CN117391757A - Method and device for determining resource feedback parameters and computer equipment - Google Patents
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Abstract
The disclosure relates to the technical field of big data, and particularly discloses a method, a device, computer equipment, a storage medium and a computer program product for determining resource feedback parameters. The method comprises the following steps: acquiring description information of a first resource and attribute information of a candidate first object; sampling feedback parameters of the first resources in batches, inputting description information of the first resources and attribute information of the candidate first objects into a preset identification model for prediction aiming at the feedback parameters of each batch, and determining the number of the matched candidate first objects as a matching result; further determining the resource increment of the second object under the condition that the second object interacts with the candidate first object; and determining a target feedback parameter as the feedback parameter in the description information of the first resource corresponding to the maximum resource increment in the plurality of batches. The accuracy of feedback parameters is improved, and the increase of the resources of the second object is ensured.
Description
Technical Field
The present invention relates to the field of big data technologies, and in particular, to a method, an apparatus, a computer device, a storage medium, and a computer program product for determining a resource feedback parameter.
Background
Before the first object and the second object perform resource interaction, the first object generally selects feedback resources with higher feedback rate, so that the interaction amount of the feedback resources is directly influenced by the feedback rate. If the feedback rate is set too high, the feedback amount of the feedback resource exceeds the expected feedback amount of the second object, which may result in a reduction of the resource of the second object. If the feedback rate is set too low, the resource interaction amount is reduced, so that the feedback of the second object based on the resource is reduced, and the resource of the second object is reduced. Therefore, it is necessary to set an appropriate feedback rate to increase the resources of the second object.
In the related art, the feedback rate of the first object is predicted based on the feedback rate prediction model by determining the characteristics related to the feedback rate. In the related art, the feedback rate is not accurately predicted, and the resources of the second object cannot be effectively increased.
Disclosure of Invention
In view of the foregoing, it is desirable to provide a method, an apparatus, a computer device, a computer readable storage medium and a computer program product for determining a resource feedback parameter.
In a first aspect, the present application provides a method for determining a resource feedback parameter. The method comprises the following steps:
Acquiring description information of a first resource and attribute information of a candidate first object; the first resource comprises a resource returned to the first object by the second object in response to the second resource sent by the first object;
sampling feedback parameters of the first resources in batches, inputting description information of the first resources and attribute information of the candidate first objects into a preset identification model for prediction aiming at the feedback parameters of each batch, outputting a matching result of the first resources and the candidate first objects, and determining the number of the candidate first objects with the matching result as the matching result; the description information of the first resource comprises feedback parameters;
determining the resource increment of the second object under the condition that the second object interacts with the candidate first object according to the description information of the first resource and the number of the candidate first objects matched by the matching result;
and determining a target feedback parameter as the feedback parameter in the description information of the first resource corresponding to the maximum resource increment in the plurality of batches.
In one embodiment, the training manner of the identification model includes:
Acquiring sample information; wherein the sample information includes: a matching relation is marked between the first sample object and the first sample resource;
constructing an initial training model, inputting the first object of the sample and the first resource of the sample into the initial training model, and outputting to obtain a prediction result;
and carrying out iterative adjustment on the initial training model based on the difference between the prediction result and the marked matching relation to obtain an identification model.
In one embodiment, sampling feedback parameters of the first resource in batches, and inputting, for each batch of feedback parameters, description information of the first resource and attribute information of the candidate first object into a preset recognition model to predict, where the method includes:
determining a maximum feedback parameter under the condition that the second object resource is increased according to the attribute information of the first resource;
dividing feedback parameters by a preset feedback parameter interval threshold to obtain a feedback parameter set;
and inputting the description information of the first resource and the attribute information of the candidate first object into an identification model aiming at the feedback parameter of each batch, wherein the feedback parameter in the description information of the first resource is obtained by batch sampling from a feedback parameter set.
In one embodiment, the feedback parameters in the description information of the first resource are adjusted and predicted according to the feedback parameter set, including:
predicting the matching probability of the candidate first object and the second object under each feedback parameter in the feedback parameter set;
under the condition that the matching probability is larger than a preset probability threshold, determining that the matching result of the candidate first object and the first resource is matching;
and determining the number of the candidate first objects matched with the matching result of the first resource.
In one embodiment, according to the description information of the first resource and the number of candidate first objects matched by the matching result, determining the resource increment of the second object under the condition that the second object interacts with the candidate first object includes:
determining a first resource total amount of first resources returned by the second object to the first objects according to the description information of the first resources and the number of candidate first objects matched by the matching result, and transmitting a second resource total amount of resources to the second object by the candidate first objects;
And determining the second object resource increment according to the first resource total amount and the second resource total amount.
In one embodiment, the determining the target feedback parameter is a feedback parameter in the description information of the first resource corresponding to the maximum increase of the resources in the plurality of batches, and includes:
acquiring feedback parameters of the batches and corresponding resource increment of the second object;
obtaining feedback parameters corresponding to the maximum resource increment of the second object by comparing the resource increment of a plurality of second objects;
and determining the feedback parameter corresponding to the maximum resource increment as the target feedback parameter.
