CN111898018B - Virtual resource sending method and device, electronic equipment and storage medium - Google Patents
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Abstract
The application provides a method, a device, electronic equipment and a storage medium for sending virtual resources, which are used for acquiring user characteristic data, resource characteristic data and historical behavior data; processing the historical sequence information constructed according to the historical behavior data by using a cyclic neural network model to obtain hidden characteristics; inputting the user characteristic data, the resource characteristic data and the hidden characteristic into a pre-trained prediction model for calculation to obtain the use probability of the virtual resource by the target user; and sending the virtual resource to the target user according to the use probability. The virtual resource sent by the technical scheme is based on the use probability of the target user, namely, only the virtual resource with higher use probability is sent to the target user, so that the target user receives the virtual resource really needed by the target user.
Description
Technical Field
The disclosure relates to the technical field of internet, and in particular relates to a method and a device for sending virtual resources, electronic equipment and a storage medium.
Background
With the increasing development of technology and the popularization of the internet, more and more people consume the internet, such as online shopping, online purchasing services, and the like. Merchants and consumer platforms often attract users by sending a variety of virtual resources, such as coupons, to users.
However, at present, virtual resources are issued indiscriminately, and some users basically don't see the transmitted virtual resources due to their habits, so that the transmission of the virtual resources is meaningless, and therefore, the current virtual resource transmission method cannot enable the target user to receive the virtual resources really needed.
Disclosure of Invention
In order to overcome the problems in the related art, the present disclosure provides a method, an apparatus, an electronic device, and a storage medium for transmitting virtual resources.
In a first aspect, a method for sending a virtual resource is provided, including:
Acquiring user characteristic data of a target user, resource characteristic data of a virtual resource and historical behavior data of the target user on the virtual resource;
Constructing historical sequence information according to the historical behavior data, and processing the historical sequence information by using a cyclic neural network model to obtain hidden features related to the historical sequence information;
Inputting the user characteristic data, the resource characteristic data and the hidden characteristic into a pre-trained prediction model for calculation to obtain the use probability of the target user on the virtual resource;
and sending the virtual resource to the target user according to the use probability.
Optionally, the method further comprises:
The recurrent neural network model is trained by forward propagation and backward propagation.
Optionally, the forward propagation process includes updating a forget gate output, updating an input gate output, updating a cell state, updating an output gate output, and updating a current sequence index output.
Optionally, the back propagation process includes:
Acquiring an output value of each neuron in the forward propagation process;
Performing reverse calculation according to the output value to obtain an error term value of each neuron;
and calculating the gradient of the weight of each neuron according to the error term value.
Optionally, the method further comprises:
Acquiring a training sample set, wherein the training sample set comprises a positive sample and a negative sample;
and training the model according to the training sample set to obtain a decision tree, and pruning the decision tree to obtain the prediction model.
Alternatively, when the target user is allowed to hold only one virtual resource, the positive sample includes virtual resources that are effectively used, and the negative sample includes virtual resources that are not effectively used.
Optionally, when the target user is operated to hold a plurality of virtual resources, the positive sample is other virtual resources whose values are smaller than those of the virtual resources to be used in all the virtual resources, and the negative sample is other virtual resources whose values are larger than those of the virtual resources to be used in the virtual resources.
In a second aspect, there is provided a transmission apparatus for virtual resources, including:
The data acquisition module is configured to acquire user characteristic data of a target user, resource characteristic data of a virtual resource and historical behavior data of the target user on the virtual resource;
The first calculation module is configured to construct historical sequence information according to the historical behavior data, and the historical sequence information is processed by using a cyclic neural network model to obtain hidden features related to the historical sequence information;
the second calculation module is configured to input the user characteristic data, the resource characteristic data and the hidden characteristic into a pre-trained prediction model for calculation, so as to obtain the use probability of the virtual resource by the target user;
And the transmission execution module is configured to transmit the virtual resource to the target user according to the use probability.
Optionally, the method further comprises:
a first training module is configured to train the recurrent neural network model by forward propagation and backward propagation.
