CN111079053A - Product information display method and device, electronic equipment and storage medium - Google Patents
Product information display method and device, electronic equipment and storage mediumDownload PDFInfo
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Abstract
Description
Technical Field
The present invention relates to the field of computer technologies, and in particular, to a method and an apparatus for displaying product information, an electronic device, and a storage medium.
Background
Currently, in many applications, product information of some other products besides the application can be displayed, for example, product information of a game product or product information of a shopping platform can be displayed on an application program interface. In the existing display scheme, the product information of products of corresponding types can be displayed for different types of users by analyzing the user types, for example, for users who often shop, the product information of a shopping platform can be displayed on an application program interface.
However, the existing display scheme has the problem of unreasonable display time. For example, a user may be showing product information on an application interface at work that is not related to the user's work, or may be showing too much product information at the same user terminal for a certain period of time. The problem that the product information display time is unreasonable causes the experience of the user on the application program to be poor, and further the user can uninstall the application program.
Disclosure of Invention
The embodiment of the invention aims to provide a product information display method, which is used for solving the problem that the product information display time in an application program is unreasonable. The specific technical scheme is as follows:
in order to achieve the above object, an embodiment of the present invention provides a product information display method, including:
acquiring environment information of a user terminal, wherein the environment information represents the operation condition of the user terminal;
inputting the environmental information into a pre-trained neural network model to obtain an output result indicating whether to display the information of the product to be displayed;
and when the output result shows that the information of the product to be displayed is displayed, displaying the information of the product to be displayed.
Further, the environment information includes at least one of the following information:
information of an application program run by the user terminal;
load information of the user terminal;
network usage information of the user terminal;
runtime information of the user terminal.
Further, the inputting the environmental information into a pre-trained neural network model to obtain an output result indicating whether to display the information of the product to be displayed includes:
and inputting the environmental information and the characteristic information of the product to which the information of the product to be displayed belongs into a pre-trained neural network model to obtain an output result indicating whether the information of the product to be displayed is displayed or not.
Further, the characteristic information of the product at least comprises one of the following information:
type information of the product;
name information of the product;
content information of the product, the content information comprising: text content information and/or image content information.
In order to achieve the above object, an embodiment of the present invention provides a neural network model training method, including:
inputting sample environment information contained in a training sample set into a neural network model to be trained to obtain an output result of the neural network model to be trained, wherein the training sample set contains sample environment information and a known display result indicating whether product information of a sample product is displayed or not, and the sample environment information indicates the operation condition of a specified user terminal;
determining whether a preset training ending condition is met;
if yes, determining the current neural network model to be trained as the neural network model obtained by training;
if not, adjusting the neural network model to be trained to obtain a new neural network model to be trained, and starting the next training.
Further, the sample environment information includes at least one of the following information:
the information of the application program operated by the appointed user terminal;
load information of the specified user terminal;
network usage information of the designated user terminal;
the run time information of the specified user terminal.
Further, the inputting the sample environment information included in the training sample set into the neural network model to be trained to obtain the output result of the neural network model to be trained includes:
inputting the sample environment information contained in the training sample set and the characteristic information of the sample product into a neural network model to be trained to obtain an output result of the neural network model to be trained;
the training sample set also contains characteristic information of the sample product.
Further, the characteristic information of the sample product at least comprises one of the following information:
type information of the sample product;
name information of the sample product;
content information of the sample product, the content information comprising: text content information and/or image content information.
Further, the training sample set is obtained by the following steps:
when sample product information is displayed in a specified application program of a specified user terminal, recording environment information of the specified user terminal as sample environment information;
monitoring whether a preset operation is executed;
when the preset operation is monitored to be executed, determining that a display result corresponding to the sample environment information is display product information;
and when the preset operation is monitored not to be executed, determining that the display result corresponding to the sample environment information is not to display the product information.
Further, the training sample set is obtained by the following steps:
when sample product information is displayed in a specified application program of a specified user terminal, recording environment information of the specified user terminal as sample environment information;
monitoring whether a preset operation is executed;
when the preset operation is monitored to be executed, determining that the display result corresponding to the sample environment information and the characteristic information of the sample product is display product information;
and when the preset operation is monitored not to be executed, determining that the display result corresponding to the sample environment information and the characteristic information of the sample product is not to display product information.
Further, the monitoring whether the preset operation is executed includes:
monitoring whether the designated application program is not unloaded after a preset time period is passed by taking the time point when the designated application program starts to display the sample product information as an initial time; or,
monitoring whether the sample product information is clicked.