The application also provides a device for determining the resource feedback parameters, which comprises:
the acquisition module is used for acquiring the description information of the first resource and the attribute information of the candidate first object; the first resource comprises a resource returned to the first object by the second object in response to the second resource sent by the first object;
the quantity determining module is used for sampling feedback parameters of the first resources in batches, inputting the description information of the first resources and the attribute information of the candidate first objects into a preset identification model for prediction aiming at the feedback parameters of each batch, outputting the matching result of the first resources and the candidate first objects, and determining the quantity of the candidate first objects with the matching result as the matching result; the description information of the first resource comprises feedback parameters;
The resource increasing module is used for determining the resource increasing amount of the second object under the condition that the second object interacts with the candidate first object according to the description information of the first resource and the number of the candidate first objects matched by the matching result;
and the target parameter module is used for determining that the target feedback parameter is the feedback parameter in the description information of the first resource corresponding to the maximum resource increment in the plurality of batches.
In one embodiment, the apparatus comprises:
the sample acquisition module is used for acquiring sample information; wherein the sample information includes: a matching relation is marked between the first sample object and the first sample resource;
the construction module is used for constructing an initial training model, inputting the first sample object and the first sample resource into the initial training model, and outputting a prediction result;
and the recognition model module is used for iteratively adjusting the initial training model based on the difference between the prediction result and the marked matching relation to obtain a recognition model.
In one embodiment, the determining number module includes:
A determining submodule, configured to determine a maximum feedback parameter in a case where the second object resource increases according to the attribute information of the first resource;
the segmentation sub-module is used for segmenting feedback parameters by a preset feedback parameter interval threshold value to obtain a feedback parameter set;
and the prediction sub-module is used for inputting the description information of the first resource and the attribute information of the candidate first object into the identification model aiming at the feedback parameter of each batch, wherein the feedback parameter in the description information of the first resource is obtained by batch sampling from a feedback parameter set.
In one embodiment, the determining number module includes:
the probability sub-module is used for predicting the matching probability of the candidate first object and the second object under each feedback parameter in the feedback parameter set;
the matching sub-module is used for determining that the matching result of the candidate first object and the first resource is matching under the condition that the matching probability is larger than a preset probability threshold;
and the number sub-module is used for determining the number of the candidate first objects matched with the matching result of the first resource.
In one embodiment, the resource adding module includes:
the total quantum module is used for determining the total amount of first resources returned by the second object to the first objects according to the description information of the first resources and the number of candidate first objects matched by the matching result, and the total amount of second resources of resources sent by the first objects to the second object;
and the adding quantum module is used for determining the second object resource adding amount according to the first resource total amount and the second resource total amount.
In one embodiment, the target parameter module includes:
the acquisition sub-module is used for acquiring feedback parameters of the batches and the corresponding resource increment of the second object;
the parameter sub-module is used for obtaining feedback parameters corresponding to the maximum resource increment of the second object by comparing the resource increment of the plurality of second objects;
and the target sub-module is used for determining the feedback parameter corresponding to the maximum resource increment as the target feedback parameter.
In a third aspect, the present application also provides a computer device. The computer device comprises a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method for determining the resource feedback parameters according to any one of the embodiments of the disclosure.
In a fourth aspect, the present application also provides a computer-readable storage medium. The computer readable storage medium has stored thereon a computer program which, when executed by a processor, implements a method for determining resource feedback parameters according to any of the embodiments of the present disclosure.
In a fifth aspect, the present application also provides a computer program product. The computer program product comprises a computer program which, when executed by a processor, implements a method for determining resource feedback parameters according to any of the embodiments of the present disclosure.
The method, the device, the computer equipment, the storage medium and the computer program product for determining the resource feedback parameters are characterized in that the matching result of the first resource and the candidate first object under a plurality of feedback parameters is determined by predicting the first resource under the batch feedback parameters and the information of the candidate first object, the number of the matched candidate first object is determined, the resource increment of the second object is determined by the number of the matched candidate first object and the information of the first resource, and the feedback parameter corresponding to the largest resource increment is selected from the resource increment of the second object under the plurality of feedback parameters to be used as the feedback parameter of the first resource. The prediction accuracy of the feedback parameters is improved, the increased resources of the second object can be effectively determined through the number of the matched first objects, and further, the corresponding feedback parameters when the increased resources of the second object are maximum are determined, so that the resources of the second object are accurately and effectively increased. And dividing the maximum feedback parameter from zero to the maximum feedback parameter to cover the set of the feedback parameters, so that the maximum increase of the second object resource is ensured, and the accuracy of the feedback parameter is ensured.
