CN111626767A - Resource data distribution method, device and equipment - Google Patents
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Abstract
The application discloses a resource data distribution method, a resource data distribution device and resource data distribution equipment, relates to the technical field of data processing, and can realize accurate distribution of resource data and improve the return rate of the resource data by considering a user, an entity and interaction behaviors between the user and the entity in the resource data distribution process. The method comprises the following steps: acquiring characteristic parameters related to a user and an entity object; forming a plurality of dimensional characteristics by using the characteristic parameters related to the user and the entity object, and inputting the dimensional characteristics to a pre-constructed conversion rate model to obtain a prediction function expression; according to the prediction function expression, determining the resource data provided by the corresponding entity object when the conversion rate of the resource data used by the user is the highest as the first resource data which should be provided to the user by the entity object; and issuing the first resource data to a user.
Description
Technical Field
The present application relates to the field of data processing technologies, and in particular, to a method, an apparatus, and a device for issuing resource data.
Background
In the mobile internet era, with the rapid development of electronic commerce technology, a network platform has become an important tool for daily consumption of people. In order to achieve better interaction with the user, the merchant often uses some interaction parameters to enable the user to receive certain resource data in advance, so that the user can use the received resource data to achieve order transaction of the object in the network platform, and thus the user can enjoy order preference.
In the related art, in the process of issuing resource data, a merchant may determine a value of the resource data using some operation rules, for example, the resource data is valued based on a frequency of a user login platform as a rule, a user who logs in less than 2 times a month is a low-frequency user, a user who logs in more than 10 times a month is a high-frequency user, the low-frequency user sets resource data with a high value, and the high-frequency user sets resource data with a low value.
In the process of implementing the invention, the inventor finds that the related art has at least the following problems:
when the network platform is used for distributing the resource data, the user can better participate in order transaction, and the interactivity between the merchant and the user is improved. Because the resource data issued by different users are set by utilizing the manually specified rules, the issuing mode of the resource data is too single, if the users do not pay attention to the products, the users cannot be promoted to buy the products even if the numerical values provided in the resource data are higher, the users who pay attention to the products have certain unfairness, the resource data cannot be accurately distributed, the return rate of the resource data is lower, and the interactivity between merchants and the users is reduced.
Disclosure of Invention
In view of this, the present application provides a method, an apparatus, and a device for issuing resource data, and mainly aims to solve the problem that in the prior art, an issuing manner of resource data is too single, and resource data cannot be accurately distributed.
According to an aspect of the present application, there is provided a method for issuing resource data, the method including:
acquiring characteristic parameters related to a user and an entity object;
forming a plurality of dimensional characteristics of the characteristic parameters related to the user and the entity object, inputting the dimensional characteristics into a pre-constructed conversion rate model to obtain a prediction function expression, wherein the conversion rate model outputs the prediction function expression based on the input dimensional characteristics, and the prediction function expression is used for describing the relation between the conversion rate of the resource data used by the user and the change of the resource data provided by the entity object;
according to the prediction function expression, determining the resource data provided by the corresponding entity object when the conversion rate of the resource data used by the user is the highest as the first resource data which should be provided to the user by the entity object;
and issuing the first resource data to a user.
Further, the feature parameters related to the user and the entity object include a user feature parameter, an entity object feature parameter, and a feature parameter for interaction between the user and the entity object, and the obtaining of the feature parameters related to the user and the entity object specifically includes:
analyzing user behavior data by using a data model trained by a user label to obtain user characteristic parameters;
analyzing a browsing sequence of a user to the entity object within a first preset time by using a mapping model trained by the node sequence of the entity object to obtain characteristic parameters of the entity object;
and counting the access data of the user to the entity object within a second preset time after the entity object provides the resource data, and acquiring the characteristic parameters of the interaction between the user and the entity object.
Further, the analyzing a browsing sequence of the user to the entity object within a first preset time by using the mapping model trained by the node sequence of the entity object to obtain the characteristic parameters of the entity object specifically includes:
mapping a browsing sequence of the user to the entity object in a first preset time into a spatially distributed expression of the entity object by using a mapping model trained by the node sequence of the entity object;
and calculating the connection information between the entity objects according to the distributed expression of the entity objects on the space, and acquiring the characteristic parameters of the entity objects.
Further, the acquiring the characteristic parameters of the interaction between the user and the entity object by counting the interaction data of the user to the entity object in a second preset time after the entity object provides the resource data specifically includes:
performing data embedding on the entity object after the entity object provides the resource data, and collecting behavior data related to user operation in the entity object data;
and counting the interaction data of the user to the entity object within a second preset time based on the behavior data related to the user operation in the entity object data, and acquiring the characteristic parameters of the interaction between the user and the entity object.
Further, a plurality of decision trees are established in the conversion rate model, each decision tree is used for predicting a relationship between a conversion rate of the resource data used by the user and changes along with the resource data provided by the entity object from different dimensional characteristics, a plurality of dimensional characteristics formed by characteristic parameters related to the user and the entity object are input to the conversion rate model established in advance to obtain a prediction function expression, and the method specifically comprises the following steps:
forming a plurality of dimensional characteristics by using the characteristic parameters related to the user and the entity object by using a pre-constructed conversion rate model, distributing the dimensional characteristics to each leaf node in a decision tree, wherein each leaf node corresponds to a parameter of characteristic mapping;
and predicting the decision tree of the optimal structure according to the parameters of the feature mapping corresponding to each leaf node and the leaf node in the decision tree, and taking the decision tree of the optimal structure as a prediction function expression.
