CN113760521B - Virtual resource allocation method and device - Google Patents
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Abstract
The invention discloses a virtual resource allocation method and device, and relates to the technical field of Internet. One embodiment of the method comprises the following steps: acquiring historical behavior data of a first user, wherein the historical behavior data comprises: the first user's characteristics with respect to the virtual resource; obtaining behavior preference of a first user about virtual resources according to historical behavior data by using a first classification model, wherein the first classification model is obtained by training according to the historical behavior data of a plurality of second users; predicting the probability of using the virtual resources to be allocated by the first user by using a prediction model according to the behavior preference and the attributes of the virtual resources to be allocated, wherein the prediction model is obtained by training according to historical behavior data of using the virtual resources by a plurality of second users and the attributes of the virtual resources used by the second users; and when the probability is greater than a preset threshold, the virtual resource to be allocated is allocated to the first user. The embodiment improves the accuracy of virtual resource allocation and the utilization rate of the virtual resources.
Description
Technical Field
The present invention relates to the field of internet technologies, and in particular, to a method and an apparatus for allocating virtual resources.
Background
In the field of electronic commerce, with the wide application of mobile payment, an electronic commerce platform generally pushes a message to a user on a website or an app in a popup window or other mode to remind the user of obtaining virtual resources provided by the platform.
In the process of implementing the present invention, the inventor finds that at least the following problems exist in the prior art:
At present, an e-commerce platform generally provides virtual resources for users in a random mode, and does not distinguish the users, so that the allocation of the virtual resources is not accurate enough, the utilization rate of the virtual resources provided by the platform is low, and the virtual resources are wasted.
Disclosure of Invention
In view of this, the embodiments of the present invention provide a method and an apparatus for allocating virtual resources, which can obtain a behavior preference of a first user according to historical behavior data of the first user, predict a probability that the first user uses a virtual resource to be allocated according to the behavior preference of the first user with respect to the virtual resource and an attribute of the virtual resource to be allocated, and allocate the virtual resource to be allocated to the first user with a high use probability, thereby improving accuracy of allocating the virtual resource and usage rate of the virtual resource.
In order to achieve the above object, according to an aspect of an embodiment of the present invention, there is provided a virtual resource allocation method.
The method for distributing the virtual resources comprises the following steps:
Acquiring historical behavior data of a first user, wherein the historical behavior data comprises: the first user's characteristics with respect to the virtual resource;
Obtaining behavior preference of the first user about the virtual resource according to the historical behavior data by using the first classification model; the first classification model is trained according to historical behavior data of a plurality of second users;
predicting the probability of using the virtual resource to be allocated by the first user by using a prediction model according to the behavior preference and the attribute of the virtual resource to be allocated, wherein the prediction model is obtained by training according to historical behavior data of using the virtual resource by a plurality of second users and the attribute of the virtual resource used by the second users;
and when the probability is greater than a preset threshold, the virtual resource to be allocated is allocated to the first user.
Alternatively, the process may be carried out in a single-stage,
The attributes of the virtual resource to be allocated include: provider information of virtual resources to be allocated;
Obtaining behavior preference of the first user about the resource provider according to the historical behavior data by using the first classification model;
And predicting the probability of the first user using the virtual resource to be allocated provided by the resource provider by using the prediction model according to the behavior preference and the provider information of the virtual resource to be allocated.
Alternatively, the process may be carried out in a single-stage,
The attributes of the virtual resource to be allocated further include: the value of the virtual resource to be allocated;
and predicting the probability of using the virtual resources to be allocated provided by the resource provider by using the prediction model according to the behavior preference of the first user about the resource provider and the value of the virtual resources to be allocated.
Alternatively, the process may be carried out in a single-stage,
Obtaining historical transaction data of a resource provider, wherein the historical transaction data comprises: the corresponding resource provider provides the characteristics of the virtual resource;
Determining attribute preferences of the resource provider with respect to the virtual resource according to the historical transaction data, respectively, using a second classification model;
Obtaining behavior preference of the first user about the resource provider according to the historical behavior data by using the first classification model;
and predicting the probability of the first user using the virtual resources to be allocated provided by the resource provider by using a prediction model according to the attribute preference, the behavior preference of the first user about the resource provider and the attribute of the virtual resources to be allocated.