In order to achieve the above object, an embodiment of the present invention further provides a product information display apparatus, including:
the system comprises an information acquisition module, a processing module and a processing module, wherein the information acquisition module is used for acquiring environment information of a user terminal, and the environment information represents the running condition of the user terminal;
the information processing module is used for inputting the characteristic information of the product to which the information of the product to be displayed belongs and the environment information into a pre-trained neural network model to obtain an output result which represents whether the information of the product to be displayed is displayed or not;
and the information display module is used for displaying the information of the product to be displayed when the output result shows that the information of the product to be displayed is displayed.
Further, the environment information includes at least one of the following information:
information of an application program run by the user terminal;
load information of the user terminal;
network usage information of the user terminal;
runtime information of the user terminal.
Further, the information processing module is specifically configured to input the environmental information and feature information of a product to which the information of the product to be displayed belongs into a pre-trained neural network model, and obtain an output result indicating whether to display the information of the product to be displayed.
Further, the characteristic information of the product at least comprises one of the following information:
type information of the product;
name information of the product;
content information of the product, the content information comprising: text content information and/or image content information.
In order to achieve the above object, an embodiment of the present invention further provides a neural network model training apparatus, including:
the model training module is used for inputting sample environment information contained in a training sample set into a neural network model to be trained to obtain an output result of the neural network model to be trained, wherein the training sample set contains the sample environment information and a known display result indicating whether product information of a sample product is displayed or not, and the sample environment information indicates the running condition of a specified user terminal;
the condition determining module is used for determining whether a preset training finishing condition is met; if yes, determining the current neural network model to be trained as the neural network model obtained by training; if not, adjusting the neural network model to be trained to obtain a new neural network model to be trained, and starting the next training.
Further, the sample environment information includes at least one of the following information:
the information of the application program operated by the appointed user terminal;
load information of the specified user terminal;
network usage information of the designated user terminal;
the run time information of the specified user terminal.
Further, the model training module is specifically configured to input the sample environment information and the feature information of the sample product, which are included in the training sample set, into a neural network model to be trained, so as to obtain an output result of the neural network model to be trained; the training sample set also contains characteristic information of the sample product.
Further, the characteristic information of the sample product at least comprises one of the following information:
type information of the sample product;
name information of the sample product;
content information of the sample product, the content information comprising: text content information and/or image content information.
Further, the neural network model training apparatus further includes:
a sample obtaining module, configured to obtain the training sample set by using the following steps:
when sample product information is displayed in a specified application program of a specified user terminal, recording environment information of the specified user terminal as sample environment information;
monitoring whether a preset operation is executed;
when the preset operation is monitored to be executed, determining that a display result corresponding to the sample environment information is display product information;
and when the preset operation is monitored not to be executed, determining that the display result corresponding to the sample environment information is not to display the product information.
Further, the sample obtaining module is specifically configured to obtain the training sample set by using the following steps:
when sample product information is displayed in a specified application program of a specified user terminal, recording environment information of the specified user terminal as sample environment information;
monitoring whether a preset operation is executed;
when the preset operation is monitored to be executed, determining that the display result corresponding to the sample environment information and the characteristic information of the sample product is display product information;
and when the preset operation is monitored not to be executed, determining that the display result corresponding to the sample environment information and the characteristic information of the sample product is not to display product information.
Further, the sample obtaining module is specifically configured to monitor whether the specified application program is not unloaded after a preset time period elapses, with a time point at which the specified application program starts to display sample product information as an initial time; or,
monitoring whether the sample product information is clicked.
In order to achieve the above object, an embodiment of the present invention provides an electronic device, which includes a processor, a communication interface, a memory, and a communication bus, where the processor and the communication interface are configured to complete communication between the memory and the processor through the communication bus;
a memory for storing a computer program;
and the processor is used for realizing the steps of any one of the product information display methods when executing the program stored in the memory.
In order to achieve the above object, an embodiment of the present invention provides a computer-readable storage medium, in which a computer program is stored, and the computer program, when executed by a processor, implements any of the above steps of the product information presentation method.
In order to achieve the above object, an embodiment of the present invention further provides a computer program product containing instructions, which when run on a computer, causes the computer to perform any of the steps of the product information presentation method described above.