Further, the predicting a decision tree with an optimal structure according to parameters of feature mapping corresponding to each leaf node and the leaf node in the decision tree, and using the decision tree with the optimal structure as a prediction function expression specifically includes:
sequencing each leaf node in the decision tree according to gradient data from big to small, and listing cutting points for cutting the decision tree;
scoring the segmentation points for segmenting the decision tree aiming at the parameters of the feature mapping corresponding to each leaf node in the decision tree;
and taking a decision tree formed by the segmentation points with the highest score as a decision tree of an optimal structure to obtain a prediction function expression.
Further, after determining target resource data to be issued to the user by the entity object according to the prediction function expression of the resource data provided by the user to the entity object, the method further includes:
respectively aggregating users containing the same attribute characteristics and aggregating entity objects containing the same attribute characteristics by using the characteristic parameters related to the users and the entity objects to obtain a target user group and a target entity object group;
determining second resource data which should be issued to a target user group by the target entity object group according to the first resource data which should be issued to the user by the entity object;
the issuing of the target resource data to the user specifically includes:
and issuing the second resource data to the target user group.
According to another aspect of the present application, there is provided an apparatus for issuing resource data, the apparatus including:
the acquiring unit is used for acquiring characteristic parameters related to a user and an entity object;
the prediction unit is used for inputting a plurality of dimensional characteristics formed by characteristic parameters related to the user and the entity object into a conversion rate model constructed in advance to obtain a prediction function expression, wherein the conversion rate model outputs the prediction function expression based on the input plurality of dimensional characteristics, and the prediction function expression is used for describing the relation between the conversion rate of the resource data used by the user and the change of the resource data provided by the entity object;
the first determining unit is used for determining the resource data provided by the corresponding entity object when the conversion rate of the resource data used by the user is the highest as the first resource data which is to be distributed to the user by the entity object according to the prediction function expression;
and the issuing unit is used for issuing the first resource data to the user.
Further, the characteristic parameters related to the user and the entity object include a user characteristic parameter, an entity object characteristic parameter, and a characteristic parameter for interaction between the user and the entity object, and the obtaining unit includes
The first acquisition module is used for analyzing user behavior data by using a data model trained by a user label to acquire user characteristic parameters;
the second acquisition module is used for analyzing a browsing sequence of the user to the entity object within first preset time by using the mapping model trained by the node sequence of the entity object to acquire characteristic parameters of the entity object;
and the third acquisition module is used for counting the access data of the user to the entity object in a second preset time after the entity object provides the resource data, and acquiring the characteristic parameters of the interaction between the user and the entity object.
Further, the second obtaining module includes:
the mapping submodule is used for mapping a browsing sequence of the user to the entity object in a first preset time into a distributed expression of the entity object on the space by using a mapping model trained by the node sequence of the entity object;
and the calculation submodule is used for calculating the connection information between the entity objects according to the distributed expression of the entity objects on the space and acquiring the characteristic parameters of the entity objects.
Further, the third obtaining module includes:
the collection submodule is used for performing data point burying on the entity object after the entity object provides the resource data and collecting behavior data related to user operation in the entity object data;
and the statistic submodule is used for counting the interaction data of the user to the entity object in second preset time based on the behavior data related to the user operation in the entity object data, and acquiring the characteristic parameters of the interaction between the user and the entity object.
Further, a plurality of decision trees are built in the conversion rate model, each decision tree is used for predicting the relation between the conversion rate of the user using the resource data and the change of the resource data provided by the entity object from different dimensional features, and the prediction unit comprises:
the distribution module is used for forming a plurality of dimensional characteristics by using the characteristic parameters related to the user and the entity object through a pre-constructed conversion rate model and distributing the dimensional characteristics to each leaf node in the decision tree, and each leaf node corresponds to a parameter of characteristic mapping;
and the prediction module is used for predicting the decision tree with the optimal structure according to the parameters of the feature mapping corresponding to each leaf node and the leaf node in the decision tree, and taking the decision tree with the optimal structure as a prediction function expression.
Further, the prediction module comprises:
the sorting submodule is used for sorting all leaf nodes in the decision tree according to gradient data from large to small and listing cutting points for cutting the decision tree;
the scoring submodule is used for scoring the segmentation points for segmenting the decision tree according to the parameters of the feature mapping corresponding to each leaf node in the decision tree;
and the prediction submodule is used for taking a decision tree formed by the segmentation point with the highest score as a decision tree with an optimal structure to obtain a prediction function expression.
Further, the apparatus further comprises:
the aggregation unit is used for respectively aggregating users containing the same attribute characteristics and aggregating entity objects containing the same attribute characteristics by using the characteristic parameters related to the users and the entity objects to obtain a target user group and a target entity object group after determining target resource data to be issued to the users by the entity objects according to the prediction function expression of the resource data provided by the users to the entity objects;
a second determining unit, configured to determine, according to the first resource data that the entity object should be issued to the user, second resource data that the target entity object group should be issued to the target user group;
the issuing unit is specifically further configured to issue the second resource data to the target user group.