Alternatively, the process may be carried out in a single-stage,
The method further comprises the steps of:
And determining a plurality of original features of the historical behavior data of the first user, and inputting the plurality of original features into a feature selector to obtain the features of the first user about the virtual resource.
Alternatively, the process may be carried out in a single-stage,
The predictive model is trained from a random forest algorithm, and any three of GBDT algorithm, catboost algorithm, lightGBM algorithm, XGBoost algorithm, and LR algorithm.
Alternatively, the process may be carried out in a single-stage,
Three initial models are respectively constructed by utilizing any three of GBDT algorithm, catboost algorithm, lightGBM algorithm, XGBoost algorithm and LR algorithm;
Respectively inputting the historical behavior data of the second user into the three initial models to train the three initial models;
And taking the output of the three initial models after training and the behavior preference of the second user about the virtual resources, which is output by the first classification model, as the input of a random forest algorithm so as to train the prediction model.
Alternatively, the process may be carried out in a single-stage,
The first classification model and the second classification model are trained based on a clustering algorithm.
Alternatively, the process may be carried out in a single-stage,
The feature selector is based on feature_selection library.
In order to achieve the above object, according to still another aspect of the embodiments of the present invention, there is provided an allocation apparatus of virtual resources.
The device for distributing virtual resources in the embodiment of the invention comprises: the system comprises a data acquisition module, a classification module, a prediction module and an allocation module; wherein,
The data acquisition module is used for acquiring historical behavior data of the first user, wherein the historical behavior data comprises: the first user's characteristics with respect to the virtual resource;
The classification module is used for obtaining the behavior preference of the first user about the virtual resource according to the historical behavior data acquired by the data acquisition module by utilizing the first classification model; the first classification model is trained according to historical behavior data of a plurality of second users;
The prediction module is used for predicting the probability of the first user using the virtual resource to be allocated by utilizing the prediction model according to the behavior preference and the attribute of the virtual resource to be allocated, which are obtained by training according to the historical behavior data of the plurality of second users using the virtual resource and the attribute of the virtual resource used by the second users;
And the allocation module is used for allocating the virtual resources to be allocated to the first user when the probability predicted by the prediction module is greater than a preset threshold value.
To achieve the above object, according to still another aspect of the embodiments of the present invention, there is provided a virtual resource allocation server.
The virtual resource allocation server of the embodiment of the invention comprises the following components: one or more processors; and the storage device is used for storing one or more programs, and when the one or more programs are executed by the one or more processors, the one or more processors realize the virtual resource allocation method according to the embodiment of the invention.
To achieve the above object, according to still another aspect of the embodiments of the present invention, there is provided a computer-readable storage medium.
A computer-readable storage medium of an embodiment of the present invention stores a computer program thereon, which when executed by a processor implements a virtual resource allocation method of an embodiment of the present invention.
One embodiment of the above invention has the following advantages or benefits: after the historical behavior data of the first user are obtained, the historical behavior data containing the relevant characteristics of the virtual resources are firstly input into a first classification model to obtain the behavior preference of the first user about the virtual resources, then the behavior preference and the attribute of the virtual resources to be allocated are input into a prediction model to predict the probability of the first user using the virtual resources to be allocated, and if the obtained probability is larger than a first threshold value, the virtual resources to be allocated are allocated to the first user. As can be seen from the above description, the embodiment of the present invention can divide the first user into corresponding user types according to the historical behavior data of the first user, obtain the behavior preference of the first user according to the behavior preference of the corresponding user types, predict the probability that the first user uses the virtual resource to be allocated according to the behavior preference of the first user about the virtual resource and the attribute of the virtual resource to be allocated, and allocate the virtual resource to be allocated to the first user with high use probability, thereby improving the accuracy of virtual resource allocation and the use rate of the virtual resource.
Further effects of the above-described non-conventional alternatives are described below in connection with the embodiments.
Drawings
The drawings are included to provide a better understanding of the invention and are not to be construed as unduly limiting the invention. Wherein:
FIG. 1 is a schematic diagram of main steps of a virtual resource allocation method according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of main steps of another virtual resource allocation method according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of the main steps of a predictive model training method according to an embodiment of the invention;
FIG. 4 is a schematic diagram of the main steps of yet another virtual resource allocation method according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of main steps of a virtual resource allocation method according to still another embodiment of the present invention;
FIG. 6 is a schematic diagram of the main modules of a virtual resource allocation apparatus according to an embodiment of the present invention;
FIG. 7 is an exemplary system architecture diagram in which embodiments of the present invention may be applied;
Fig. 8 is a schematic diagram of a computer system suitable for use in implementing an embodiment of the invention.