In order to achieve the above object, an embodiment of the present invention provides an electronic device, which includes a processor, a communication interface, a memory, and a communication bus, where the processor and the communication interface are configured to complete communication between the memory and the processor through the communication bus;
a memory for storing a computer program;
and the processor is used for realizing any step of the neural network model training method when executing the program stored in the memory.
In order to achieve the above object, an embodiment of the present invention provides a computer-readable storage medium, in which a computer program is stored, and the computer program, when executed by a processor, implements any of the above steps of the neural network model training method.
In order to achieve the above object, an embodiment of the present invention further provides a computer program product containing instructions, which when run on a computer, causes the computer to perform any of the above-mentioned neural network model training method steps.
The embodiment of the invention has the following beneficial effects:
according to the product information display method provided by the embodiment of the invention, the environment information of the user terminal is obtained, the environment information is input into the pre-trained neural network model, the output result indicating whether the product information to be displayed is obtained, and when the output result indicates that the product information to be displayed is displayed, the product information to be displayed is displayed. By adopting the method provided by the embodiment of the invention, the environment information is input into the pre-trained neural network model, and when the output result shows that the information of the product to be displayed is displayed, the information of the product to be displayed is displayed. The display time of the product information to be displayed is controlled through the pre-trained neural network model, the problem that the display time of the product information to be displayed is unreasonable is solved, and the experience of a user on an application program is improved.
Of course, not all of the advantages described above need to be achieved at the same time in the practice of any one product or method of the invention.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1a is a flowchart of a product information display method according to an embodiment of the present invention;
FIG. 1b is a flowchart of a neural network model training method according to an embodiment of the present invention;
FIG. 2 is a flowchart of another product information display method according to an embodiment of the present invention;
FIG. 3 is a flow chart of another neural network model training method provided by an embodiment of the present invention;
fig. 4a is a flowchart of acquiring a training sample set in a neural network model training method according to an embodiment of the present invention;
fig. 4b is a flowchart of another method for training a neural network model according to an embodiment of the present invention, in which a training sample set is obtained;
FIG. 5 is a schematic structural diagram of a product information display device according to an embodiment of the present invention;
fig. 6a is a schematic structural diagram of a neural network model training apparatus according to an embodiment of the present invention;
FIG. 6b is a schematic structural diagram of another neural network model training apparatus according to an embodiment of the present invention;
fig. 7 is a schematic structural diagram of an electronic device according to an embodiment of the present invention;
fig. 8 is a schematic structural diagram of another electronic device according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the 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 invention.
The embodiment of the invention discloses a product information display method, which comprises the following steps as shown in figure 1 a:
step 101a, obtaining environment information of the user terminal, wherein the environment information represents an operation condition of the user terminal.
And 102a, inputting the environment information into a pre-trained neural network model to obtain an output result indicating whether to display the information of the product to be displayed.
And 103a, displaying the information of the product to be displayed when the output result shows that the information of the product to be displayed is displayed.
By adopting the method provided by the embodiment of the invention, the environment information is input into the pre-trained neural network model, and when the output result shows that the information of the product to be displayed is displayed, the information of the product to be displayed is displayed. The display time of the product information to be displayed is controlled through the pre-trained neural network model, the problem that the display time of the product information to be displayed is unreasonable is solved, and the experience of a user on an application program is improved.
The following describes the product information display method and apparatus provided by the present invention in detail with specific embodiments in conjunction with the accompanying drawings.
In an embodiment of the present invention, as shown in fig. 2, a product information display method provided in an embodiment of the present invention may include the following steps:
In this step, the environment information may include part or all of the following information:
first environmental information: the information of the application program run by the user terminal may include:
the name of the application program currently opened by the user terminal, for example, the names of the application program a, the application program B and the application program C currently opened by the user terminal;
the process information of the application program run by the user terminal, for example, the number of processes run by the user terminal may be 63;
the names of the applications running in the background of the user terminal, for example, the names of the application D, the application E and the application F in the background of the user terminal.
Second environmental information: the load information of the user terminal may include:
the CPU resource occupancy of the user terminal, for example, the current CPU resource occupancy of the user terminal may be 10%;
the GPU resource occupancy of the user terminal, for example, the current GPU resource occupancy of the user terminal may be 8%;
the memory usage rate of the user terminal, for example, the current GPU resource occupancy rate of the user terminal may be 8%;
the memory frequency of the user terminal, the current memory frequency of the user terminal, may be 660 Hz.