According to yet another aspect of the present application, there is provided a storage medium having stored thereon a computer program which, when executed by a processor, implements the above-described resource data issuing method.
According to still another aspect of the present application, there is provided a resource data issuing entity apparatus, including a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor, where the processor implements the resource data issuing method when executing the program.
By the technical scheme, compared with the resource data distribution mode in which the numerical value of the resource data is determined by manual experience in the existing mode, the resource data distribution method, the resource data distribution device and the resource data distribution equipment provided by the application take the characteristic parameters related to the user and the entity object into consideration, form a plurality of dimensional characteristics of the characteristic parameters related to the user and the entity object and input the dimensional characteristics into a pre-constructed conversion rate model, output a prediction function expression of the resource data provided by the user to the entity object by using the conversion rate model based on the input dimensional characteristics, the prediction function expression is used for describing the relation between the conversion rate of the resource data used by the user and the change of the resource data provided by the entity object, and further provide the resource data corresponding to the entity object when the conversion rate of the resource data used by the user is maximum to serve as the resource data correspondingly distributed by the user, the user can acquire the resource data with the optimal numerical value, so that the user can more intentionally participate in the interaction of the resource data, the resource data can be accurately distributed, and the return rate of the resource data is improved.
The foregoing description is only an overview of the technical solutions of the present application, and the present application can be implemented according to the content of the description in order to make the technical means of the present application more clearly understood, and the following detailed description of the present application is given in order to make the above and other objects, features, and advantages of the present application more clearly understandable.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
fig. 1 is a schematic flowchart illustrating a method for issuing resource data according to an embodiment of the present application;
fig. 2 is a schematic flowchart illustrating another resource data issuing method according to an embodiment of the present application;
fig. 3 is a schematic structural diagram illustrating an apparatus for issuing resource data according to an embodiment of the present application;
fig. 4 is a schematic structural diagram illustrating another resource data issuing apparatus according to an embodiment of the present application.
Detailed Description
The present application will be described in detail below with reference to the accompanying drawings in conjunction with embodiments. It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict.
At present, through the existing resource data issuing mode, the numerical value of the resource data can be subsidized less for the high-frequency user and the numerical value of the resource data can be subsidized more for the low-frequency user, however, the essence of the resource data issuing is to promote the user to consume the numerical value of the resource data, and because the numerical pricing of the resource data issued by different users is determined by manual experience, the numerical pricing of the resource data is unreasonable, the resource data cannot be accurately issued theoretically, and the return rate of the resource data is low.
In order to solve the problem, the present embodiment provides a method for issuing resource data, as shown in fig. 1, the method includes:
101. and acquiring characteristic parameters related to the user and the entity object.
The characteristic parameters related to the user and the entity object may include a user characteristic parameter, an entity object characteristic parameter, and a parameter of interaction between the user and the entity object, where the entity object is an online merchant.
In the embodiment of the present invention, the feature data related to the user and the entity object may be specifically obtained by collecting a user behavior data set in the application log, and since the user behavior data set includes various behaviors of the user in the process of accessing the entity object, such as login, browsing, placing an order, and the like, the user feature parameters, the entity object feature parameters, and parameters of interaction between the user and the entity object may be extracted by using the user behavior data set.
The user characteristic parameters describe characteristic information of the user in each dimension, including but not limited to sensitivity of the user to numerical values in resource data, user portrait, user behavior of a user in a service scene, and the like, a tagged user model can be constructed according to information of user characteristics, user behavior of a service scene, and the like, characteristic information of the user in each dimension is predicted according to user model data, the user portrait can include gender, age, occupation of the user, consumption condition at a resource issuing platform, age stage at the user, and the like, the user characteristic parameters can be determined by specifically combining account information of the user logging in the resource issuing platform, the sensitivity of the user to numerical values in the resource data can include the use condition of the user to the resource data issued to an account, the user characteristic parameters can be determined by specifically combining the condition of the user obtaining the resource data, the payment times of the user using the resource data, and the like, the condition that the user accesses the app may include a probability that the user logs in the app, a probability that the user performs an operation on the product, and the like, and may be specifically determined by collecting a behavior log of the user in the app.
The above-mentioned entity object feature parameter is feature information describing similarity between entity objects, and may be represented by a vector, and specifically may predict similarity between entity objects according to a browsing sequence of a user to the entity objects within a preset time, for example, the obtaining of the browsing sequence of the user to the entity objects within 30 minutes is as follows: the method comprises the following steps of A entity object, B entity object and C entity object, and the correlation of different entity objects is converted into points on the space to describe, the vector of the entity object A and the vector of the entity object B are calculated, and the closer the space distance between the two vectors is, the higher the similarity between the entity object A and the entity object B is.
The interaction parameter between the user and the entity object is used for describing the access condition of the user to the entity object, and specifically, the numerical value in the resource data provided by the entity object and the behavior characteristics of the product provided by the user to the entity object in the preset time, such as access, repurchase behavior, purchase behavior and the like, can be counted.