Drawings
Fig. 1 is a flow chart of a method for determining a resource feedback parameter in an embodiment;
FIG. 2 is a flow diagram of recognition model training in one embodiment;
FIG. 3 is a flow chart of feedback parameter model prediction in one embodiment;
FIG. 4 is a flow diagram of model predictive outcome analysis in one embodiment;
FIG. 5 is a flow diagram of resource increment determination in one embodiment;
FIG. 6 is a flow chart illustrating the determination of target feedback parameters according to one embodiment;
fig. 7 is a flowchart of an application of a method for determining a resource feedback parameter in one embodiment;
fig. 8 is a schematic diagram of a device for determining a resource feedback parameter in an embodiment;
FIG. 9 is a schematic diagram of an identification model training apparatus in one embodiment;
fig. 10 is an internal structural view of a computer device in one embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application.
It should be further noted that, the first resources (including, but not limited to, interaction information, interaction time, etc. of the resources) and the candidate first objects (including, but not limited to, user equipment information, object personal information, etc.) related in the present disclosure are information and data authorized by the user or sufficiently authorized by each party. The data acquisition, storage, use and processing in the technical scheme all accord with the relevant regulations of national laws and regulations.
In one embodiment, as shown in fig. 1, a method for determining a resource feedback parameter is provided, which includes the following steps:
step S1, acquiring description information of a first resource and attribute information of a candidate first object; the first resource comprises a resource returned to the first object by the second object in response to the second resource sent by the first object;
step S2, sampling feedback parameters of the first resources in batches, inputting description information of the first resources and attribute information of the candidate first objects into a preset identification model for prediction according to the feedback parameters of each batch, outputting a matching result of the first resources and the candidate first objects, and determining the number of the candidate first objects with the matching result as the matching result; the description information of the first resource comprises feedback parameters;
step S3, determining the resource increment of the second object under the condition that the second object interacts with the candidate first object according to the description information of the first resource and the number of the candidate first objects matched by the matching result;
step S4, determining that the target feedback parameter is the feedback parameter in the description information of the first resource corresponding to the maximum resource increment in the plurality of batches.
In an exemplary embodiment, the description information of the first resource may include feedback parameters, resource interaction modes, and the like of the first resource; for example, when the first resource is a product resource, the description information of the first resource may include: product feedback rate, risk level, interactive resource quantity, product mode, interactive channel and the like; in another exemplary embodiment, the description information of the first resource may include feedback parameters, the number of interactive resources, etc. of the first resource, for example, when the first resource is a coupon, the description information of the first resource may include: the first resource may include: the resource interaction center designated by the coupon is provided with the lowest interaction quantity, the preferential degree, the service time limit and the like of the coupon.
In one exemplary embodiment, the attribute information of the candidate first object may include personal information of the first object account, for example, information disclosed by a network or platform.
In an exemplary embodiment, the first resource includes a resource returned by the second object to the first object in response to the second resource sent by the first object; for example, the first resource is a product resource that includes a product center that transmits the product resource to a user in response to an amount of interaction resources transmitted by the user, and in another exemplary embodiment, when the first resource is a coupon, the coupon includes merchant information that transmits the coupon to the user in response to the user's most recent interaction information.
In an exemplary embodiment, the batch sampling the feedback parameter of the first resource may include: and sequentially sampling feedback parameters of the first resource, wherein the feedback parameters of the feedback parameters in batches can comprise continuously increasing the feedback parameter interval threshold value from 0 and predicting possible feedback parameters of the first resource according to the model.
In an exemplary embodiment, the outputting the matching result of the first resource and the candidate first object, and determining that the matching result is the number of matched candidate first objects may include determining, according to the prediction result, the number of first objects matched with the first resource under each feedback parameter.
In an exemplary embodiment, the determining that the second object performs resource interaction with the candidate first object may include determining that the second object corresponding to the first resource performs resource interaction with the first object when the matching result of the first resource and the first candidate object is a match, and calculating the resource increment after the interaction.
In an exemplary embodiment, the determining the target feedback parameter is a feedback parameter in the description information of the first resource corresponding to the maximum increase of the resources in the plurality of batches, and comparing the obtained increase of the resources of the second object under the plurality of feedback parameters to obtain the feedback parameter corresponding to the maximum increase of the resources of the second object.
According to the method for determining the resource feedback parameters, the matching result of the first resources and the candidate first objects under the feedback parameters is determined by predicting the first resources and the candidate first objects under the feedback parameters, the number of the matched candidate first objects is determined, the resource increment of the second objects is determined by the number of the matched candidate first objects and the information of the first resources, and the feedback parameter corresponding to the largest resource increment is selected from the resource increment of the second objects under the feedback parameters to serve as the feedback parameter of the first resources. The number of the matched first objects is determined through different feedback parameters, the increased resource amount of the second object is determined according to the different feedback parameters and the corresponding number of the matched first objects, the situation that the number of the matched first objects is reduced due to the fact that feedback parameters are too small, and the first resource amount returned to the first object by the second object due to the fact that feedback parameters are too large is avoided, and further the increase of the second object resource is reduced is caused, the fact that each feedback parameter has the increase of the resource corresponding to the second object is guaranteed, the fact that the feedback parameter corresponding to the largest increase of the second object resource is the target feedback parameter is guaranteed, the accuracy of the target feedback parameter is guaranteed, and the resources of the second object are accurately and effectively increased.