Optionally, the forward propagation process includes updating a forget gate output, updating an input gate output, updating a cell state, updating an output gate output, and updating a current sequence index output.
Optionally, the back propagation process includes:
Acquiring an output value of each neuron in the forward propagation process;
Performing reverse calculation according to the output value to obtain an error term value of each neuron;
and calculating the gradient of the weight of each neuron according to the error term value.
Optionally, the method further comprises:
a sample acquisition module configured to acquire a training sample set, the training sample set comprising a positive sample and a negative sample;
and the second training module is configured to perform model training according to the training sample set to obtain a decision tree, and trim the decision tree to obtain the prediction model.
Alternatively, when the target user is allowed to hold only one virtual resource, the positive sample includes virtual resources that are effectively used, and the negative sample includes virtual resources that are not effectively used.
Optionally, when the target user is operated to hold a plurality of virtual resources, the positive sample is other virtual resources whose values are smaller than those of the virtual resources to be used in all the virtual resources, and the negative sample is other virtual resources whose values are larger than those of the virtual resources to be used in the virtual resources.
In a third aspect, there is provided an electronic device comprising:
A processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to perform the transmission method according to the first aspect.
In a fourth aspect, there is provided a non-transitory computer readable storage medium, which when executed by a processor of a mobile terminal, causes the mobile terminal to perform the transmission method according to the first aspect.
In a fifth aspect, a computer program product is provided for performing the transmission method according to the first aspect.
The technical scheme provided by the embodiment of the disclosure can comprise the following beneficial effects: the virtual resource sent by the technical scheme is based on the use probability of the target user, namely, only the virtual resource with higher use probability is sent to the target user, so that the target user receives the virtual resource really needed by the target user.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
FIG. 1 is a flow chart illustrating a method of transmitting virtual resources according to an exemplary embodiment;
FIG. 2 is a flow chart illustrating another method of transmitting virtual resources according to an exemplary embodiment;
FIG. 3 is a block diagram of a transmitting device of a virtual resource, according to an example embodiment;
FIG. 4a is a block diagram of another virtual resource transmitting apparatus according to an exemplary embodiment;
FIG. 4b is a block diagram of a transmitting device of yet another virtual resource, according to an example embodiment;
FIG. 5 is a block diagram of an electronic device, shown in accordance with an exemplary embodiment;
fig. 6 is a block diagram of another electronic device, shown in accordance with an exemplary embodiment.
Detailed Description
Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples do not represent all implementations consistent with the invention. Rather, they are merely examples of apparatus and methods consistent with aspects of the invention as detailed in the accompanying claims.
Fig. 1 is a flowchart illustrating a method of transmitting virtual resources according to an exemplary embodiment.
As shown in fig. 1, the method for sending virtual resources provided in this embodiment is used in a server of an internet-based consumption platform, where the consumption platform may be understood as a network purchase platform that provides a commodity purchase service or a service purchase service, and the sending method specifically includes the following steps.
S1, acquiring user characteristic data, resource characteristic data and historical behavior data.
By target user is meant a general user of the consumption platform or a particular user of the plurality of users selected to push virtual resources thereto. The user characteristic data is characteristic data of a target user, the resource characteristic data refers to characteristic data of virtual resources such as coupons, and the historical behavior data refers to behavior data of the target user on the virtual resources.
The user characteristic data comprise data such as user gender, age, platform activity days, fan number and the like of a target user; the resource characteristic data comprise data such as the amount of the virtual resource, the discount of the virtual resource, the preferential amount and the like; the historical behavior data includes interactive features such as the number of times the user has historically received the virtual resource, such as coupons, the number of times the user has used the virtual resource, the rate of cancellation of the virtual resource, and the like.
The historical behavior data are integrated in the historical behavior sequence information, and specifically comprise virtual resource information of historical consumption of a target user.
S2, processing the historical sequence information constructed according to the historical behavior data by using a cyclic neural network model.
The method comprises the steps of constructing historical sequence information according to the historical behavior data, and then processing the historical sequence information by utilizing a pre-trained cyclic neural network model so as to obtain hidden features related to the historical sequence information.