Detailed Description
Exemplary embodiments of the present invention will now be described with reference to the accompanying drawings, in which various details of the embodiments of the present invention are included to facilitate understanding, and are to be considered merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the invention. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
It should be noted that the embodiments of the present invention and the technical features in the embodiments may be combined with each other without collision.
Fig. 1 is a schematic diagram of main steps of a virtual resource allocation method according to an embodiment of the present invention.
As shown in fig. 1, a virtual resource allocation method in an embodiment of the present invention mainly includes the following steps:
Step S101: acquiring historical behavior data of a first user, wherein the historical behavior data comprises: the first user is characterized about the virtual resource.
In the embodiment of the invention, when the historical behavior data of the first user not only comprises the characteristics of the first user about the virtual resource, but also comprises the characteristics of the first user irrelevant to the virtual resource, the characteristics of the first user about the virtual resource can be screened from the historical behavior data of the first user, and then the subsequent steps are carried out. For example, the historical behavior data of a certain first user includes: the method comprises the steps of logging in by a first user, browsing commodities within the last 30 days of the first user, consuming the commodities by the first user, average consumption amount of each consumption of the first user, the number of times the first user obtains virtual resources, the number of times the first user uses the virtual resources, the amount of the virtual resources used by the first user, average time interval from obtaining the virtual resources to using the virtual resources by the first user, and the minimum consumption amount after the first user uses the virtual resources. From the above historical behavior data, it can be seen that: the number of logins of the first user, the commodities browsed by the first user in the last 30 days, the number of consumption of the first user and the average amount of consumption of each of the first user belong to characteristics irrelevant to the virtual resources, the number of times the first user takes the virtual resources, the number of times the first user uses the virtual resources, the amount of the virtual resources used by the first user, the average time interval from the taking of the virtual resources to the use of the virtual resources and the lowest consumption amount after the first user uses the virtual resources are relevant to the virtual resources, and at the moment, the characteristics irrelevant to the virtual resources can be filtered by using a characteristic selector, and the characteristics relevant to the virtual resources are screened out and then the follow-up steps are carried out.
Therefore, as shown in fig. 2, the embodiment of the present invention further provides another virtual resource allocation method, which mainly includes the following steps S201 to S206:
step S201: acquiring historical behavior data of a first user;
step S202: determining a plurality of original features of the historical behavior data of the first user, wherein the original features include a feature of the first user with respect to the virtual resource and a feature of the first user that is unrelated to the virtual resource;
Step S203: inputting a plurality of original features into a feature selector to obtain the features of the first user about the virtual resource;
Step S204: obtaining behavior preference of the first user about the virtual resource according to the characteristics of the first user about the virtual resource by using a first classification model, wherein the first classification model is obtained by training according to historical behavior data of a plurality of second users;
Step S205: predicting the probability of using the virtual resource to be allocated by the first user by using a prediction model according to the behavior preference and the attribute of the virtual resource to be allocated, wherein the prediction model is obtained by training according to historical behavior data of using the virtual resource by a plurality of second users and the attribute of the virtual resource used by the second users;
step S206: and when the probability is greater than a preset threshold, the virtual resource is allocated to the first user.
In the embodiment of the present invention, the feature selector may be obtained based on a feature_ sel ection library in Python, and is used for rejecting irrelevant features and redundant features in the historical behavior data of the first user according to the requirement of the model in the subsequent step: irrelevant features, i.e. features with low relevance to the model, such as features of the first user that are not related to the virtual resource, i.e. belong to the category, should be rejected; redundant features, i.e., non-divergent features, such as features with variance close to 0, indicate that historical behavior data contains this feature, and that this feature contains little information and can therefore also be culled. The feature selector can reduce the feature quantity, reduce the data dimension and improve the accuracy of the output result of the follow-up model.
Step S102: obtaining behavior preference of the first user about the virtual resource according to the historical behavior data by using the first classification model; the first classification model is trained based on historical behavioral data of a plurality of second users.