In one embodiment, as shown in fig. 2, step S2 includes:
step S31, obtaining sample information; wherein the sample information includes: a matching relation is marked between the first sample object and the first sample resource;
s32, constructing an initial training model, inputting the first sample object and the first sample resource into the initial training model, and outputting to obtain a prediction result;
and step S33, carrying out iterative adjustment on the initial training model based on the difference between the prediction result and the marked matching relation to obtain an identification model.
In an exemplary embodiment, the sample information may include the first sample object, where a matching relationship is marked between the first sample object and the first sample resource, for example, when the first sample resource is a product resource, the sample information includes: the account information of the user, the information of the applied product resources, the information of other product resources when the product resources are applied, and the like; in another exemplary embodiment, when the sample first resource is a coupon, the sample information includes account information of the user, historical coupon usage information, the number of interactions of the historical coupon, coupons contained by the user when the historical coupon was used, and the like.
In an exemplary embodiment, the recognition model may include: determining the lowest feedback parameter matching the candidate first object by means of an identification model, which in another exemplary embodiment may comprise: determining whether the candidate first object is matched with the first sample resource under the feedback parameter through an identification model, for example, determining the matching probability of the candidate first object with the first sample resource under the feedback parameter through a logistic regression mode, and determining that the candidate first object with the matching probability higher than a preset threshold is matched with the first sample resource.
In an exemplary embodiment, the recognition model may include correcting the predicted result by matching the candidate first object with the true result of the first resource, and inputting corrected data into the recognition model for continuous iterative correction. It should be noted that, the setting mode of the recognition model is not limited to the model example of the deep learning mode, for example, the recognition model may also be used as the recognition model through traditional machine learning, and those skilled in the art may also make other changes in light of the technical spirit of the present application, but as long as the implemented functions and effects are the same or similar to those of the present application, all the changes should be covered in the protection scope of the present application.
In this embodiment, by constructing an initial training model, predicting the first sample object and the first sample resource, iteratively adjusting the model based on a difference between a prediction result and a difference between the first sample object and the first sample resource before a matching relationship, to form an identification model, accuracy of the identification model can be improved, a matching result between the candidate first object and the first resource can be accurately predicted through training of the model, an increase of resources of the second object is further determined, convenience is provided for determining feedback parameters later, and accuracy of the resource feedback parameters is further ensured.
In one embodiment, as shown in fig. 3, step S2 includes:
step S41, determining the maximum feedback parameter under the condition that the second object resource is increased according to the attribute information of the first resource;
step S42, dividing feedback parameters by a preset feedback parameter interval threshold to obtain a feedback parameter set;
step S43, for each batch of feedback parameters, the description information of the first resource and the attribute information of the candidate first object are input into the recognition model, where the feedback parameters in the description information of the first resource are obtained by batch sampling from a feedback parameter set.
In an exemplary embodiment, the maximum feedback parameter may include: by determining the increase of the second resource, for example, the first resource is a product resource, the maximum feedback parameter may include a value of the feedback parameter when the difference between the total amount of reclaimed resources of the second object and the total amount of original resources of the second object is zero.
In an exemplary embodiment, the dividing the feedback parameter by the preset feedback parameter interval threshold to obtain the feedback parameter set may include: by setting a fixed interval, the feedback parameters are increased from zero to the maximum, and all the feedback parameters are taken as feedback parameter sets, in another exemplary embodiment, the feedback parameter sets may be determined by a preset number of feedback resource sets, for example, the number of feedback resource sets is determined to be 100, and the feedback parameters from 0 to the maximum are equally divided into 100 parts, so as to obtain the feedback parameter sets.
In an exemplary embodiment, the inputting, for each batch, the description information of the first resource and the attribute information of the candidate first object into the recognition model, where the feedback parameter in the description information of the first resource is obtained by batch sampling from a feedback parameter set may include: and inputting the description information of the first resource and the attribute information of the candidate first object into a model, and sequentially inputting feedback parameters in the feedback parameter set to judge the matching result of the first resource and the candidate first object.
In this embodiment, the maximum feedback parameter is determined according to the change of the second object resource, the feedback parameter set is obtained according to the preset interval threshold, and under each feedback parameter of the feedback parameter set, the matching result of the first resource and the candidate first object is determined, so that the feedback parameter set can be determined to cover the feedback parameter, the maximum increase of the second object resource under the target feedback parameter is ensured, and the accuracy of the matching result of the first resource and the first candidate object is ensured by sequentially inputting the feedback parameter, the accuracy of the increase of the second object resource is further ensured, and the accuracy of the feedback parameter is ensured.
In one embodiment, as shown in fig. 4, step S43 includes:
step S431, predicting the matching probability of the candidate first object and the second object under each feedback parameter in the feedback parameter set;
step S432, determining that the matching result of the candidate first object and the first resource is matching when the matching probability is greater than a preset probability threshold;
step S433, determining the number of candidate first objects that match the matching result of the first resource.