The recurrent neural network model of the present application is preferably an LSTM network model. The LSTM network is a time recurrent neural network, is a special RNN network, and can learn long-term dependency information. All RNNs have a chained form of repeating neural network modules. In a standard RNN, this repeated module has only a very simple structure, such as a tanh layer. Whereas in LSTM networks, the duplicated modules have a different structure.
And S3, calculating the user characteristic data, the resource characteristic data and the hidden characteristic by using a prediction model.
Specifically, user characteristic data, resource characteristic data and hidden characteristics are input into a pre-trained prediction model, so that the use probability of the virtual resource by the target user is obtained, and the use probability is in [0,1 ]. Specifically, the usage probability includes a usage probability of the virtual resource by the target user, or a usage probability of the different virtual resource by the target user.
And S4, sending the virtual resources to the target user according to the use probability.
And after determining the use probability of the corresponding virtual resource by the target user, sending the corresponding virtual resource to the target user according to the use probability. Specifically, the virtual resource with higher use probability can be sent to the target user, and the virtual resource with lower use probability is not sent to the target user, so that the value of the corresponding virtual resource is realized, and the income of a consumption platform is improved.
From the above technical solution, it can be seen that this embodiment provides a method for sending virtual resources, specifically, obtaining user feature data, resource feature data and historical behavior data; processing the historical sequence information constructed according to the historical behavior data by using a cyclic neural network model to obtain hidden characteristics; inputting the user characteristic data, the resource characteristic data and the hidden characteristic into a pre-trained prediction model for calculation to obtain the use probability of the virtual resource by the target user; and sending the virtual resource to the target user according to the use probability. The virtual resource sent by the technical scheme is based on the use probability of the target user, namely, only the virtual resource with higher use probability is sent to the target user, so that the target user receives the virtual resource really needed by the target user.
In addition, the embodiment further comprises a prediction model training step, specifically training the cyclic neural network model, such as an LSTM network, through forward propagation and backward propagation modes.
In the structure of the LSTM network, three gating structures, namely a forget gate, an input gate and an output gate, are included. The forgetting gate controls whether to forget the hidden cell state of the upper layer or not with a certain probability in the LSTM structure.
The LSTM network can avoid the problem of gradient disappearance of the standard RNN network through the three gate structure. In the invention, the network can be used for processing the historical behavior sequence information of the user, wherein the historical behavior of the user is input, the historical behavior comprises coupon information of historical consumption of the user, the coupon information is output as the extracted hidden feature, and the hidden feature is the feature which is related to the historical behavior sequence information of the user and is difficult to intuitively extract, and the hidden feature is used in a coupon issuing model.
From the block diagram of the LSTM network, its forward propagation process can be divided into the following phases: updating the forget gate output, updating the input gate output, updating the cell state, updating the output gate output, and updating the current sequence index prediction output.
In LSTM back propagation, the basic idea is to iteratively update all parameters by gradient descent, and wherein the key is to calculate the partial derivatives of all parameters based on a loss function. The propagation process mainly comprises the following steps: firstly, obtaining an output value of each neuron in a forward propagation process; then, the error term value of each neuron is reversely calculated according to each output value, and the reverse propagation of the LSTM error term also comprises two directions, which are the same as the standard RNN: firstly, back propagation along time, namely, calculating an error term of each moment from the current moment t; the other is to propagate the error term to the upper layer. And finally, calculating the gradient of the weight of each neuron according to the corresponding error term value.
Fig. 2 is a flowchart illustrating another method of transmitting virtual resources according to an exemplary embodiment.
As shown in fig. 2, the method for sending virtual resources provided in this embodiment specifically includes the following steps.
S01, acquiring a training sample set.
The training sample set includes positive samples and negative samples. The positive and negative sample designs are different according to the number of virtual resources the user is allowed to possess. If the user is only allowed to own one virtual resource, then the design sample is as follows: in the history issuing data of the effective virtual resource, the effective virtual resource refers to that a certain virtual resource is issued and received by a user (the user knows that the virtual resource exists), and if the virtual resource is used by the user, the effective virtual resource is a positive sample; and vice versa is a negative example.
