behavior characteristic and displaying the target component to the target object.
According to another aspect of the embodiments of the present application, there is provided an electronic device, including a memory, a processor, a communication interface, and a communication bus, where the memory stores a computer program executable on the processor, and the memory and the processor communicate with each other through the communication bus and the communication interface, and the processor implements the steps of the method when executing the computer program.
According to another aspect of embodiments of the present application, there is also provided a computer readable medium having non-volatile program code executable by a processor, the program code causing the processor to perform the above-mentioned method.
Compared with the related art, the technical scheme provided by the embodiment of the application has the following advantages:
the method comprises the steps of acquiring first behavior data of a target object, wherein the first behavior data is generated by the operation of the target object on an internet platform; determining a first behavior feature of the target object according to the first behavior data; and configuring a target component matched with the first behavior characteristic, and showing the target component to the target object. The method and the device can intelligently analyze the favorite features of the user according to the behavior data generated by the user on the e-commerce platform, and then intelligently display the page assembly matched with the favorite features of the user, so that the problems that the assembly configuration mode is single, the user demand is not matched, and the user click conversion rate is low are solved.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and together with the description, serve to explain the principles of the application.
In order to more clearly illustrate the technical solutions in the embodiments or related technologies of the present application, the drawings needed to be used in the description of the embodiments or related technologies will be briefly described below, and it is obvious for those skilled in the art to obtain other drawings without any creative effort.
FIG. 1 is a hardware environment diagram of an alternative component configuration method according to an embodiment of the present application;
FIG. 2 is a flow chart of an alternative component configuration method provided in accordance with an embodiment of the present application;
FIG. 3 is a flow chart of an alternative feature recognition method provided in accordance with an embodiment of the present application;
FIG. 4 is a flow chart of an alternative decoupling assembly configuration method provided in accordance with an embodiment of the present application;
FIG. 5 is a block diagram of an alternative component configuration apparatus provided in accordance with an embodiment of the present application;
fig. 6 is a schematic structural diagram of an alternative electronic device according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
In the following description, suffixes such as "module", "component", or "unit" used to denote elements are used only for the convenience of description of the present application, and have no specific meaning in themselves. Thus, "module" and "component" may be used in a mixture.
In the related art, the page style presents the content to the user in a feed stream and is continuously updated, the updated content is provided according to a program, and the output mode of the component is single and is not matched with the requirements of the user. And the page dynamic component can only be developed in a frame such as single vue, or exact or native javascript, the cross-frame performance is weak, and the page compatibility and the page attractiveness cannot be guaranteed.
To address the problems mentioned in the background, according to an aspect of embodiments of the present application, an embodiment of a component configuration method is provided.
Alternatively, in the embodiment of the present application, the above-described component configuration method may be applied to a hardware environment formed by the terminal 101 and the server 102 as shown in fig. 1. As shown in fig. 1, a server 102 is connected to a terminal 101 through a network, which may be used to provide services for the terminal or a client installed on the terminal, and a database 103 may be provided on the server or separately from the server, and is used to provide data storage services for the server 102, and the network includes but is not limited to: wide area network, metropolitan area network, or local area network, and the terminal 101 includes but is not limited to a PC, a cell phone, a tablet computer, and the like.
A component configuration method in the embodiment of the present application may be executed by the server 102, or may be executed by both the server 102 and the terminal 101, as shown in fig. 2, the method may include the following steps:
step S201, acquiring first behavior data of the target object, where the first behavior data is generated by the target object operating on the internet platform.
In this embodiment of the application, the internet platform may be a social media platform, an e-commerce shopping platform, and the like, the target object is a user, the first behavior data may be data generated by a series of operations of the user on the social media platform and the e-commerce shopping platform, and taking the e-commerce shopping platform as an example, the first behavior data may be operations of clicking, sliding, long pressing, and the like of each component in a page by the user. The above operation of the user on the components in the page is not only because the corresponding commodities of the components attract the user, but also the visual effect of the components is consistent with the preference of the user, so that the user is interested in generating the impulse of clicking. Therefore, behavior data generated by the user in the process of browsing the page is obtained, and the preference and interest preference of the user can be further analyzed.