Third environment information: the network usage information of the user terminal may include:
the network type of the user terminal, for example, the network to which the user terminal is currently connected may be a wired network, an optical fiber network, or a wireless network;
network quality of user terminal access;
the network speed of the user terminal access, for example, the network speed of the user terminal currently access is 10 Mbps.
Fourth environment information: the runtime information of the user terminal may include:
the current operation time of the user terminal, for example, the current operation time of the user terminal may be 9 hours, 24 minutes and 15 seconds;
the user terminal may be continuously operated for a period of time, for example, the user terminal may be operated for 3 hours and 15 minutes from the time of power-on.
In this step, the feature information of the product to which the information of the product to be displayed belongs may include part or all of the following information:
the type information of the product, for example, the product to which the information of the product to be displayed belongs may be a game product or a shopping platform product;
name information of the product, for example, the name of the product to which the information of the product to be displayed belongs may be product a, product B, or product C;
content information of the product, the content information may include: the text content information and/or the image content information can be, for example, words and/or images which introduce the characteristics and functions of the product.
And 204, when the output result shows that the information of the product to be displayed is displayed, displaying the information of the product to be displayed.
In this step, the manner of displaying the information of the product to be displayed may include:
the display can be carried out in a pop-up window mode;
the display interface can also be displayed at a preset position of the display interface of the user terminal, wherein the preset position can be set according to specific application conditions.
And step 205, when the output result shows that the information of the product to be displayed is not displayed, ending the operation.
By adopting the method provided by the embodiment of the invention, the characteristic information and the environmental information of the product to which the information of the product to be displayed belongs are input into the pre-trained neural network model, and the information of the product to be displayed is displayed by judging whether the output result represents the information of the product to be displayed or not, and if so, the information of the product to be displayed is displayed. The display time of the product information to be displayed is controlled through the pre-trained neural network model, the problem that the display time of the product information to be displayed is unreasonable is solved, and the experience of a user on an application program is improved.
In the embodiment of the invention, the neural network model is obtained by training based on a training sample set, and the training sample set can contain sample environment information and a known display result indicating whether to display product information of a sample product; alternatively, the training sample set may also include sample environment information, characteristic information of the sample product, and a display result of product information that is known to indicate whether the sample product is displayed.
The embodiment of the invention discloses a neural network model training method, which comprises the following steps as shown in figure 1 b:
and 101b, inputting sample environment information contained in the training sample set into the neural network model to be trained to obtain an output result of the neural network model to be trained, wherein the training sample set contains the sample environment information and a known display result indicating whether the product information of the sample product is displayed or not, and the sample environment information indicates the running condition of the appointed user terminal.
And step 102b, judging whether a preset training finishing condition is met.
And 103b, if yes, determining the current neural network model to be trained as the trained neural network model.
And step 104b, if the training result does not meet the requirement, adjusting the neural network model to be trained to obtain a new neural network model to be trained, and returning to the step 101b to start the next training.
The method provided by the embodiment of the invention is adopted. The neural network model is obtained through pre-training, and the display time of the product information to be displayed is controlled by the neural network model, so that the problem that the display time of the product information to be displayed is unreasonable is solved, and the experience of a user on an application program is improved.
The neural network model training method and apparatus provided by the present invention are described in detail below with reference to the accompanying drawings.
In an embodiment of the present invention, as shown in fig. 3, a neural network model training method provided in an embodiment of the present invention may include the following steps:
step 301, inputting the sample environment information and the characteristic information of the sample product contained in the training sample set into the neural network model to be trained, and obtaining the output result of the neural network model to be trained.
In this step, the Neural network model to be trained may be FANN (Fast architectural Neural network library) or may be another applicable Neural network model.
In this step, the preset training termination condition may include:
training the neural network model to be trained for preset times by using a training sample set, wherein the preset times can be specifically set according to practical application; or,
the method includes the steps that test sample environment information contained in a test sample set and feature information of a test sample product are input into a current neural network model to be trained, and the numerical value of a calculated loss function is smaller than a preset threshold value, wherein the preset threshold value can be specifically set according to practical application, for example, the preset threshold value can be 0.001, and the preset threshold value can also be 0.01.
And 303, when the preset training finishing condition is met, determining the current neural network model to be trained as the trained neural network model.
And 304, when the preset training ending condition is not met, adjusting the neural network model to be trained to obtain a new neural network model to be trained, returning to the step 301, and starting the next training.