If the user is allowed to own multiple virtual resources, the sample design is as follows: in the effective virtual resource history issuing data, if a certain virtual resource is used by a user, all virtual resources smaller than the amount of the virtual resource are regarded as positive samples (the user uses the virtual resource because the virtual resource has great preference, and if the virtual resource does not exist, other virtual resources meeting the conditions are also used); and vice versa is a negative example.
S02, training a prediction model according to the training sample set.
After the training sample set comprising the positive sample and the negative sample is obtained, model training is carried out according to the training sample set, and the prediction model is obtained. Model training is carried out according to the training sample set to obtain a corresponding decision tree, and the decision tree is trimmed to obtain a corresponding prediction model.
S1, acquiring user characteristic data, resource characteristic data and historical behavior data.
By target user is meant a general user of the consumption platform or a particular user of the plurality of users selected to push virtual resources thereto. The user characteristic data is characteristic data of a target user, the resource characteristic data refers to characteristic data of virtual resources such as coupons, and the historical behavior data refers to behavior data of the target user on the virtual resources.
The user characteristic data comprise data such as user gender, age, platform activity days, fan number and the like of a target user; the resource characteristic data comprise data such as the amount of the virtual resource, the discount of the virtual resource, the preferential amount and the like; the historical behavior data includes interactive features such as the number of times the user has historically received the virtual resource, such as coupons, the number of times the user has used the virtual resource, the rate of cancellation of the virtual resource, and the like.
The historical behavior data are integrated in the historical behavior sequence information, and specifically comprise virtual resource information of historical consumption of a target user.
S2, processing the historical sequence information constructed according to the historical behavior data by using a cyclic neural network model.
The method comprises the steps of constructing historical sequence information according to the historical behavior data, and then processing the historical sequence information by utilizing a pre-trained cyclic neural network model so as to obtain hidden features related to the historical sequence information.
And S3, calculating the user characteristic data, the resource characteristic data and the hidden characteristic by using a prediction model.
Specifically, user characteristic data, resource characteristic data and hidden characteristics are input into a pre-trained prediction model, so that the use probability of the virtual resource by the target user is obtained, and the use probability is in [0,1 ]. Specifically, the usage probability includes a usage probability of the virtual resource by the target user, or a usage probability of the different virtual resource by the target user.
And S4, sending the virtual resources to the target user according to the use probability.
And after determining the use probability of the corresponding virtual resource by the target user, sending the corresponding virtual resource to the target user according to the use probability. Specifically, the virtual resource with higher use probability can be sent to the target user, and the virtual resource with lower use probability is not sent to the target user, so that the value of the corresponding virtual resource is realized, and the income of a consumption platform is improved.
From the above technical solution, it can be seen that this embodiment provides a method for sending virtual resources, specifically, obtaining user feature data, resource feature data and historical behavior data; processing the historical sequence information constructed according to the historical behavior data by using a cyclic neural network model to obtain hidden characteristics; inputting the user characteristic data, the resource characteristic data and the hidden characteristic into a pre-trained prediction model for calculation to obtain the use probability of the virtual resource by the target user; and sending the virtual resource to the target user according to the use probability. The virtual resource sent by the technical scheme is based on the use probability of the target user, namely, only the virtual resource with higher use probability is sent to the target user, so that the target user receives the virtual resource really needed by the target user. In addition, compared with the previous embodiment, the embodiment has the advantages that the model training process is additionally arranged, so that the technical scheme has a directly available prediction model, and additional model training is not needed, thereby improving the prediction efficiency.
Fig. 3 is a block diagram illustrating a transmitting apparatus of a virtual resource according to an exemplary embodiment.
As shown in fig. 3, the sending device of the virtual resource provided in this embodiment is used in a server of an internet-based consumption platform, where the consumption platform may be understood as a network purchase platform that provides a commodity purchase service or a service purchase service, and the sending device specifically includes a data acquisition module 10, a first calculation module 20, a second calculation module 30, and a sending execution module 40.
The data acquisition module is configured to acquire user characteristic data, resource characteristic data, and historical behavior data.