It can be understood that when the entity object uses the resource delivery platform to deliver the resource data, only the dimensional characteristics such as the frequency of using the resource data by the user may be considered, but the dimensional characteristics related to the entity object are not considered, and the resource data used by the user, for example, the access probability of the user is increased by using similar entity objects, and the user has refereability to whether to use the resource data, and the access condition of the user to the entity object may indicate the attention degree of the user to the entity object, and also has refereability to whether to use the resource data by the user, so by obtaining the characteristic parameter related to the user and the entity object is equivalent to the preparation work of predicting the conversion rate of using the resource data by the user subsequently, the conversion rate of using the resource data by the user is predicted as the basis for carrying out numerical pricing on the delivered resource data.
102. And forming a plurality of dimensional characteristics by using the characteristic parameters related to the user and the entity object, and inputting the dimensional characteristics to a pre-constructed conversion rate model to obtain a prediction function expression.
In the embodiment of the invention, the pre-constructed conversion rate model outputs the prediction function expression based on the input multiple dimension characteristics, the prediction function expression is used for describing the relation between the conversion rate of the resource data used by the user and the change of the resource data provided by the entity object, and the obtained multiple dimension characteristics are different for different users and entity objects, so that the prediction function expression suitable for the users and the entity objects is different in output.
It can be understood that the conversion rate model may output a prediction function expression, so as to predict a relationship between a conversion rate of a user using resource data and a change of the resource data provided by an entity object, and specifically, based on whether the known user uses a label of the resource data provided by the entity object, a feature parameter related to the user and the entity object is used as a training sample, and the conversion rate model is constructed by presetting the training sample which is continuously and iteratively input by the learning model.
In the embodiment of the present invention, the preset learning model may be selected as a supervision model having a learning effect, including but not limited to a boost model, an xgboost model, and the like, and is not limited herein.
103. And according to the prediction function expression, determining the resource data provided by the corresponding entity object when the conversion rate of the resource data used by the user is the highest as the first resource data which should be provided to the user by the entity object.
In the embodiment of the present invention, since the prediction function expression can predict the conversion rate of the resource data used by the user for the change of the value range of the resource data provided by the entity object, the higher the conversion rate of the resource data used by the user is, the higher the probability that the user purchases a product using the resource data is, of course, the higher the conversion rate of the resource data used by the user is, rather than the higher the resource data provided by the entity object is, the higher the conversion rate of the resource data used by the user is, the resource data provided by the corresponding entity object when the conversion rate of the resource data used by the user is the highest can be selected as the.
104. And issuing the first resource data to a user.
The first resource data is equivalent to a numerical subsidy issued by the entity object to the user through the resource issuing platform, so that the user can use the first resource data when consuming the resource object, thereby saving the corresponding numerical value, wherein the first resource data issued to the user can include but is not limited to red packets, coupons, full discount coupons and the like.
It can be understood that, the entity object does not directly issue the resource data to the user, and in order to accurately deliver the resource data suitable for interaction to the user, the resource data suitable for being pushed to the user is calculated by using the resource issuing platform, so the execution subject of the embodiment of the present invention may be a resource data issuing device, which is generally configured on the side of the resource issuing platform, and the resource data determined by the resource data issuing platform according to the interaction characteristics of the user and the entity object is used to quickly determine the resource data of interest for the user, so that the user can use the resource data more effectively, and further, the interactivity between the entity object and the user is improved.
Compared with the resource data distribution method in which the numerical value of the resource data is determined by manual experience in the existing method, the resource data distribution method provided by the embodiment of the invention takes the characteristic parameters related to the user and the entity object into consideration, forms a plurality of dimensional characteristics by the characteristic parameters related to the user and the entity object and inputs the dimensional characteristics into the pre-constructed conversion rate model, outputs the prediction function expression of the resource data provided by the user to the entity object based on the input dimensional characteristics by using the conversion rate model, the prediction function expression is used for describing the relation between the conversion rate of the resource data used by the user and the change of the resource data provided by the entity object, further provides the resource data by the entity object corresponding to the maximum conversion rate of the resource data used by the user as the resource data correspondingly distributed by the user, so that the user obtains the resource data with the optimal numerical value, the user is more willing to participate in the interaction of the resource data, the resource data are accurately distributed, and the return rate of the resource data is improved.
Further, as a refinement and an extension of the specific implementation of the foregoing embodiment, in order to fully describe the specific implementation process of the embodiment, the embodiment provides another method for issuing resource data, as shown in fig. 2, the method includes:
201. and acquiring characteristic parameters related to the user and the entity object.
In the embodiment of the present invention, the feature parameters related to the user and the entity object may include a user feature parameter, an entity object feature parameter, and a parameter for interaction between the user and the entity object.
For the user characteristic parameters, the data model trained by the user labels can be used for analyzing the user behavior data to obtain the user characteristic parameters, the data model trained by the user labels can be used for mapping the user behavior data to a plurality of subspaces, the characteristic weight of the user behavior data on the user labels is captured, and the user characteristic parameters are predicted.