In this step, adjusting the neural network model to be trained may include:
parameters of each layer of the neural network model to be trained are adaptively adjusted;
the model structure of the neural network model to be trained is adaptively adjusted, for example, the neural network model to be trained may be increased or reduced in parameter layer according to the current training result, or the neural network model to be trained may be increased or reduced in neural network node according to the current training result.
In the embodiment of the invention, the neural network MODEL obtained by training can be stored in the MODEL file, and the user terminal can download the software package containing the MODEL file from the server side and further use the neural network MODEL obtained by training to control the display time of the product information.
In an embodiment of the present invention, in a possible implementation manner, the training sample set includes sample environment information, feature information of a sample product, and a display result indicating whether the product information of the sample product is displayed, and the training sample set is obtained, as shown in fig. 4a, specifically, the method may include the following steps:
step 401a, when the sample product information is displayed in the designated application program of the designated user terminal, recording the environment information of the designated user terminal as the sample environment information.
In this step, the designated user terminal may be one or more user terminals for acquiring training samples.
In this step, the sample environment information indicates an operation condition of the specified user terminal, and the sample environment information may include part or all of the following types of information:
first-type environment information: the information specifying the application program run by the user terminal may include:
specifying the name of the application currently opened by the user terminal, for example, specifying the names of application a1, application B1, and application C1 currently opened by the user terminal; process information specifying that the user terminal runs the application, for example, the number of processes specified that the user terminal runs may be 65; the names of applications running in the background of the user terminal are specified, for example, the names of application D1, application E1, and application F1 in the background of the user terminal.
Second-type environment information: the load information specifying the user terminal may include:
specifying the CPU resource occupancy of the user terminal, for example, specifying the current CPU resource occupancy of the user terminal may be 15%; specifying the GPU resource occupancy of the user terminal, for example, specifying the current GPU resource occupancy of the user terminal may be 12%; specifying the memory usage rate of the user terminal, for example, specifying that the current GPU resource occupancy rate of the user terminal may be 8%; the memory frequency of the user terminal is designated, and the current memory frequency of the designated user terminal can be 660 Hz.
The third type of environment information: the network usage information specifying the user terminal may include:
the network type of the user terminal access is specified, for example, the network to which the user terminal currently accesses may be a wired network, an optical network, or a wireless network; appointing the network quality of the user terminal access; the network speed of the user terminal access is specified, for example, the network speed of the user terminal currently accessed is specified to be 10 Mbps.
Fourth type of environmental information: the specifying of the runtime information of the user terminal may include:
specifying the current operation time of the user terminal, for example, the current operation time of the user terminal may be 9 hours, 24 minutes and 10 seconds; the length of time that the user terminal is continuously operated is specified, for example, the length of time that the user terminal is operated from power-on may be 3 hours and 10 minutes.
In this step, the characteristic information of the sample product may include part or all of the following information:
type information of sample products, for example, the sample products can be game products and can also be shopping platform products; sample product name information, for example, the name of the sample product may be product a1, product B1, or product C1; content information of the sample product, the content information may include: the text content information and/or the image content information can be, for example, words and/or images which introduce the characteristics and functions of the sample product.
Instep 402a, it is determined whether a predetermined operation is performed, if yes, step 403a is performed, and if no, step 404a is performed.
In this step, determining whether the preset operation is executed may include:
mode A: the method comprises the steps of taking the time point when a designated application program starts to display sample product information as an initial time, and judging whether the designated application program is not unloaded after a preset time period;
the designated application program may be one or more application programs, and the preset time period may be set according to an actual application requirement, for example, the preset time period may be set to 24 hours.
Mode B: whether the sample product information is clicked or not is judged, for example, whether the sample product information displayed in the specified application program is clicked or not is monitored.
Step 403a, when it is monitored that the preset operation is executed, determining that the display result corresponding to the sample environment information and the characteristic information of the sample product is the display product information.
In this step, as in the above manner a, the time point at which the specified application starts to display the sample product information is taken as the starting time, and when it is monitored that the specified application is not uninstalled after the preset time period elapses, it may be determined that the display results corresponding to the sample environment information and the characteristic information of the sample product are display product information;
as described in the above manner B, when it is monitored that the user clicks the sample product information displayed in the designated application program, it may be determined that the display result corresponding to the sample environment information and the characteristic information of the sample product is the display product information.
Step 404a, when it is detected that the preset operation is not executed, determining that the display result corresponding to the sample environment information and the characteristic information of the sample product is not the product information.