In the embodiment of the invention, because the first users with similar historical behavior data may have similar behavior preferences for the virtual resources with different attributes, the first user can be classified by using the first classification model to obtain the behavior preferences of the first user about the virtual resources. The first classification model may compare historical behavior data of the first user with parameters corresponding to existing user categories in the first classification model, and classify the first user into appropriate user categories according to comparison results, so as to obtain behavior preferences of the first user about the virtual resources according to behavior preferences of the user to the virtual resources. For example, the historical behavior data of a first user is displayed, in the last month, the first user takes five times of virtual resources and uses two times of virtual resources, and the first classification model compares the data with the parameters corresponding to the existing user categories to obtain that the first user belongs to the user category of "occasionally uses virtual resources", so that the behavior preference of the first user about virtual resources can be "occasionally used" according to the behavior preference of the user category, and the behavior preference of the first user about "occasionally uses" virtual resources can be obtained.
In the embodiment of the invention, the first classification model is trained by using a clustering algorithm according to historical behavior data of a plurality of second users, and the clustering algorithm can be DBSCAN (Dens ity-Based Spatial Clustering of Applications with Noise, a spatial clustering algorithm with noise based on density) or K-means algorithm. The second user may be a user having similar historical behavioral data to the first user, e.g., the first user and the second user are users using the same e-commerce platform. In a preferred embodiment of the invention, the first classification model is based on DBSCAN training. The training process for the first classification model specifically includes: inputting a predefined plurality of original features into a feature selector to obtain sample features, wherein the sample features comprise: features related to virtual resources; acquiring historical behavior data of a plurality of second users according to the sample characteristics; the method comprises the steps of inputting historical behavior data of a plurality of second users into a first classification model, dividing the plurality of second users into different user categories according to the historical behavior data, a preset neighborhood distance threshold epsilon and a preset sample number threshold MinPts in the neighborhood, and outputting the number of the user categories obtained by dividing, wherein each user category may have different behavior preference on the same virtual resource. For example, if the output trained by the first classification model is "class X users", it means that the input plurality of second users are classified into classes of users having different classes X.
Step S103: and predicting the probability of the first user using the virtual resource to be allocated by using a prediction model according to the behavior preference and the attribute of the virtual resource to be allocated, wherein the prediction model is trained according to historical behavior data of the plurality of second users using the virtual resource and the attribute of the virtual resource used by the second users.
Step S104: and when the probability is greater than a preset threshold, the virtual resource to be allocated is allocated to the first user.
In an embodiment of the invention, the predictive model is trained from a random forest algorithm, and any three of GBDT algorithm, catboost algorithm, lightGBM algorithm, XGBoost algorithm, and LR algorithm. As shown in fig. 3, the training method of the prediction model mainly includes the following steps S301 to S306:
Step S301: three initial models were constructed as the first layer of the predictive model using any three of GBDT algorithm, catboost algorithm, lightGBM algorithm, XGBoost algorithm, and LR algorithm, respectively.
In a preferred embodiment of the present invention, three initial models are constructed by selecting GBDT algorithm, catboost algorithm, and LightGBM algorithm, respectively.
Step S302: the historical behavior data of the second user is randomly divided into a training set and a verification set.
For example, the training set may include 80% of the historical behavior data and the validation set may include another 20% of the historical behavior data.
Step S303: and respectively inputting the training set into the three initial models to train the three initial models to obtain importance scores of the features contained in the training set, and taking the features which are more than the median of the importance scores and are contained in the training set as a first output of a first layer of the prediction model.
Step S304: and respectively inputting the verification set into three initial models to respectively carry out parameter adjustment optimization on the three initial models, obtaining importance scores of the features contained in the verification set, and taking the features which are more than the median of the importance scores and are contained in the verification set as a second output of the first layer of the prediction model.
Step S305: and taking the random forest algorithm as a second layer of the prediction model, inputting the first output and the second output of the first layer of the trained prediction model and the behavior preference of the second user about the virtual resource output by the first classification model into the random forest algorithm, and training the second layer of the prediction model.
Step S306: and randomly selecting partial data from the historical behavior data of the second user as a test set, and performing parameter adjustment optimization on the second layer of the prediction model.