In the embodiment of the application, the time length of stay of the user on a single page can be obtained. The length of time a user remains on a single page can reflect the degree of interest the user has in the items, component buttons, etc. on that page. In order to avoid misjudging that the user is far away from the equipment and the retention time of the page is long, the user operation can be monitored, if no operation exists for a long time, the current situation can be judged to be that the user is far away from the equipment, so that the risk of misjudgment is reduced, and the accuracy of user analysis is improved.
In the embodiment of the application, personal data of the user, such as age, gender and the like, can also be acquired. The group range of the user can be reduced to a certain extent through data such as age, gender and the like, and the accuracy of user analysis is improved.
In the embodiment of the application, adjustment data fed back by the user can be obtained. In the process of browsing the page, the user can perform actions such as collection, praise and the like on very favorite commodities and activities, and can feed back the uninterested contents to reduce the related contents. The adjustment data fed back by the user can intuitively reflect the interest and the preference of the user, so that the user analysis is more accurate.
Step S202, determining a first behavior feature of the target object according to the first behavior data.
In the embodiment of the application, the interest preference of the user can be positioned based on big data analysis. A large amount of data can be collected to train a neural network model, and characteristics of user behavior data are extracted by using the neural network model, so that interest preference of a user is positioned. The neural network model may be a deep belief network model, a convolutional neural network model, a recurrent neural network model, or the like.
Step S203, configuring a target component matched with the first behavior feature, and displaying the target component to the target object.
In the embodiment of the application, the components matched with the interest preferences of the user can be configured after the interest preferences of the user are positioned, so that the components which can arouse the interest of the user can be provided for the user, different visual effects can be achieved, and the components which are oriented by the content can be used. In the process of configuration and display to the user, the components can be switched in real time according to the analysis of the user, so that the components which are more suitable for the requirements of the user are dynamically configured, and the click conversion rate of the user is improved.
In the embodiment of the application, previous operation records of a user can be collected, a hot spot graph of the page component is generated, more points on the hot spot graph of the component with more clicking times are more and more dense, and therefore the same or similar components can be configured to be displayed to the user, so that the user clicking is attracted, and the clicking conversion rate of the user is improved.
In the embodiment of the application, the e-commerce shopping platform is taken as an example, the final purpose of improving the click conversion rate of the user is to attract the user to purchase commodities.
By adopting the technical scheme, the favorite features of the user can be intelligently analyzed according to the behavior data generated by the user on the e-commerce platform, and then the page component matched with the favorite features of the user is intelligently displayed, so that the problem that the click conversion rate of the user is low due to the fact that the component configuration mode is single and the user requirement is not matched is solved.
The present application provides a method for determining user interest preference characteristics using a neural network model, which is described in detail below with reference to the steps shown in fig. 3.
Optionally, determining the first behavior feature of the target object according to the first behavior data comprises:
step S301, the first behavior data is identified by utilizing a first neural network model.
In this embodiment of the application, the first behavior data may be used as an input of a first neural network model, and the first neural network model identifies the first behavior data and then outputs an identification result. The recognition result includes predicted values of the respective user interest preference features corresponding to the first behavior data.
Step S302, determining a first behavior feature of the target object according to a recognition result of the first neural network model on the first behavior data, wherein the first neural network model is obtained by training the second neural network model by using training data with marking information, the marking information is used for marking a user behavior feature corresponding to the training data, and the recognition result is used for indicating an incidence relation between the first behavior data and the first behavior feature.
In the embodiment of the present application, the user interest preference feature with the largest predicted value may be used as the first behavior feature matched with the first behavior data.
In the embodiment of the present application, the first neural network model and the second neural network model are taken as deep belief network models as an example. The deep belief network model (DBN) is a probabilistic generative model that builds a joint distribution between observed data and individual tags. In the embodiment of the application, joint distribution between user behavior data (observation data) and different user interest preference characteristics (labels) is established.
Alternatively, a joint distribution between the length of time a user stays on a single page, age, gender, etc. (observation data) and different device control parameters user interest preference characteristics (tags) may also be established.