In this step, as in the above manner a, the time point when the specified application starts to display the sample product information is taken as the starting time, and when it is monitored that the specified application is uninstalled after the preset time period elapses, it may be determined that the display results corresponding to the sample environment information and the characteristic information of the sample product are non-display product information;
as described in the above manner B, when it is monitored that the user does not click on the sample product information displayed in the designated application program, it may be determined that the display result corresponding to the sample environment information and the characteristic information of the sample product is the non-display product information.
In an embodiment of the present invention, in a possible implementation manner, the training sample set includes sample environment information and a display result indicating whether to display product information of a sample product, and the training sample set is obtained, as shown in fig. 4b, specifically includes the following steps:
In step 402b, it is determined whether a predetermined operation is performed, if yes, step 403b is performed, and if no, step 404b is performed.
Step 403b, when it is monitored that the preset operation is executed, determining that the display result corresponding to the sample environment information is the display product information.
Step 404b, when it is monitored that the preset operation is not executed, determining that the display result corresponding to the sample environment information is that the product information is not displayed.
In the embodiment of the invention, the training sample set is collected, the neural network model to be trained is trained on the basis of the training sample set to obtain the trained neural network model, and the display time of the product information to be displayed is controlled by using the trained neural network model, so that the problem of unreasonable display time of the product information to be displayed is solved, and the experience of a user on an application program is improved.
Based on the same inventive concept, according to the product information display method provided in the above embodiment of the present invention, correspondingly, another embodiment of the present invention further provides a product information display apparatus, a schematic structural diagram of which is shown in fig. 5, and specifically includes:
aninformation obtaining module 501, configured to obtain environment information of a user terminal, where the environment information indicates an operating condition of the user terminal;
theinformation processing module 502 is configured to input feature information and environmental information of a product to which the information of the product to be displayed belongs into a pre-trained neural network model, and obtain an output result indicating whether the information of the product to be displayed is displayed;
and theinformation display module 503 is configured to display the information of the product to be displayed when the output result indicates that the information of the product to be displayed is displayed.
By adopting the device provided by the embodiment of the invention, the environment information is input into the pre-trained neural network model, and when the output result shows that the information of the product to be displayed is displayed, the information of the product to be displayed is displayed. The display time of the product information to be displayed is controlled through the pre-trained neural network model, the problem that the display time of the product information to be displayed is unreasonable is solved, and the experience of a user on an application program is improved.
Further, the environment information includes at least one of the following information:
information of an application program run by the user terminal;
load information of the user terminal;
network usage information of the user terminal;
runtime information of the user terminal.
Further, theinformation processing module 502 is specifically configured to input the environmental information and the feature information of the product to which the information of the product to be displayed belongs into a pre-trained neural network model, so as to obtain an output result indicating whether to display the information of the product to be displayed.
Further, the characteristic information of the product at least comprises one of the following information:
type information of the product;
name information of the product;
content information of the product, the content information comprising: text content information and/or image content information.
Based on the same inventive concept, according to the neural network model training method provided in the above embodiment of the present invention, correspondingly, another embodiment of the present invention further provides a neural network model training device, a schematic structural diagram of which is shown in fig. 6a, and specifically includes:
themodel training module 601 is configured to input sample environment information included in a training sample set into a neural network model to be trained to obtain an output result of the neural network model to be trained, where the training sample set includes the sample environment information and a known display result indicating whether to display product information of a sample product, and the sample environment information indicates an operation condition of a specific user terminal;
acondition determining module 602, configured to determine whether a preset training ending condition is met; if yes, determining the current neural network model to be trained as the neural network model obtained by training; if not, adjusting the neural network model to be trained to obtain a new neural network model to be trained, and starting the next training.
Further, the sample environment information includes at least one of the following information:
information specifying an application program run by the user terminal;
load information of a designated user terminal;
specifying network usage information of a user terminal;
run time information of the user terminal is specified.
Further, themodel training module 601 is specifically configured to input sample environment information included in the training sample set and feature information of the sample product into the neural network model to be trained, so as to obtain an output result of the neural network model to be trained; the training sample set also contains characteristic information of the sample product.
Further, the characteristic information of the sample product includes at least one of the following information:
type information of the sample product;
name information of the sample product;
content information of the sample product, the content information including: text content information and/or image content information.
Further, as shown in fig. 6b, the neural network model training apparatus further includes:
asample obtaining module 603, configured to obtain a training sample set by:
when sample product information is displayed in a specified application program of a specified user terminal, recording environment information of the specified user terminal as sample environment information;
monitoring whether a preset operation is executed;
when the preset operation is monitored to be executed, determining a display result corresponding to the sample environment information as display product information;
and when the preset operation is not executed, determining that the display result corresponding to the sample environment information is not the product information.