In an exemplary embodiment, predicting the probability of matching the candidate first object with the first resource under each feedback parameter in the set of feedback parameters may include: obtaining the matching probability of the first resource and the candidate first object through recognition model prediction; in another exemplary embodiment, the description information of the first resource and the attribute information of the candidate first object are input, a model predicts a minimum feedback parameter of the first resource matching with the candidate first object, and determines that the first resource matches with the candidate first object if the feedback parameter is greater than the minimum feedback parameter.
In an exemplary embodiment, in a case that the matching probability is greater than a preset probability threshold, determining that the matching result of the candidate first object and the first resource is a match may include: and when the matching probability is larger than a preset result, determining that the first resource is matched with the candidate first object. For example, if the threshold is determined to be fifty-five percent, then the first resource having the matching probability greater than fifty-five percent is determined to match the candidate first object.
In an exemplary embodiment, determining that the number of candidate first objects matching the first resource is the number of candidate first objects matching the first resource may include determining the matching result of the first resource and the candidate first objects by the above-preset threshold value, and further determining the number of matches of the first resource and the candidate first objects, and in another exemplary embodiment, the number of matches of the first resource and the candidate first objects may be directly determined by a matching probability, for example, the matching probability is 0.6, the number of the candidate first objects matching the first resource is determined to be 0.6, and if the two person matching probabilities are respectively 0.2,0.8, the number of matches of the candidate first objects matching the first resource is determined to be 1.
In an exemplary embodiment, the inputting the description information of the first resource and the attribute information of the candidate first object into the recognition model, and adjusting and predicting feedback parameters in the description information of the first resource according to the feedback parameter set may include: and inputting the description information of the first resource and the attribute information of the candidate first object into a model, sequentially inputting feedback parameters in the feedback parameter set to judge the matching result of the first resource and the candidate first object, in another exemplary embodiment, inputting the first resource and the candidate first object into the model, predicting the matching result of the first resource and the candidate first object as the lowest feedback parameter matched, and determining the matching result of the first resource and the candidate first object as the matching under the condition of the feedback parameter larger than the lowest feedback parameter.
In this embodiment, the matching probability of the candidate first object and the first resource under a plurality of feedback parameters in the feedback set is obtained through model prediction, so as to determine the matching result of the candidate first object and the first resource, and further determine the number of first objects whose matching result is matching. The matching result of the candidate first object and the first resource can be accurately determined through the matching probability, so that the accuracy of the matching result for the number of the matched first objects is guaranteed, convenience is provided for obtaining the resource increment of the second object subsequently, and the accuracy of feedback parameter determination is guaranteed.
In one embodiment, as depicted in fig. 5, step S3 includes:
step S51, according to the description information of the first resources and the number of candidate first objects, the matching result of which is the matching, determining the total amount of first resources of the first resources returned by the second objects to the number of first objects, and the total amount of second resources of the resources sent by the number of candidate first objects to the second objects;
and step S52, determining the second object resource increment according to the first resource total amount and the second resource total amount.
In an exemplary embodiment, the first total amount of resources may include a sum of all resources returned by the second object to the number of candidate first objects, e.g., when the first resource is a product resource, the first total amount of resources includes a sum of product resources returned by the second object to the number of candidate first objects and resources returned by the product resource to the candidate objects. In another exemplary embodiment, when the first resource is a coupon, the first total amount of resources includes resources returned by the second object to the number of candidate first objects and a value on an accompanying coupon, including: the coupon offers merchandise.
In an exemplary embodiment, the number of the candidate first objects sending a second total amount of resources to the second object comprises: the sum of the number of resources transmitted by the candidate first object to the second object, e.g. when the first resource is a coupon, may comprise the amount of resources actually transmitted by the first object to the second object.
In an exemplary embodiment, the second object resource increment may be determined by a difference between the first total amount of resources and the second total amount of resources, for example, when the first resource is a product resource, the second object resource increment may be determined by a difference between a final resource of the second object and an original resource of the second object, wherein the original resource of the second object may include: recovering the sum of the product resource and the resource fed back to the first object, wherein the final resource of the second object may include: and the sum of the resources sent by the candidate first objects to the second object. In another exemplary embodiment, the resource increment of the second object may be the resource increment rate of the second object, for example, a ratio of a difference between a total amount of the second resource and a total amount of the first resource to the total amount of the first resource may be calculated.
In this embodiment, the total amount of resources returned by the second object to the candidate first object and the total amount of resources sent by the candidate first object to the second object may be accurately determined by the description information of the first resource and the number of the candidate first objects that the matching result is the matching result, so as to determine the increase amount of resources of the second object. The total amount of the sending resources and the total amount of the receiving resources of the second object can be accurately determined through the description information of the first resources and the number of the candidate first objects, which are matched according to the matching result, the total amount of the resource increase of the second object is further determined, and convenience is provided for determining feedback parameters through comparison of the total amounts of the resource increase of the second objects with different feedback parameters.