Specifically, the mapping model trained by the node sequence of the entity object can be used to map the browsing sequence of the entity object by the user in the first preset time into a spatially distributed expression of the entity object, and further, the connection information between the entity objects is calculated according to the spatially distributed expression of the entity object to obtain the characteristic parameter of the entity object. Here, the browsing sequence of the entity object by the user in the first preset time may generate a node sequence, that is, the node sequence of the entity object browsed by the user, the mapping model may map the node sequence to points in space, a vector formed by each point represents an entity object, and the closer the spatial distance of the vector formed by the entity object is, the higher the similarity between the entity objects is.
For the parameter of the interaction between the user and the entity object, the characteristic parameter of the interaction between the user and the entity object can be obtained by counting the access data of the user to the entity object in the second preset time after the entity object provides the resource data. The method specifically includes the steps of performing data point burying on an entity object after the entity object provides resource data, collecting behavior data related to user operation in the entity object data, further counting interaction data of the user on the entity object within second preset time based on the behavior data related to the user operation in the entity object data, and obtaining characteristic parameters of interaction between the user and the entity object.
202. And forming a plurality of dimensional characteristics by using the characteristic parameters related to the user and the entity object by using a pre-constructed conversion rate model and distributing the dimensional characteristics to each leaf node in the decision tree.
Specifically, in the process of constructing the conversion rate model, a decision tree can be continuously added by using the preset learning model, feature splitting is continuously performed to grow the decision tree, one decision tree is added each time, a new prediction function expression is learned to fit a residual error of the last prediction, when training is completed, a user forms a plurality of decision trees, each decision tree is used for predicting the relation between the conversion rate of the user using resource data and the change of the resource data provided by the entity object from different dimensional features, so that the plurality of dimensional features formed by the feature parameters related to the user and the entity object are input into the conversion rate model constructed in advance, the plurality of dimensional features are required to be distributed to each leaf node in the decision tree, and each leaf node is provided with a parameter for feature mapping.
203. And predicting the decision tree of the optimal structure according to the parameters of the feature mapping corresponding to each leaf node and the leaf node in the decision tree, and taking the decision tree of the optimal structure as a prediction function expression.
In the embodiment of the invention, each leaf node in the decision tree can be sorted from large to small according to gradient data, and the dividing points for dividing the decision tree are listed; scoring the segmentation points segmented by the decision tree aiming at the parameters of the feature mapping corresponding to each leaf node in the decision tree; and taking a decision tree formed by the segmentation points with the highest score as a decision tree of an optimal structure to obtain a prediction function expression. The prediction function expression can predict the ordering probability of the user in the given resource data value range of the entity object, for example, if the entity object provides the resource data value range of 3-7 elements, the ordering probability of the user using the resource data can be predicted in the value range.
Specifically, due to different learning algorithms adopted by the constructed conversion rate model, the prediction function expressions output based on the conversion rate model may not be the same, for example, the prediction function expression may be a smooth curve in which the conversion rate increases as the value of the resource data provided by the entity object increases, may be a jagged curve in which the conversion rate is not monotonous as the value of the resource data provided by the entity object increases, may be a curve in which the conversion rate increases and then decreases as the value of the resource data provided by the entity object increases, and the like, and further obtains the prediction function expression formed by the user as the change of the resource data provided by the entity object based on a plurality of dimensional features formed by the input user and the feature parameters related to the entity object, where the user forms different prediction function expressions for different entity objects.
204. And according to the prediction function expression, determining the resource data provided by the corresponding entity object when the conversion rate of the resource data used by the user is the highest as the first resource data which should be provided to the user by the entity object.
In order to save the resource cost of the entity object, under a normal condition, the entity object sets an upper limit value on the numerical value of the resource data in advance, if the numerical value of the first resource data which is determined to be issued to the user by the entity object exceeds the upper limit value set by the entity object according to a prediction function expression of the resource data provided by the user to the entity object, the entity object cannot provide sufficient resource data for the user, the resource data with a difference amount needs to be provided for the user by virtue of the resource issuing platform, and then the entity object and the resource data provided by the resource issuing platform are combined to form the sufficient first resource data; if the value of the first resource data which is determined to be issued to the user by the entity object does not exceed the upper limit value set by the entity object according to the prediction function expression of the resource data provided by the user to the entity object, the entity object can issue the first resource data with the full amount to the user without providing any resource data by virtue of a resource issuing platform. Of course, if the resource issuing platform still cannot provide sufficient resource data to the user, the value of the resource data provided by the corresponding entity object when the conversion rate of the resource data used by the user is the highest can be found out in the range of the value of the resource data provided by the entity object, and the value is used as the first resource data to be issued to the user by the entity object. For example, the value range of the resource data provided by the entity object is 3-7, and with the prediction function expression output by the conversion rate model, the value of the resource data provided by the entity object corresponding to the highest conversion rate of the resource data used by the user is 15, while the value of the resource data provided by the resource issuing platform cannot provide the redundant resource data is 8, so that the value range of the resource data provided by the entity object corresponding to the highest conversion rate of the resource data used by the user is 6, and the value is used as the first resource data to be issued to the user by the entity object.
205. And respectively aggregating the users with the same attribute characteristics and the entity objects with the same attribute characteristics by using the characteristic parameters related to the users and the entity objects to obtain a target user group and a target entity object group.