Further, thesample obtaining module 603 is specifically configured to obtain the training sample set by using the following steps:
when sample product information is displayed in a specified application program of a specified user terminal, recording environment information of the specified user terminal as sample environment information;
monitoring whether a preset operation is executed;
when the preset operation is monitored to be executed, determining the display result corresponding to the sample environment information and the characteristic information of the sample product as display product information;
and when the preset operation is not executed, determining that the display result corresponding to the sample environment information and the characteristic information of the sample product is not the product information.
Further, thesample obtaining module 603 is specifically configured to monitor whether the specified application program is not unloaded after a preset time period, with a time point when the specified application program starts to display the sample product information as an initial time; or,
monitoring whether the sample product information is clicked.
Based on the same inventive concept, according to the product information display method provided by the above embodiment of the present invention, correspondingly, the embodiment of the present invention further provides an electronic device, as shown in fig. 7, which includes aprocessor 701, acommunication interface 702, amemory 703 and acommunication bus 704, wherein theprocessor 701, thecommunication interface 702 and thememory 703 complete mutual communication through thecommunication bus 704,
amemory 703 for storing a computer program;
theprocessor 701 is configured to implement the following steps when executing the program stored in the memory 703:
acquiring environment information of a user terminal, wherein the environment information represents the operation condition of the user terminal;
inputting characteristic information and the environmental information of a product to which information of a product to be displayed belongs into a pre-trained neural network model to obtain an output result indicating whether the information of the product to be displayed is displayed or not, wherein the neural network model is obtained by training based on a training sample set, and the training sample set comprises sample environmental information, characteristic information of the sample product and a known display result indicating whether the information of the product of the sample product is displayed or not;
and when the output result shows that the information of the product to be displayed is displayed, displaying the information of the product to be displayed.
Based on the same inventive concept, according to the neural network model training method provided by the above embodiment of the present invention, correspondingly, the embodiment of the present invention further provides an electronic device, as shown in fig. 8, which includes aprocessor 801, acommunication interface 802, amemory 803 and acommunication bus 804, wherein theprocessor 801, thecommunication interface 802 and thememory 803 complete mutual communication through thecommunication bus 804,
amemory 803 for storing a computer program;
theprocessor 801 is configured to implement the following steps when executing the program stored in the memory 803:
inputting sample environment information contained in a training sample set into a neural network model to be trained to obtain an output result of the neural network model to be trained, wherein the training sample set contains sample environment information and a known display result indicating whether product information of a sample product is displayed or not, and the sample environment information indicates the operation condition of a specified user terminal;
determining whether a preset training ending condition is met;
if yes, determining the current neural network model to be trained as the neural network model obtained by training;
if not, adjusting the neural network model to be trained to obtain a new neural network model to be trained, and starting the next training.
The communication bus mentioned in the electronic device 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. For ease of illustration, only one thick line is shown, but this does not mean that there is only one bus or one type of bus.
The communication interface is used for communication between the electronic equipment and other equipment.
The Memory may include a Random Access Memory (RAM) or a Non-Volatile Memory (NVM), 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, including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but 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, discrete Gate or transistor logic device, discrete hardware component.
In another embodiment of the present invention, a computer-readable storage medium is further provided, in which a computer program is stored, and the computer program, when executed by a processor, implements the steps of any of the above product information presentation methods.
In another embodiment of the present invention, a computer program product containing instructions is provided, which when run on a computer causes the computer to execute any of the above-mentioned product information presentation methods.
In yet another embodiment of the present invention, a computer-readable storage medium is further provided, in which a computer program is stored, and the computer program, when executed by a processor, implements the steps of any of the above neural network model training methods.
In yet another embodiment, a computer program product containing instructions is also provided, which when run on a computer causes the computer to perform any of the neural network model training methods of the above embodiments.
In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, cause the processes or functions described in accordance with the embodiments of the invention to occur, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, from one website site, computer, server, or data center to another website site, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device, such as a server, a data center, etc., that incorporates one or more of the available media. The usable medium may be a magnetic medium (e.g., floppy Disk, hard Disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., Solid State Disk (SSD)), among others.
It is noted that, herein, 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.