In the embodiment of the application, the marking information at least identifies the user behavior data in each piece of training data and the user interest preference features corresponding to the user behavior data, for example, a dynamic component of a user frequently clicking a real-time action and features of the user interested in dynamic things are marked, a component with a darker color is marked and features of the user interested in dark things are marked. The marking information can also identify the age, the gender, the stay time in a single page, the corresponding user interest preference characteristics and the like of the user in each training data.
Optionally, before the identifying the first behavior data by using the first neural network model, the method further includes obtaining the first neural network model as follows:
and initializing each parameter in the second neural network model through the training data to obtain a third neural network model.
And under the condition that the identification accuracy of the third neural network model on the test data reaches a target threshold value, taking the third neural network model as the first neural network model.
And under the condition that the recognition accuracy of the third neural network model on the test data does not reach the target threshold, continuing to train the third neural network model by using the training data to adjust the numerical values of all the parameters in the third neural network model until the recognition accuracy of the third neural network model on the test data reaches the target threshold.
In the embodiment of the application, the untrained second neural network model can be initialized by using the training data to obtain a third neural network model, then the third neural network model is trained, the third neural network model is tested by using the test data under the condition that the third neural network model is trained to be convergent, and the third neural network model is used as the first neural network model and put into use under the condition that the precision of the test result reaches the target threshold value. And under the condition that the precision of the test result does not reach the target threshold value, adjusting the converged threshold value, and continuing to train the third neural network model.
Optionally, before the third neural network model is used as the first neural network model, the method further includes training the third neural network model until the third neural network model converges as follows:
inputting each training data into a third neural network model to obtain a training predicted value of the user behavior characteristic;
determining a loss value according to the difference between the plurality of training predicted values and the actual user behavior characteristics corresponding to the training data;
and correcting the third neural network model by using the plurality of loss values until the precision of the output result of the third neural network model reaches the target threshold value.
In the embodiment of the application, the model training stage is a stage of continuously adjusting model parameters according to the continuously reduced error.
In the embodiment of the application, a deep confidence network model is taken as an example, and an optional overall model training method is described.
First, a large amount of data needs to be collected, including but not limited to the following parameters: the number of clicks the user has on each component, the speed of sliding on the page, age, gender, length of stay on a single page, etc.
And then, the collected parameters are summarized and simply processed so as to be used in time during model training. The simple processing may be data cleansing of the acquired parameters. Data cleansing (Data cleansing) is a process of re-examining and verifying Data with the aim of deleting duplicate information, correcting existing errors, and providing Data consistency.
And then, the dimension reduction can be carried out on the collected data by adopting a principal component analysis method so as to select main characteristics and remove part of redundant data. Inputting the data subjected to dimensionality reduction into a deep belief network model (DBN), setting the number N of hidden layers and the learning rate of the deep belief network model, determining the number of nodes in each hidden layer through a genetic algorithm, and meanwhile, carrying out self-adaptive adjustment on the learning rate by using an Adam optimization algorithm. And then, adjusting the weight and the bias of the model by using an error back propagation algorithm to establish a prediction model.
The present application provides a method of how to configure a component in detail, which is described in detail below with reference to the steps shown in fig. 4.
Optionally, configuring the target component matched with the first behavior feature comprises:
step S401, a target code block matched with the first behavior feature is obtained, and the target code block is an independent code block in a code warehouse.
Step S402, executing a target code block in a target frame corresponding to a target page to generate a first component, wherein the target page is a currently browsed page of a target object, and the target frame is a technical frame applied by the target page;
step S403, in a case that the first component is adapted to the target display screen, taking the first component as the target component, and displaying the target component on the target page, where the target display screen is a display screen on the terminal used by the target object.
In the embodiment of the present application, a code patch technology may be adopted, each code block may implement an independent function module, and there is no dependency relationship between each code block and the function module, so as to implement complete decoupling. The code may be written in a cross-platform code editor to run on a different platform, different framework, when needed to execute the target code block.
The target framework may be a user interface framework commonly used in front-end development, such as vue, act, etc.