It can be understood that, the first resource data that is determined according to the prediction function expression and is to be issued to the user by the entity object exists a personalized resource data value for each entity object for each user, and a large amount of data support is required in the actual resource data issuing process. In order to save resource occupation, similar users and similar entity objects may be aggregated to obtain a target user group containing the same attribute characteristics and a target entity object group containing the same attribute characteristics.
For example, users A1-A5 contain the same attribute characteristics, then users A1-A5 belong to a similar group of users, entity objects B1-B5 contain the same attribute characteristics, then entity objects B1-B5 belong to a similar group of entity objects.
206. And determining second resource data which should be issued to a target user group by the target entity object group according to the first resource data which should be issued to the user by the entity object.
Because each user can determine a resource data value corresponding to each entity object, the resource data value to be issued to each user in the target entity object group can be used as a sample for the resource data value to be issued to each user in the target user group by the target entity object group, and the average value of a plurality of samples is obtained as second resource data to be issued to the target user group by the target entity object group.
For example, if the users a1-a5 are target user groups and the users B1-B5 belong to target entity object groups, then 25 samples are mapped to the entity objects B1-B5 for the users a1-a5, respectively, and an average value of the 25 samples is obtained as second resource data to be issued to the target user groups by the target entity object groups.
207. And issuing the second resource data to the target user group.
It is to be understood that, in the process of issuing the second resource data to the user, in order to improve the conversion rate of the user using the resource data, the issuing form of the resource data may also be set, for example, the number of the resource data issued for the user with the lower frequency of using the resource data is greater than the number of the resource data issued for the user with the higher frequency of using the resource data, so as to promote the user with the lower frequency of using the resource data to use.
Further, as a specific implementation of the method in fig. 1 and fig. 2, an embodiment of the present application provides an apparatus for issuing resource data, and as shown in fig. 3, the apparatus includes: an acquisition unit 31, a prediction unit 32, a determination unit 33, and an issuing unit 34.
An obtaining unit 31, configured to obtain a feature parameter related to a user and an entity object;
the prediction unit 32 may be configured to form a plurality of dimensional features from feature parameters related to the user and the entity object, input the dimensional features into a conversion rate model constructed in advance, and obtain a prediction function expression, where the conversion rate model outputs the prediction function expression based on the input dimensional features, and the prediction function expression is used to describe a relationship between a conversion rate of the user using the resource data and a change of the resource data provided by the entity object;
the first determining unit 33 may be configured to determine, according to the prediction function expression, resource data provided by the entity object corresponding to the case where the conversion rate of the resource data used by the user is the highest, as first resource data to be issued to the user by the entity object;
the issuing unit 34 may be configured to issue the first resource data to a user.
Compared with the resource data distribution mode in which the numerical value of the resource data is determined by manual experience in the existing mode, the resource data distribution device provided by the embodiment of the invention takes the characteristic parameters related to the user and the entity object into consideration, forms a plurality of dimensional characteristics by the characteristic parameters related to the user and the entity object and inputs the dimensional characteristics into the pre-constructed conversion rate model, outputs the prediction function expression of the resource data provided by the user to the entity object based on the input dimensional characteristics by using the conversion rate model, the prediction function expression is used for describing the relation between the conversion rate of the resource data used by the user and the change of the resource data provided by the entity object, further provides the resource data by the entity object corresponding to the maximum conversion rate of the resource data used by the user as the resource data correspondingly distributed by the user, so that the user obtains the resource data with the optimal numerical value, the user is more willing to participate in the interaction of the resource data, the resource data are accurately distributed, and the return rate of the resource data is improved.
In a specific application scenario, as shown in fig. 4, the feature parameters related to the user and the entity object include a user feature parameter, an entity object feature parameter, and a feature parameter of interaction between the user and the entity object, where the obtaining unit 31 includes:
the first obtaining module 311 may be configured to analyze user behavior data by using a data model trained by a user tag, and obtain a user characteristic parameter;
the second obtaining module 312 may be configured to analyze a browsing sequence of the user to the entity object within a first preset time by using a mapping model trained by the node sequence of the entity object, and obtain a characteristic parameter of the entity object;
the third obtaining module 313 may be configured to obtain the feature parameter of the interaction between the user and the entity object by counting access data of the user to the entity object within a second preset time after the entity object provides the resource data.
In a specific application scenario, as shown in fig. 4, the second obtaining module 312 includes:
the mapping submodule 3121 may be configured to map, using a mapping model trained by a node sequence of an entity object, a browsing sequence of the entity object within a first preset time by the user into a spatially distributed expression of the entity object;
the calculating sub-module 3122 may be configured to calculate, according to the spatially distributed expression of the entity objects, connection information between the entity objects, and obtain the entity object characteristic parameters.
In a specific application scenario, as shown in fig. 4, the third obtaining module 313 includes:
a collecting sub-module 3131, which may be configured to collect behavior data related to a user operation in the entity object data by performing data burial on the entity object after the entity object provides the resource data;
the statistic sub-module 3132 may be configured to, based on the behavior data related to the user operation in the entity object data, count interaction data of the user with respect to the entity object within a second preset time, and obtain a feature parameter of interaction between the user and the entity object.