In one embodiment, as depicted in fig. 6, step S4 includes:
step S71, obtaining feedback parameters of the batches and corresponding resource increment of the second object;
step S72, obtaining feedback parameters corresponding to the maximum resource increment of the second object by comparing the resource increment of a plurality of second objects;
step S73, determining the feedback parameter corresponding to the maximum resource increment as the target feedback parameter.
In an exemplary embodiment, the obtaining feedback parameters of the plurality of batches and the corresponding resource increment of the second object may include: and the resource increment of the second object corresponding to each feedback parameter in the feedback parameter set is correspondingly increased, and the resource increment is in one-to-one correspondence with the feedback parameter.
In an exemplary embodiment, the obtaining, by comparing the resource increment amounts of the plurality of second objects, feedback parameters corresponding to the largest resource increment amount of the second object may include: in another exemplary embodiment, a plurality of feedback parameters with the largest resource increment are determined, and the plurality of feedback parameters are selected.
In this embodiment, the feedback parameters of the batches and the corresponding resource increment of the second object are obtained, the resource increment is compared, the feedback parameter corresponding to the largest resource increment is obtained, and the feedback parameter corresponding to the largest increment is determined as the current feedback parameter, so that the feedback parameters and the corresponding resource increment of the second object can be determined, the current feedback parameter with the largest resource increment is obtained through comparison, the accuracy of the feedback parameter is ensured, and the resource increment of the second object is also ensured.
It should be understood that, although the steps in the flowcharts related to the embodiments described above are sequentially shown as indicated by arrows, these steps are not necessarily sequentially performed in the order indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in the flowcharts described in the above embodiments may include a plurality of steps or a plurality of stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of the steps or stages is not necessarily performed sequentially, but may be performed alternately or alternately with at least some of the other steps or stages.
In an exemplary embodiment, the method for determining the resource feedback parameter may be performed as shown in the flowchart of fig. 7:
step S801, obtaining attribute information of a candidate first object and description information of a first resource;
step S802, determining the maximum feedback parameter under the condition that the second resource is increased;
Step 803, determining a feedback parameter set according to the feedback parameter interval threshold and the maximum feedback parameter;
step S804, inputting the feedback parameters, the attribute information of the first object, and the description information of the first resource into an identification model for prediction;
step S805, determining the number of candidate first objects matching the first resource;
step S806, calculating the second object resource increment under each feedback parameter;
step S807, determining a feedback parameter with the maximum retrospective second object resource increment as a target feedback parameter according to the second object resource increment;
based on the same inventive concept, the embodiment of the application also provides a device for determining the resource feedback parameters for implementing the above-mentioned method for determining the resource feedback parameters. The implementation of the solution provided by the apparatus is similar to the implementation described in the above method, so the specific limitation in the embodiments of the determining apparatus for one or more resource feedback parameters provided below may refer to the limitation of the determining method for the resource feedback parameter hereinabove, and will not be repeated herein.
In one embodiment, as shown in fig. 8, there is provided a device 10 for determining a resource feedback parameter, including: an acquisition module 100, a determination number module 300, a resource addition module 500, and a target parameter module 700, wherein:
the acquisition module is used for acquiring the description information of the first resource and the attribute information of the candidate first object; the first resource comprises a resource returned to the first object by the second object in response to the second resource sent by the first object;
the quantity determining module is used for sampling feedback parameters of the first resources in batches, inputting the description information of the first resources and the attribute information of the candidate first objects into a preset identification model for prediction aiming at the feedback parameters of each batch, outputting the matching result of the first resources and the candidate first objects, and determining the quantity of the candidate first objects with the matching result as the matching result; the description information of the first resource comprises feedback parameters;
the resource increasing module is used for determining the resource increasing amount of the second object under the condition that the second object interacts with the candidate first object according to the description information of the first resource and the number of the candidate first objects matched by the matching result;
And the target parameter module is used for determining that the target feedback parameter is the feedback parameter in the description information of the first resource corresponding to the maximum resource increment in the plurality of batches.
In one embodiment, as shown in FIG. 9, there is provided an identification model training module 20 comprising: an acquisition sample module 301, a construction module 302 and an identification model module 303:
the sample acquisition module is used for acquiring sample information; wherein the sample information includes: a matching relation is marked between the first sample object and the first sample resource;
the construction module is used for constructing an initial training model, inputting the first sample object and the first sample resource into the initial training model, and outputting a prediction result;
and the recognition model module is used for carrying out iterative adjustment on the initial training model based on the difference between the prediction result and the marked matching relation to obtain a recognition model.
In one embodiment, the determining the number of modules includes: a determination sub-module, a segmentation sub-module, and a prediction sub-module, wherein:
a determining submodule, configured to determine a maximum feedback parameter in a case where the second object resource increases according to the attribute information of the first resource;
The segmentation sub-module is used for segmenting feedback parameters by a preset feedback parameter interval threshold value to obtain a feedback parameter set;
and the prediction sub-module is used for inputting the description information of the first resource and the attribute information of the candidate first object into the identification model aiming at the feedback parameter of each batch, wherein the feedback parameter in the description information of the first resource is obtained by batch sampling from a feedback parameter set.