By target user is meant a general user of the consumption platform or a particular user of the plurality of users selected to push virtual resources thereto. The user characteristic data is characteristic data of a target user, the resource characteristic data refers to characteristic data of virtual resources such as coupons, and the historical behavior data refers to behavior data of the target user on the virtual resources.
The user characteristic data comprise data such as gender, age, platform activity days, fan number and the like of a target user; the resource characteristic data comprise data such as the amount of the virtual resource, the discount of the virtual resource, the preferential amount and the like; the historical behavior data includes interactive features such as the number of times the user historically retrieves the virtual resource, such as coupons, the number of times the user uses the virtual resource, the rate at which the virtual resource is revoked, and the like.
The historical behavior data are integrated in the historical behavior sequence information, and specifically comprise virtual resource information of historical consumption of a target user.
The first computing module is configured to process historical sequence information constructed from historical behavioral data using a recurrent neural network model.
The method comprises the steps of constructing historical sequence information according to the historical behavior data, and then processing the historical sequence information by utilizing a pre-trained cyclic neural network model so as to obtain hidden features related to the historical sequence information.
The recurrent neural network model of the present application is preferably an LSTM network model. The LSTM network is a time recurrent neural network, is a special RNN network, and can learn long-term dependency information. All RNNs have a chained form of repeating neural network modules. In a standard RNN, this repeated module has only a very simple structure, such as a tanh layer. Whereas in LSTM networks, the duplicated modules have a different structure.
The second computing module is configured to compute user feature data, resource feature data, and hidden features using the predictive model.
Specifically, user characteristic data, resource characteristic data and hidden characteristics are input into a pre-trained prediction model, so that the use probability of the virtual resource by the target user is obtained, and the use probability is in [0,1 ]. Specifically, the usage probability includes a usage probability of the virtual resource by the target user, or a usage probability of the different virtual resource by the target user.
The transmission execution module is configured to transmit the virtual resource to the target user according to the probability of use.
And after determining the use probability of the corresponding virtual resource by the target user, sending the corresponding virtual resource to the target user according to the use probability. Specifically, the virtual resource with higher use probability can be sent to the target user, and the virtual resource with lower use probability is not sent to the target user, so that the value of the corresponding virtual resource is realized, and the income of a consumption platform is improved.
As can be seen from the above technical solution, the present embodiment provides a device for sending virtual resources, specifically, obtaining user feature data, resource feature data and historical behavior data; processing the historical sequence information constructed according to the historical behavior data by using a cyclic neural network model to obtain hidden characteristics; inputting the user characteristic data, the resource characteristic data and the hidden characteristic into a pre-trained prediction model for calculation to obtain the use probability of the virtual resource by the target user; and sending the virtual resource to the target user according to the use probability. The virtual resource sent by the technical scheme is based on the use probability of the target user, namely, only the virtual resource with higher use probability is sent to the target user, so that the target user receives the virtual resource really needed by the target user.
In addition, as shown in fig. 4a, the present embodiment further includes a first training module 50. The first training module trains the recurrent neural network model, such as an LSTM network, by way of forward propagation and reverse propagation.
In the structure of the LSTM network, three gating structures, namely a forget gate, an input gate and an output gate, are included. The forgetting gate controls whether to forget the hidden cell state of the upper layer or not with a certain probability in the LSTM structure.
The LSTM network can avoid the problem of gradient disappearance of the standard RNN network through the three gate structure. In the invention, the network can be used for processing the historical behavior sequence information of the user, wherein the historical behavior of the user is input, the historical behavior comprises coupon information of historical consumption of the user, the coupon information is output as the extracted hidden feature, and the hidden feature is the feature which is related to the historical behavior sequence information of the user and is difficult to intuitively extract, and the hidden feature is used in a coupon issuing model.
From the block diagram of the LSTM network, its forward propagation process can be divided into the following phases: updating the forget gate output, updating the input gate output, updating the cell state, updating the output gate output, and updating the current sequence index prediction output.