According to the method and the device, related code blocks can be called from a code warehouse to run according to the determined user interest preference characteristics, the target component is generated and displayed to the user, the component which is interested by the user and has visual impact is provided, and the conversion rate of clicking of the user is improved.
The present application also provides a method of component adaptation, as shown in fig. 5.
Optionally, in a case where the first component is adapted to the target display screen, before the first component is taken as the target component, the method further includes adjusting the first component as follows:
acquiring a first resolution of a target display screen; calculating a scaling coefficient between the first resolution and a preset resolution, wherein the preset resolution is the resolution of a display screen used by the first component in the original design; the first component is adjusted according to the scaling factor.
In the embodiment of the application, the display position and the size of the component can be properly adjusted according to the device actually used by the user, so that the problem that the component styles displayed by different devices are inconsistent is solved. During the fitting, a mode of scaling in equal proportion can be adopted, and a mode of adjusting the horizontal spacing can also be adopted. For example, the preset resolution is 320 × 480 pixels, and the component display uses a double graph, that is, one dot in the screen is represented by one pixel. The pixel density (PPI) of a resolution screen of 320 × 480 Pixels size is 163, i.e., 163 Pixels Per Inch of length. If the device used by the user has a resolution of 640 x 960 pixels and the pixel density of the resolution screen with a size of 640 x 960 pixels is 326, then from the above two pixel densities, a scaling factor of 2 can be obtained, and therefore it can be determined that the double graph display component is used on the device of the user, where the double graph is represented by two pixels for one dot in the screen.
Optionally, in the case that the target component operates abnormally, the method further includes: masking the target code block to remove the target component; and sending the target code block and the exception information to an exception handling queue for handling.
In the embodiment of the application, as the code patch technology is adopted for decoupling, and no dependency relationship exists among the components, the components can be shielded and submitted to a worker for exception handling under the condition that the components are abnormal, so that the operation risk is reduced.
In the embodiment of the application, through the code patch technology, not only can the decoupling of each component be realized, but also each floor of the long page can be decoupled, and once the floor data is abnormal, the floor data is automatically hidden, and the abnormal processing is carried out by the staff.
The method comprises the steps of acquiring first behavior data of a target object, wherein the first behavior data is generated by the operation of the target object on an internet platform; determining a first behavior feature of the target object according to the first behavior data; and configuring a target component matched with the first behavior characteristic, and showing the target component to the target object. The method and the device can intelligently analyze the favorite features of the user according to the behavior data generated by the user on the e-commerce platform, and then intelligently display the page assembly matched with the favorite features of the user, so that the problems that the assembly configuration mode is single, the user demand is not matched, and the user click conversion rate is low are solved.
According to still another aspect of an embodiment of the present application, as shown in fig. 5, there is provided a component configuring apparatus including: the data acquiring module 501 is configured to acquire first behavior data of a target object, where the first behavior data is generated by an operation of the target object on an internet platform; a feature determination module 502, configured to determine a first behavior feature of the target object according to the first behavior data; and the component configuration module 503 is configured to configure a target component matched with the first behavior feature and expose the target component to the target object.
It should be noted that the data obtaining module 501 in this embodiment may be configured to execute step S201 in this embodiment, the feature determining module 502 in this embodiment may be configured to execute step S202 in this embodiment, and the component configuring module 503 in this embodiment may be configured to execute step S203 in this embodiment.
It should be noted here that the modules described above are the same as the examples and application scenarios implemented by the corresponding steps, but are not limited to the disclosure of the above embodiments. It should be noted that the modules described above as a part of the apparatus may operate in a hardware environment as shown in fig. 1, and may be implemented by software or hardware.
Optionally, the feature determination module is specifically configured to: identifying the first behavior data by using a first neural network model; and determining a first behavior feature of the target object according to a recognition result of the first neural network model on the first behavior data, wherein the first neural network model is obtained by training the second neural network model by using training data with marking information, the marking information is used for marking the user behavior feature corresponding to the training data, and the recognition result is used for indicating the incidence relation between the first behavior data and the first behavior feature.