In a specific application scenario, as shown in fig. 4, a plurality of decision trees are built in the conversion rate model, each decision tree is used for predicting, from different dimensional features, a relationship between a conversion rate of a user using resource data and a change of the resource data provided by an entity object, and the prediction unit 32 includes:
the distribution module 321 is configured to form a plurality of dimensional features by using a pre-constructed conversion rate model and distribute the feature parameters related to the user and the entity object to each leaf node in the decision tree, where each leaf node corresponds to a parameter of feature mapping;
the prediction module 322 may be configured to predict a decision tree with an optimal structure according to parameters of feature mappings corresponding to each leaf node and the leaf node in the decision tree, and use the decision tree with the optimal structure as a prediction function expression.
In a specific application scenario, as shown in fig. 4, the prediction module 322 includes:
the sorting submodule 3221 may be configured to sort, according to gradient data, each leaf node in the decision tree from large to small, and list segmentation points at which the decision tree is segmented;
the scoring sub-module 3222 may be configured to score, for a parameter of the feature mapping corresponding to each leaf node in the decision tree, a segmentation point at which the decision tree is segmented;
the prediction sub-module 3223 may be configured to use a decision tree formed by the segmentation point with the highest score as a decision tree with an optimal structure, so as to obtain a prediction function expression.
In a specific application scenario, as shown in fig. 4, the apparatus further includes:
the aggregation unit 35 may be configured to, after determining target resource data to be issued to the user by the entity object according to the prediction function expression of the resource data provided by the user to the entity object, respectively aggregate users having the same attribute characteristics and aggregate entity objects having the same attribute characteristics by using the characteristic parameters related to the user and the entity object, so as to obtain a target user group and a target entity object group;
a second determining unit 36, configured to determine, according to the first resource data that the entity object should be issued to the user, second resource data that the target entity object group should be issued to the target user group;
the issuing unit 34 may be further configured to issue the second resource data to the target user group.
It should be noted that, other corresponding descriptions of the functional units related to the apparatus for issuing resource data provided in this embodiment may refer to the corresponding descriptions in fig. 1 and fig. 2, and are not described herein again.
Based on the method shown in fig. 1 and fig. 2, correspondingly, an embodiment of the present application further provides a storage medium, on which a computer program is stored, and the computer program, when executed by a processor, implements the method for issuing resource data shown in fig. 1 and fig. 2.
Based on such understanding, the technical solution of the present application may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a usb disk, a removable hard disk, etc.), and includes several instructions for enabling a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method according to the implementation scenarios of the present application.
Based on the method shown in fig. 1 and fig. 2 and the virtual device embodiment shown in fig. 3 and fig. 4, in order to achieve the above object, an embodiment of the present application further provides an entity device for issuing resource data, which may specifically be a computer, a smart phone, a tablet computer, a smart watch, a server, or a network device, and the entity device includes a storage medium and a processor; a storage medium for storing a computer program; a processor for executing a computer program to implement the method for issuing resource data as shown in fig. 1 and fig. 2.
Optionally, the entity device may further include a user interface, a network interface, a camera, a Radio Frequency (RF) circuit, a sensor, an audio circuit, a WI-FI module, and the like. The user interface may include a Display screen (Display), an input unit such as a keypad (Keyboard), etc., and the optional user interface may also include a USB interface, a card reader interface, etc. The network interface may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface), etc.
Those skilled in the art will appreciate that the entity device structure for issuing resource data provided in the present embodiment is not limited to the entity device, and may include more or less components, or combine some components, or arrange different components.
The storage medium may further include an operating system and a network communication module. The operating system is a program for managing hardware and software resources of the actual device for store search information processing, and supports the operation of the information processing program and other software and/or programs. The network communication module is used for realizing communication among components in the storage medium and communication with other hardware and software in the information processing entity device.
Through the above description of the embodiments, those skilled in the art will clearly understand that the present application can be implemented by software plus a necessary general hardware platform, and can also be implemented by hardware. By applying the technical scheme, compared with the existing mode, the method and the device have the advantages that the relation between the conversion rate of the resource data used by the user and the change of the resource data provided by the entity object is predicted by considering the characteristic parameters related to the user and the entity object, and then the resource data provided by the entity object corresponding to the maximum conversion rate of the resource data used by the user is used as the resource data correspondingly provided by the user, so that the user can obtain the resource data with the optimal numerical value, the resource data can be accurately provided, and the return rate of the resource data can be improved.
Those skilled in the art will appreciate that the figures are merely schematic representations of one preferred implementation scenario and that the blocks or flow diagrams in the figures are not necessarily required to practice the present application. Those skilled in the art will appreciate that the modules in the devices in the implementation scenario may be distributed in the devices in the implementation scenario according to the description of the implementation scenario, or may be located in one or more devices different from the present implementation scenario with corresponding changes. The modules of the implementation scenario may be combined into one module, or may be further split into a plurality of sub-modules.
The above application serial numbers are for description purposes only and do not represent the superiority or inferiority of the implementation scenarios. The above disclosure is only a few specific implementation scenarios of the present application, but the present application is not limited thereto, and any variations that can be made by those skilled in the art are intended to fall within the scope of the present application.