In one embodiment, the determining the number of modules includes: probability submodule, match submodule and number submodule, wherein:
the probability sub-module is used for predicting the matching probability of the candidate first object and the second object under each feedback parameter in the feedback parameter set;
the matching sub-module is used for determining that the matching result of the candidate first object and the first resource is matching under the condition that the matching probability is larger than a preset probability threshold;
and the number sub-module is used for determining the number of the candidate first objects matched with the matching result of the first resource.
In one embodiment, the resource adding module includes: total quantum module and add quantum module, wherein:
The total quantum module is used for determining the total amount of first resources returned by the second object to the first objects according to the description information of the first resources and the number of candidate first objects matched by the matching result, and the total amount of second resources of resources sent by the first objects to the second object;
and the adding quantum module is used for determining the second object resource adding amount according to the first resource total amount and the second resource total amount.
In one embodiment, the target parameter module includes: the method comprises the steps of obtaining a sub-module, a parameter sub-module and a target sub-module, wherein:
the acquisition sub-module is used for acquiring feedback parameters of the batches and the corresponding resource increment of the second object;
the parameter sub-module is used for obtaining feedback parameters corresponding to the maximum resource increment of the second object by comparing the resource increment of the plurality of second objects;
and the target sub-module is used for determining the feedback parameter corresponding to the maximum resource increment as the target feedback parameter.
All or part of the modules in the above-mentioned resource feedback parameter determining device may be implemented by software, hardware, and a combination thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one embodiment, a computer device is provided, which may be a server, and the internal structure of which may be as shown in fig. 10. The computer device includes a processor, a memory, an Input/Output interface (I/O) and a communication interface. The processor, the memory and the input/output interface are connected through a system bus, and the communication interface is connected to the system bus through the input/output interface. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer device is for storing resource data. The input/output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a method of determining resource feedback parameters.
It will be appreciated by those skilled in the art that the structure shown in fig. 10 is merely a block diagram of some of the structures associated with the present application and is not limiting of the computer device to which the present application may be applied, and that a particular computer device may include more or fewer components than shown, or may combine certain components, or have a different arrangement of components.
It should be noted that, the user information (including, but not limited to, user equipment information, user personal information, etc.) and the data (including, but not limited to, data for analysis, stored data, presented data, etc.) referred to in the present application are information and data authorized by the user or sufficiently authorized by each party, and the collection, use and processing of the related data are required to comply with the related laws and regulations and standards of the related countries and regions.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, database, or other medium used in the various embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high density embedded nonvolatile Memory, resistive random access Memory (ReRAM), magnetic random access Memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric Memory (Ferroelectric Random Access Memory, FRAM), phase change Memory (Phase Change Memory, PCM), graphene Memory, and the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, and the like. By way of illustration, and not limitation, RAM can be in the form of a variety of forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), and the like. The databases referred to in the various embodiments provided herein may include at least one of relational databases and non-relational databases. The non-relational database may include, but is not limited to, a blockchain-based distributed database, and the like. The processors referred to in the embodiments provided herein may be general purpose processors, central processing units, graphics processors, digital signal processors, programmable logic units, quantum computing-based data processing logic units, etc., without being limited thereto.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples only represent a few embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the present application. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made without departing from the spirit of the present application, which would be within the scope of the present application. Accordingly, the scope of protection of the present application shall be subject to the appended claims.
Claims (15)
1. The method for determining the resource feedback parameter is characterized by comprising the following steps:
acquiring description information of a first resource and attribute information of a candidate first object; the first resource comprises a resource returned to the first object by the second object in response to the second resource sent by the first object;
sampling feedback parameters of the first resources in batches, inputting description information of the first resources and attribute information of the candidate first objects into a preset identification model for prediction aiming at the feedback parameters of each batch, outputting a matching result of the first resources and the candidate first objects, and determining the number of the candidate first objects with the matching result as the matching result; the description information of the first resource comprises feedback parameters;
Determining the resource increment of the second object under the condition that the second object interacts with the candidate first object according to the description information of the first resource and the number of the candidate first objects matched by the matching result;
and determining a target feedback parameter as the feedback parameter in the description information of the first resource corresponding to the maximum resource increment in the plurality of batches.
2. The method of claim 1, wherein the training mode of the recognition model comprises:
acquiring sample information; wherein the sample information includes: a matching relation is marked between the first sample object and the first sample resource;
constructing an initial training model, inputting the first object of the sample and the first resource of the sample into the initial training model, and outputting to obtain a prediction result;
and carrying out iterative adjustment on the initial training model based on the difference between the prediction result and the marked matching relation to obtain an identification model.