All the embodiments in the present specification are described in a related manner, and the same and similar parts among the embodiments may be referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, as for the device, the electronic apparatus and the storage medium embodiments, since they are substantially similar to the method embodiments, the description is relatively simple, and the relevant points can be referred to the partial description of the method embodiments.
The above description is only for the preferred embodiment of the present invention, and is not intended to limit the scope of the present invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention shall fall within the protection scope of the present invention.
Claims (10)
1. A product information display method is characterized by comprising the following steps:
acquiring environment information of a user terminal, wherein the environment information represents the operation condition of the user terminal;
inputting the environmental information into a pre-trained neural network model to obtain an output result indicating whether to display the information of the product to be displayed;
and when the output result shows that the information of the product to be displayed is displayed, displaying the information of the product to be displayed.
2. The method of claim 1, wherein inputting the environmental information into a pre-trained neural network model to obtain an output result indicating whether to display information of a product to be displayed comprises:
and inputting the environmental information and the characteristic information of the product to which the information of the product to be displayed belongs into a pre-trained neural network model to obtain an output result indicating whether the information of the product to be displayed is displayed or not.
3. A neural network model training method is characterized by comprising the following steps:
inputting sample environment information contained in a training sample set into a neural network model to be trained to obtain an output result of the neural network model to be trained, wherein the training sample set contains sample environment information and a known display result indicating whether product information of a sample product is displayed or not, and the sample environment information indicates the operation condition of a specified user terminal;
determining whether a preset training ending condition is met;
if yes, determining the current neural network model to be trained as the neural network model obtained by training;
if not, adjusting the neural network model to be trained to obtain a new neural network model to be trained, and starting the next training.
4. The method according to claim 5, wherein inputting the sample environment information included in the training sample set into the neural network model to be trained to obtain an output result of the neural network model to be trained, comprises:
inputting the sample environment information contained in the training sample set and the characteristic information of the sample product into a neural network model to be trained to obtain an output result of the neural network model to be trained;
the training sample set also contains characteristic information of the sample product.
5. The method of claim 5, wherein the training sample set is obtained by:
when sample product information is displayed in a specified application program of a specified user terminal, recording environment information of the specified user terminal as sample environment information;
monitoring whether a preset operation is executed;
when the preset operation is monitored to be executed, determining that a display result corresponding to the sample environment information is display product information;
and when the preset operation is monitored not to be executed, determining that the display result corresponding to the sample environment information is not to display the product information.
6. The method of claim 7, wherein the training sample set is obtained by:
when sample product information is displayed in a specified application program of a specified user terminal, recording environment information of the specified user terminal as sample environment information;
monitoring whether a preset operation is executed;
when the preset operation is monitored to be executed, determining that the display result corresponding to the sample environment information and the characteristic information of the sample product is display product information;
and when the preset operation is monitored not to be executed, determining that the display result corresponding to the sample environment information and the characteristic information of the sample product is not to display product information.
7. A product information display device, comprising:
the system comprises an information acquisition module, a processing module and a processing module, wherein the information acquisition module is used for acquiring environment information of a user terminal, and the environment information represents the running condition of the user terminal;
the information processing module is used for inputting the environmental information into a pre-trained neural network model to obtain an output result which represents whether the information of the product to be displayed is displayed or not;
and the information display module is used for displaying the information of the product to be displayed when the output result shows that the information of the product to be displayed is displayed.
8. A neural network model training device, comprising:
the model training module is used for inputting sample environment information contained in a training sample set into a neural network model to be trained to obtain an output result of the neural network model to be trained, wherein the training sample set contains the sample environment information and a known display result indicating whether product information of a sample product is displayed or not, and the sample environment information indicates the running condition of a specified user terminal;
the condition determining module is used for determining whether a preset training finishing condition is met; if yes, determining the current neural network model to be trained as the neural network model obtained by training; if not, adjusting the neural network model to be trained to obtain a new neural network model to be trained, and starting the next training.
9. An electronic device is characterized by comprising a processor, a communication interface, a memory and a communication bus, wherein the processor and the communication interface are used for realizing mutual communication by the memory through the communication bus;
a memory for storing a computer program;
a processor for implementing the method steps of any of claims 1-2 when executing a program stored in the memory.
10. An electronic device is characterized by comprising a processor, a communication interface, a memory and a communication bus, wherein the processor and the communication interface are used for realizing mutual communication by the memory through the communication bus;
a memory for storing a computer program;
a processor for implementing the method steps of any of claims 3 to 6 when executing a program stored in the memory.
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