In LSTM back propagation, the basic idea is to iteratively update all parameters by gradient descent, and wherein the key is to calculate the partial derivatives of all parameters based on a loss function. The propagation process mainly comprises the following steps: firstly, obtaining an output value of each neuron in a forward propagation process; then, the error term value of each neuron is reversely calculated according to each output value, and the reverse propagation of the LSTM error term also comprises two directions, which are the same as the standard RNN: firstly, back propagation along time, namely, calculating an error term of each moment from the current moment t; the other is to propagate the error term to the upper layer. And finally, calculating the gradient of the weight of each neuron according to the corresponding error term value.
In addition, as shown in fig. 4b, the present embodiment further includes a sample acquisition module 60 and a second training module 70.
The sample acquisition module is configured to acquire a training sample set.
The training sample set includes positive samples and negative samples. The positive and negative sample designs are different according to the number of virtual resources the user is allowed to possess. If the user is only allowed to own one virtual resource, then the design sample is as follows: in the history issuing data of the effective virtual resource, the effective virtual resource refers to that a certain virtual resource is issued and received by a user (the user knows that the virtual resource exists), and if the virtual resource is used by the user, the effective virtual resource is a positive sample; and vice versa is a negative example.
If the user is allowed to own multiple virtual resources, the sample design is as follows: in the effective virtual resource history issuing data, if a certain virtual resource is used by a user, all virtual resources smaller than the amount of the virtual resource are regarded as positive samples (the user uses the virtual resource because the virtual resource has great preference, and if the virtual resource does not exist, other virtual resources meeting the conditions are also used); and vice versa is a negative example.
The second training module is configured to train the predictive model based on the training sample set.
After the training sample set comprising the positive sample and the negative sample is obtained, model training is carried out according to the training sample set, and the prediction model is obtained. Model training is carried out according to the training sample set to obtain a corresponding decision tree, and the decision tree is trimmed to obtain a corresponding prediction model.
The present application also provides a computer program comprising a method of transmitting virtual resources as shown in fig. 1 or fig. 2.
Fig. 5 is a block diagram of an electronic device, according to an example embodiment.
For example, electronic device 500 may be a mobile terminal such as a mobile phone, computer, digital broadcast terminal, messaging device, game console, tablet device, medical device, exercise device, personal digital assistant, or the like.
Referring to fig. 5, an electronic device 500 may include one or more of the following components: a processing component 502, a memory 504, a power component 506, a multimedia component 509, an audio component 510, an input/output (I/O) interface 512, a sensor component 514, and a communication component 516.
The processing component 502 generally controls overall operation of the electronic device 500, such as operations associated with display, telephone calls, data communications, camera operations, and recording operations. The processing component 502 may include one or more processors 520 to execute instructions to perform all or part of the steps of the methods described above. Further, the processing component 502 can include one or more modules that facilitate interactions between the processing component 502 and other components. For example, the processing component 502 can include a multimedia module to facilitate interaction between the multimedia component 509 and the processing component 502.
Memory 504 is configured to store various types of data to support operations at device 500. Examples of such data include instructions for any application or method operating on the electronic device 500, contact data, phonebook data, messages, pictures, videos, and so forth. The memory 504 may be implemented by any type or combination of volatile or nonvolatile memory devices such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disk.
The power supply component 506 provides power to the various components of the electronic device 500. The power components 506 may include a power management system, one or more power sources, and other components associated with generating, managing, and distributing power for the electronic device 500.
The multimedia component 509 includes a screen between the electronic device 500 and the user that provides an output interface. In some embodiments, the screen may include a Liquid Crystal Display (LCD) and a Touch Panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from a user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensor may sense not only the boundary of a touch or slide action, but also the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 509 includes a front-facing camera and/or a rear-facing camera. The front-facing camera and/or the rear-facing camera may receive external multimedia data when the device 500 is in an operational mode, such as a shooting mode or a video mode. Each front camera and rear camera may be a fixed optical lens system or have focal length and optical zoom capabilities.