Optionally, the component configuring apparatus further includes a model training module, configured to: initializing each parameter in the second neural network model through the training data to obtain a third neural network model; under the condition that the identification accuracy of the third neural network model on the test data reaches a target threshold value, taking the third neural network model as a first neural network model; and under the condition that the recognition accuracy of the third neural network model on the test data does not reach the target threshold, continuing to train the third neural network model by using the training data to adjust the numerical values of all the parameters in the third neural network model until the recognition accuracy of the third neural network model on the test data reaches the target threshold.
Optionally, the model training module is further configured to: inputting each training data into a third neural network model to obtain a training predicted value of the user behavior characteristic; determining a loss value according to the difference between the plurality of training predicted values and the actual user behavior characteristics corresponding to the training data; and correcting the third neural network model by using the plurality of loss values until the precision of the output result of the third neural network model reaches the target threshold value.
Optionally, the component configuration module is specifically configured to: acquiring a target code block matched with the first behavior feature, wherein the target code block is an independent code block in a code warehouse; executing a target code block in a target frame corresponding to a target page to generate a first component, wherein the target page is a page currently browsed by a target object, and the target frame is a technical frame applied by the target page; and under the condition that the first component is matched with the target display screen, taking the first component as the target component, and displaying the target component on a target page, wherein the target display screen is a display screen on a terminal used by the target object.
Optionally, the component configuring apparatus further includes an adaptation module, configured to: acquiring a first resolution of a target display screen; calculating a scaling coefficient between the first resolution and a preset resolution, wherein the preset resolution is the resolution of a display screen used by the first component in the original design; the first component is adjusted according to the scaling factor.
Optionally, the component configuring apparatus further includes an exception handling module, configured to: masking the target code block to remove the target component; and sending the target code block and the exception information to an exception handling queue for handling.
According to another aspect of the embodiments of the present application, there is provided an electronic device, as shown in fig. 6, including a memory 601, a processor 602, a communication interface 603, and a communication bus 604, where a computer program operable on the processor 602 is stored in the memory 601, the memory 601 and the processor 602 communicate with each other through the communication interface 603 and the communication bus 604, and the processor 602 implements the steps of the method when executing the computer program.
The memory and the processor in the electronic equipment are communicated with the communication interface through a communication bus. The communication bus may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The communication bus may be divided into an address bus, a data bus, a control bus, etc.
The Memory may include a Random Access Memory (RAM) or a non-volatile Memory (non-volatile Memory), such as at least one disk Memory. Optionally, the memory may also be at least one memory device located remotely from the processor.
The Processor may be a general-purpose Processor, and includes a Central Processing Unit (CPU), a Network Processor (NP), and the like; the Integrated Circuit may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, a discrete Gate or transistor logic device, or a discrete hardware component.
There is also provided, in accordance with yet another aspect of an embodiment of the present application, a computer-readable medium having non-volatile program code executable by a processor.
Optionally, in an embodiment of the present application, a computer readable medium is configured to store program code for the processor to perform the following steps:
acquiring first behavior data of a target object, wherein the first behavior data is generated by the operation of the target object on an internet platform;
determining a first behavior feature of the target object according to the first behavior data;
and configuring a target component matched with the first behavior characteristic, and showing the target component to the target object.
Optionally, the specific examples in this embodiment may refer to the examples described in the above embodiments, and this embodiment is not described herein again.
When the embodiments of the present application are specifically implemented, reference may be made to the above embodiments, and corresponding technical effects are achieved.
It is to be understood that the embodiments described herein may be implemented in hardware, software, firmware, middleware, microcode, or any combination thereof. For a hardware implementation, the Processing units may be implemented within one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), general purpose processors, controllers, micro-controllers, microprocessors, other electronic units configured to perform the functions described herein, or a combination thereof.