Claims (10)
1. A method for issuing resource data is characterized by comprising the following steps:
acquiring characteristic parameters related to a user and an entity object;
forming a plurality of dimensional characteristics of the characteristic parameters related to the user and the entity object, inputting the dimensional characteristics into a pre-constructed conversion rate model to obtain a prediction function expression, wherein the conversion rate model outputs the prediction function expression based on the input dimensional characteristics, and the prediction function expression is used for describing the relation between the conversion rate of the resource data used by the user and the change of the resource data provided by the entity object;
according to the prediction function expression, determining the resource data provided by the corresponding entity object when the conversion rate of the resource data used by the user is the highest as the first resource data which should be provided to the user by the entity object;
and issuing the first resource data to a user.
2. The method according to claim 1, wherein the feature parameters related to the user and the entity object include a user feature parameter, an entity object feature parameter, and a feature parameter for interaction between the user and the entity object, and the obtaining the feature parameters related to the user and the entity object specifically includes:
analyzing user behavior data by using a data model trained by a user label to obtain user characteristic parameters;
analyzing a browsing sequence of a user to the entity object within a first preset time by using a mapping model trained by the node sequence of the entity object to obtain characteristic parameters of the entity object;
and counting the access data of the user to the entity object within a second preset time after the entity object provides the resource data, and acquiring the characteristic parameters of the interaction between the user and the entity object.
3. The method according to claim 2, wherein the analyzing a browsing sequence of the user to the entity object within a first preset time by using the mapping model trained by the node sequence of the entity object to obtain the characteristic parameters of the entity object specifically comprises:
mapping a browsing sequence of the user to the entity object in a first preset time into a spatially distributed expression of the entity object by using a mapping model trained by the node sequence of the entity object;
and calculating the connection information between the entity objects according to the distributed expression of the entity objects on the space, and acquiring the characteristic parameters of the entity objects.
4. The method according to claim 2, wherein the obtaining of the feature parameters of the interaction between the user and the entity object by counting the interaction data of the user to the entity object within a second preset time after the entity object provides the resource data specifically comprises:
performing data embedding on the entity object after the entity object provides the resource data, and collecting behavior data related to user operation in the entity object data;
and counting the interaction data of the user to the entity object within a second preset time based on the behavior data related to the user operation in the entity object data, and acquiring the characteristic parameters of the interaction between the user and the entity object.
5. The method according to any one of claims 1 to 4, wherein a plurality of decision trees are established in the conversion rate model, each decision tree is used for predicting a relation between changes of conversion rate of resource data used by a user along with resource data provided by an entity object from different dimensional features, and the feature parameters related to the user and the entity object form a plurality of dimensional features and are input into the conversion rate model established in advance to obtain a prediction function expression, specifically comprising:
forming a plurality of dimensional characteristics by using the characteristic parameters related to the user and the entity object by using a pre-constructed conversion rate model, distributing the dimensional characteristics to each leaf node in a decision tree, wherein each leaf node corresponds to a parameter of characteristic mapping;
and predicting the decision tree of the optimal structure according to the parameters of the feature mapping corresponding to each leaf node and the leaf node in the decision tree, and taking the decision tree of the optimal structure as a prediction function expression.
6. The method according to claim 5, wherein the predicting the decision tree with the optimal structure according to the parameters of the feature mapping corresponding to each leaf node and the leaf node in the decision tree, and using the decision tree with the optimal structure as the prediction function expression specifically comprises:
sequencing each leaf node in the decision tree according to gradient data from big to small, and listing cutting points for cutting the decision tree;
scoring the segmentation points for segmenting the decision tree aiming at the parameters of the feature mapping corresponding to each leaf node in the decision tree;
and taking a decision tree formed by the segmentation points with the highest score as a decision tree of an optimal structure to obtain a prediction function expression.
7. The method of claim 1, wherein after determining target resource data that an entity object should be issued to a user according to the prediction function expression of the resource data provided by the user to the entity object, the method further comprises:
respectively aggregating users containing the same attribute characteristics and aggregating entity objects containing the same attribute characteristics by using the characteristic parameters related to the users and the entity objects to obtain a target user group and a target entity object group;
determining second resource data which should be issued to a target user group by the target entity object group according to the first resource data which should be issued to the user by the entity object;
the issuing of the target resource data to the user specifically includes:
and issuing the second resource data to the target user group.
8. An apparatus for issuing resource data, comprising:
the acquiring unit is used for acquiring characteristic parameters related to a user and an entity object;
the prediction unit is used for inputting a plurality of dimensional characteristics formed by characteristic parameters related to the user and the entity object into a conversion rate model constructed in advance to obtain a prediction function expression, wherein the conversion rate model outputs the prediction function expression based on the input plurality of dimensional characteristics, and the prediction function expression is used for describing the relation between the conversion rate of the resource data used by the user and the change of the resource data provided by the entity object;
the first determining unit is used for determining the resource data provided by the corresponding entity object when the conversion rate of the resource data used by the user is the highest as the first resource data which is to be distributed to the user by the entity object according to the prediction function expression;
and the issuing unit is used for issuing the first resource data to the user.
9. A storage medium on which a computer program is stored, the program, when executed by a processor, implementing the method of issuing resource data of any one of claims 1 to 7.
10. A resource data issuing apparatus comprising a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor, wherein the processor implements the resource data issuing method according to any one of claims 1 to 7 when executing the program.
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