The audio component 510 is configured to output and/or input audio signals. For example, the audio component 510 includes a Microphone (MIC) configured to receive external audio signals when the electronic device 500 is in an operational mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signals may be further stored in the memory 504 or transmitted via the communication component 516. In some embodiments, the audio component 510 further comprises a speaker for outputting audio signals.
The I/O interface 512 provides an interface between the processing component 502 and peripheral interface modules, which may be keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to: homepage button, volume button, start button, and lock button.
The sensor assembly 514 includes one or more sensors for providing status assessment of various aspects of the electronic device 500. For example, the sensor assembly 514 may detect the on/off state of the device 500, the relative positioning of components, such as a display and keypad of the electronic device 500, the sensor assembly 514 may also detect a change in position of the electronic device 500 or a component of the electronic device 500, the presence or absence of a user's contact with the electronic device 500, the orientation or acceleration/deceleration of the electronic device 500, and a change in temperature of the electronic device 500. The sensor assembly 514 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. The sensor assembly 514 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 514 may also include an acceleration sensor, a gyroscopic sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.
The communication component 516 is configured to facilitate communication between the electronic device 500 and other devices, either wired or wireless. The electronic device 500 may access a wireless network based on a communication standard, such as WiFi, an operator network (e.g., 2G, 3G, 4G, or 5G), or a combination thereof. In one exemplary embodiment, the communication component 516 receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 516 further includes a Near Field Communication (NFC) module to facilitate short range communications. For example, the NFC module may be implemented based on Radio Frequency Identification (RFID) technology, infrared data association (IrDA) technology, ultra Wideband (UWB) technology, bluetooth (BT) technology, and other technologies.
In an exemplary embodiment, the electronic device 500 may be implemented by 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), controllers, microcontrollers, microprocessors, or other electronic elements for executing the transmission method as shown in fig. 1 or 2.
In an exemplary embodiment, a non-transitory computer readable storage medium is also provided, such as memory 504, including instructions executable by processor 520 of electronic device 500 to perform the above-described method. For example, the non-transitory computer readable storage medium may be ROM, random Access Memory (RAM), CD-ROM, magnetic tape, floppy disk, optical data storage device, etc.
Fig. 6 is a block diagram of another electronic device, shown in accordance with an exemplary embodiment.
For example, the electronic device 600 may be provided as a server. Referring to fig. 6, the electronic device 600 includes a processing component 622 that further includes one or more processors and memory resources represented by a memory 632 for storing instructions, such as application programs, executable by the processing component 622. The application programs stored in memory 632 may include one or more modules each corresponding to a set of instructions. Further, the processing component 622 is configured to execute instructions to perform the transmission method shown in fig. 1 or fig. 2.
The electronic device 600 may also include a power component 626 configured to perform power management of the electronic device 600, a wired or wireless network interface 650 configured to connect the electronic device 600 to a network, and an input-output (I/O) interface 658. The electronic device 600 may operate based on an operating system stored in the memory 632, such as Windows Server, mac OS XTM, unixTM, linuxTM, freeBSDTM, or the like.
Other embodiments of the application will be apparent to those skilled in the art from consideration of the specification and practice of the application disclosed herein. This application is intended to cover any variations, uses, or adaptations of the application following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the application pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the application being indicated by the following claims.
It is to be understood that the invention is not limited to the precise arrangements and instrumentalities shown in the drawings, which have been described above, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the invention is limited only by the appended claims.
Claims (16)
1. A method for transmitting virtual resources, comprising:
acquiring user characteristic data of a target user, resource characteristic data of a virtual resource and historical behavior data of the target user on the virtual resource; the user characteristic data comprise the user gender, age, platform activity days and fan number of the target user;
constructing historical sequence information according to the historical behavior data, and processing the historical sequence information by using a cyclic neural network model to obtain hidden features related to the historical sequence information; the hidden features are difficult to intuitively extract;
Inputting the user characteristic data, the resource characteristic data and the hidden characteristic into a pre-trained prediction model for calculation to obtain the use probability of the target user on the virtual resource;
and sending the virtual resource to the target user according to the use probability.
2. The transmission method according to claim 1, further comprising:
The recurrent neural network model is trained by forward propagation and backward propagation.