For a software implementation, the techniques described herein may be implemented by means of units performing the functions described herein. The software codes may be stored in a memory and executed by a processor. The memory may be implemented within the processor or external to the processor.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is merely a logical division, and in actual implementation, there may be other divisions, for example, multiple modules or components may be combined or integrated into another system, or some features may be omitted, or not implemented. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the embodiments of the present application may be essentially implemented or make a contribution to the prior art, or may be implemented in the form of a software product stored in a storage medium and including several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a U disk, a removable hard disk, a ROM, a RAM, a magnetic disk, or an optical disk. It is noted that, in this document, relational terms such as "first" and "second," and the like, may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The above description is merely exemplary of the present application and is presented to enable those skilled in the art to understand and practice the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A method of component configuration, comprising:
acquiring first behavior data of a target object, wherein the first behavior data is generated by the operation of the target object on an internet platform;
determining a first behavior feature of the target object according to the first behavior data;
and configuring a target component matched with the first behavior feature, and showing the target component to the target object.
2. The method of claim 1, wherein determining a first behavioral feature of the target object from the first behavioral data comprises:
identifying the first behavior data using a first neural network model;
determining the first behavior feature of the target object according to a recognition result of the first neural network model on the first behavior data, wherein the first neural network model is obtained after training a second neural network model by using training data with marking information, the marking information is used for marking the user behavior feature corresponding to the training data, and the recognition result is used for indicating an association relationship between the first behavior data and the first behavior feature.
3. The method of claim 2, wherein prior to identifying the first behavioral data using a first neural network model, the method further comprises obtaining the first neural network model as follows:
initializing all parameters in the second neural network model through the training data to obtain a third neural network model;
taking the third neural network model as the first neural network model when the recognition accuracy of the third neural network model on the test data reaches a target threshold;
and under the condition that the recognition accuracy of the third neural network model on the test data does not reach the target threshold, continuing to train the third neural network model by using the training data to adjust the numerical values of all parameters in the third neural network model until the recognition accuracy of the third neural network model on the test data reaches the target threshold.
4. The method of claim 3, wherein prior to using the third neural network model as the first neural network model, the method further comprises training the third neural network model until the third neural network model converges as follows:
inputting each training data into the third neural network model to obtain a training predicted value of the user behavior characteristic;
determining a loss value according to a difference between the plurality of training predicted values and the actual user behavior characteristics corresponding to the training data;
and correcting the third neural network model by using a plurality of loss values until the precision of the output result of the third neural network model reaches the target threshold value.
5. The method of any of claims 1 to 4, wherein configuring the target component that matches the first behavioral feature comprises:
acquiring a target code block matched with the first behavior feature, wherein the target code block is an independent code block in a code warehouse;
executing the target code block in a target frame corresponding to a target page to generate a first component, wherein the target page is a page currently browsed by the target object, and the target frame is a technical frame applied by the target page;
and under the condition that the first component is matched with a target display screen, taking the first component as the target component, and displaying the target component on the target page, wherein the target display screen is a display screen on a terminal used by the target object.
6. The method of claim 5, wherein, in the case that the first component is adapted to a target display screen, prior to treating the first component as the target component, the method further comprises adjusting the first component as follows:
acquiring a first resolution of the target display screen;
calculating a scaling factor between the first resolution and a preset resolution, wherein the preset resolution is a display screen resolution used by the first assembly in original design;
and adjusting the first component according to the scaling factor.
7. The method of claim 5, wherein in the event that the target component is abnormally operational, the method further comprises:
masking the target code block to remove the target component;
and sending the target code block and the abnormal information to an abnormal processing queue for processing.
8. A component placement apparatus, comprising:
the system comprises a data acquisition module, a data processing module and a data processing module, wherein the data acquisition module is used for acquiring first behavior data of a target object, and the first behavior data is generated by the operation of the target object on an internet platform;
the characteristic determining module is used for determining a first behavior characteristic of the target object according to the first behavior data;
and the component configuration module is used for configuring a target component matched with the first behavior characteristic and displaying the target component to the target object.
9. An electronic device comprising a memory, a processor, a communication interface and a communication bus, wherein the memory stores a computer program operable on the processor, and the memory and the processor communicate via the communication bus and the communication interface, wherein the processor implements the steps of the method according to any of the claims 1 to 7 when executing the computer program.
10. A computer-readable medium having non-volatile program code executable by a processor, wherein the program code causes the processor to perform the method of any of claims 1 to 7.
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