3. The method according to claim 1, wherein sampling the feedback parameters of the first resource in batches, and inputting the description information of the first resource and the attribute information of the candidate first object into a preset identification model for prediction for each batch of feedback parameters, includes:
Determining a maximum feedback parameter under the condition that the second object resource is increased according to the attribute information of the first resource;
dividing feedback parameters by a preset feedback parameter interval threshold to obtain a feedback parameter set;
and inputting the description information of the first resource and the attribute information of the candidate first object into an identification model aiming at the feedback parameter of each batch, wherein the feedback parameter in the description information of the first resource is obtained by batch sampling from a feedback parameter set.
4. The method of claim 3, wherein the feedback parameters in the description information of the first resource are adjusted and predicted according to the feedback parameter set, and the method comprises: .
Predicting the matching probability of the candidate first object and the second object under each feedback parameter in the feedback parameter set;
under the condition that the matching probability is larger than a preset probability threshold, determining that the matching result of the candidate first object and the first resource is matching;
and determining the number of the candidate first objects matched with the matching result of the first resource.
5. The method according to claim 1, wherein determining the resource increment of the second object in the case of resource interaction with the candidate first object according to the description information of the first resource and the number of candidate first objects matched by the matching result includes:
Determining a first resource total amount of first resources returned by the second object to the first objects according to the description information of the first resources and the number of candidate first objects matched by the matching result, and transmitting a second resource total amount of resources to the second object by the candidate first objects;
and determining the second object resource increment according to the first resource total amount and the second resource total amount.
6. The method of claim 1, wherein determining that the target feedback parameter is a feedback parameter in the description information of the first resource corresponding to the maximum increase in the resources in the plurality of batches comprises:
acquiring feedback parameters of the batches and corresponding resource increment of the second object;
obtaining feedback parameters corresponding to the maximum resource increment of the second object by comparing the resource increment of a plurality of second objects;
and determining the feedback parameter corresponding to the maximum resource increment as the target feedback parameter.
7. A device for determining a resource feedback parameter, the device comprising:
The acquisition module is used for acquiring the description information of the first resource and the attribute information of the candidate first object; the first resource comprises a resource returned to the first object by the second object in response to the second resource sent by the first object;
the quantity determining module is used for sampling feedback parameters of the first resources in batches, inputting the description information of the first resources and the attribute information of the candidate first objects into a preset identification model for prediction aiming at the feedback parameters of each batch, outputting the matching result of the first resources and the candidate first objects, and determining the quantity of the candidate first objects with the matching result as the matching result; the description information of the first resource comprises feedback parameters;
the resource increasing module is used for determining the resource increasing amount of the second object under the condition that the second object interacts with the candidate first object according to the description information of the first resource and the number of the candidate first objects matched by the matching result;
and the target parameter module is used for determining that the target feedback parameter is the feedback parameter in the description information of the first resource corresponding to the maximum resource increment in the plurality of batches.
8. The apparatus of claim 7, wherein the apparatus comprises:
the sample acquisition module is used for acquiring sample information; wherein the sample information includes: a matching relation is marked between the first sample object and the first sample resource;
the construction module is used for constructing an initial training model, inputting the first sample object and the first sample resource into the initial training model, and outputting a prediction result;
and the recognition model module is used for iteratively adjusting the initial training model based on the difference between the prediction result and the marked matching relation to obtain a recognition model.
9. The apparatus of claim 7, wherein the determined number module comprises:
a determining submodule, configured to determine a maximum feedback parameter in a case where the second object resource increases according to the attribute information of the first resource;
the segmentation sub-module is used for segmenting feedback parameters by a preset feedback parameter interval threshold value to obtain a feedback parameter set;
and the prediction sub-module is used for inputting the description information of the first resource and the attribute information of the candidate first object into the identification model aiming at the feedback parameter of each batch, wherein the feedback parameter in the description information of the first resource is obtained by batch sampling from a feedback parameter set.
10. The apparatus of claim 9, wherein the determined number module comprises:
the probability sub-module is used for predicting the matching probability of the candidate first object and the second object under each feedback parameter in the feedback parameter set;
the matching sub-module is used for determining that the matching result of the candidate first object and the first resource is matching under the condition that the matching probability is larger than a preset probability threshold;
and the number sub-module is used for determining the number of the candidate first objects matched with the matching result of the first resource.
11. The apparatus of claim 7, wherein the resource adding module comprises:
the total quantum module is used for determining the total amount of first resources returned by the second object to the first objects according to the description information of the first resources and the number of candidate first objects matched by the matching result, and the total amount of second resources of resources sent by the first objects to the second object;
and the adding quantum module is used for determining the second object resource adding amount according to the first resource total amount and the second resource total amount.
12. The apparatus of claim 7, wherein the target parameter module comprises:
the acquisition sub-module is used for acquiring feedback parameters of the batches and the corresponding resource increment of the second object;
the parameter sub-module is used for obtaining feedback parameters corresponding to the maximum resource increment of the second object by comparing the resource increment of the plurality of second objects;
and the target sub-module is used for determining the feedback parameter corresponding to the maximum resource increment as the target feedback parameter.
13. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 6 when the computer program is executed.
14. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 6.
15. A computer program product comprising a computer program, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 6.
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