3. The transmission method of claim 2, wherein the forward propagation process includes updating a forget gate output, updating an input gate output, updating a cell state, updating an output gate output, and updating a current sequence index output.
4. The transmission method of claim 2, wherein the back propagation process comprises:
Acquiring an output value of each neuron in the forward propagation process;
Performing reverse calculation according to the output value to obtain an error term value of each neuron;
and calculating the gradient of the weight of each neuron according to the error term value.
5. The transmission method according to claim 1, further comprising:
Acquiring a training sample set, wherein the training sample set comprises a positive sample and a negative sample;
and training the model according to the training sample set to obtain a decision tree, and pruning the decision tree to obtain the prediction model.
6. The transmission method of claim 5, wherein the positive sample includes virtual resources that are effectively used and the negative sample includes virtual resources that are not effectively used when the target user is allowed to hold only one virtual resource.
7. The transmission method according to claim 5, wherein when the target user is operated with a plurality of virtual resources, the positive sample is the other virtual resources having a smaller value than the virtual resource used among all the virtual resources, and the negative sample is the other virtual resources having a larger value than the virtual resource used among the virtual resources.
8. A virtual resource transmitting apparatus, comprising:
the data acquisition module is configured to acquire user characteristic data of a target user, resource characteristic data of a virtual resource and historical behavior data of the target user on the virtual resource; the user characteristic data comprise the user gender, age, platform activity days and fan number of the target user;
the first calculation module is configured to construct historical sequence information according to the historical behavior data, and the historical sequence information is processed by using a cyclic neural network model to obtain hidden features related to the historical sequence information; the hidden features are difficult to intuitively extract;
the second calculation module is configured to input the user characteristic data, the resource characteristic data and the hidden characteristic into a pre-trained prediction model for calculation, so as to obtain the use probability of the virtual resource by the target user;
And the transmission execution module is configured to transmit the virtual resource to the target user according to the use probability.
9. The transmitting apparatus of claim 8, further comprising:
a first training module is configured to train the recurrent neural network model by forward propagation and backward propagation.
10. The transmitting device of claim 9, wherein the forward propagation procedure includes updating a forget gate output, updating an input gate output, updating a cell state, updating an output gate output, and updating a current sequence index output.
11. The transmitting apparatus of claim 9, wherein the back propagation process comprises:
Acquiring an output value of each neuron in the forward propagation process;
Performing reverse calculation according to the output value to obtain an error term value of each neuron;
and calculating the gradient of the weight of each neuron according to the error term value.
12. The transmitting apparatus of claim 8, further comprising:
a sample acquisition module configured to acquire a training sample set, the training sample set comprising a positive sample and a negative sample;
and the second training module is configured to perform model training according to the training sample set to obtain a decision tree, and trim the decision tree to obtain the prediction model.
13. The transmission apparatus of claim 12, wherein the positive sample includes virtual resources that are effectively used and the negative sample includes virtual resources that are not effectively used when the target user is allowed to hold only one virtual resource.
14. The transmission apparatus according to claim 12, wherein when the target user is operated with a plurality of virtual resources, the positive sample is other virtual resources of which values are smaller than those of the virtual resources used among all virtual resources, and the negative sample is other virtual resources of which values are larger than those of the virtual resources used among the virtual resources.
15. An electronic device, comprising:
A processor;
a memory for storing processor-executable instructions;
Wherein the processor is configured to perform the transmission method of any one of claims 1 to 7.
16. A non-transitory computer readable storage medium, which when executed by a processor of a mobile terminal, causes the mobile terminal to perform the transmission method of any of claims 1-7.
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| CN112308635A (en) * | 2020-11-25 | 2021-02-02 | 拉扎斯网络科技（上海）有限公司 | Data processing method and device and resource providing method and device |
| CN112511856B (en) * | 2020-11-30 | 2023-06-27 | 北京达佳互联信息技术有限公司 | Virtual resource pushing method, device and server |
| CN113822722A (en) * | 2021-09-28 | 2021-12-21 | 北京沃东天骏信息技术有限公司 | Virtual resource distribution control method and device and server |
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