In a preferred embodiment of the invention, the prediction model is trained according to a random forest algorithm, a GBDT algorithm, a Catboost algorithm and a LightGBM algorithm, and the running speed and the result accuracy of the prediction model trained by the four algorithms are higher. Wherein GBDT algorithm generates a weak classifier through multiple iterations, each iteration generates a weak classifier, a loss function is enabled to descend along a gradient direction, each classifier is trained on the residual error basis of the previous classifier, and finally the weak classifier obtained through each training is weighted and summed to obtain a final total classifier; the Catboost algorithm can automatically process the category characteristics in a special mode, namely, firstly, some statistics is carried out on the category characteristics, the occurrence frequency of certain category characteristics is calculated, and super parameters are added to generate new numerical type characteristics so as to avoid manual processing of the category characteristics, the Catboost algorithm also uses combined category characteristics, the feature dimension is greatly enriched, and the model is prevented from being fitted excessively; the LightGBM algorithm is a decision tree algorithm based on a histogram algorithm, which uses a leaf-based growth algorithm with depth limitation, and a maximum depth limitation is added on the algorithm to ensure high efficiency and prevent overfitting, and the algorithm optimizes the support for category characteristics, can directly input the category characteristics, has good efficiency and expandability when facing high-dimension big data, and has three times of the processing speed of the common algorithm; the random forest algorithm improves the establishment of the decision tree, and the generalization capability of the model is further enhanced by randomly selecting a part of sample features on the nodes and then selecting an optimal feature from the sample features to make the left and right subtree division of the decision tree.
In a preferred embodiment of the invention, the above four algorithms can be fused by using a Blending method to obtain an optimal prediction model, and further improve the overall performance of the prediction model.
In the embodiment of the invention, the output of the first classification model, namely the behavior preference of the first user on the virtual resource and the attribute of the virtual resource to be allocated, are input into the prediction model, so that the probability that the first user uses the virtual resource to be allocated can be obtained, wherein the attribute of the virtual resource to be allocated comprises information related to the virtual resource to be allocated. The information included in the attributes of the virtual resource to be allocated may be different, for example, may include its provider information, and may also include its own value.
The method for allocating virtual resources according to the embodiment of the present invention is described below by taking, as an example, provider information of the virtual resources to be allocated included in the attribute of the virtual resources to be allocated.
As shown in fig. 4, an embodiment of the present invention provides a method for allocating virtual resources, which mainly includes the following steps S401 to S404:
step S401: acquiring historical behavior data of a first user, wherein the historical behavior data comprises: the first user is characterized about the virtual resource.
Step S402: and obtaining the behavior preference of the first user about the resource provider according to the historical behavior data by using the first classification model.
In the embodiment of the invention, because the first users with similar historical behavior data may have similar behavior preferences for different resource providers, the first user can be classified by using the first classification model to obtain the behavior preferences of the first user about the resource providers. The resource provider is a provider of a virtual resource, for example, when the virtual resource is a virtual resource on an e-commerce platform, the resource provider may be an operator of the e-commerce platform or a seller of the e-commerce platform, and the virtual resource may be an electronic ticket capable of participating in an event or a coupon capable of exchanging a preferential amount, service or product, etc.
For example, according to the historical behavior data of a first user, the first user uses the virtual resources related to the originality provided by a resource provider twice in a month, and the first classification model may classify the first user into a user category like browsing the originality resource provider to indicate that the first user has a higher behavior preference for the resource provider providing the originality.
Step S403: and predicting the probability of the first user using the virtual resource to be allocated provided by the resource provider by using the prediction model according to the behavior preference and the provider information of the virtual resource to be allocated.
In the embodiment of the present invention, according to the provider information of the to-be-allocated virtual resource included in the attribute of the to-be-allocated virtual resource, the prediction model may compare the behavior preference of the first user about the resource provider with the provider information of the to-be-allocated virtual resource, so as to predict the probability that the first user uses the to-be-allocated virtual resource provided by the resource provider. For example, if the first user has a higher behavior preference for the resource provider providing the article of the literature-on-article, and the resource provider indicated by the provider information of the virtual resource to be allocated is provided with the learning-class electronic product, the probability that the first user uses the virtual resource to be allocated provided by the resource provider is higher; whereas if the first user has a higher behavioral preference for the resource provider providing the articles of the literature, but the resource provider indicated by the provider information of the virtual resource to be allocated is that of the furniture item, the probability that the first user uses the virtual resource to be allocated provided by the resource provider is low.
In the embodiment of the present invention, the attribute of the virtual resource to be allocated may further include: the value of the virtual resource to be allocated. The predictive model may predict a probability that the first user uses the virtual resource to be allocated provided by the resource provider based on the first user's behavioral preferences with respect to the resource provider and the value of the virtual resource to be allocated. For example, if the first user has a higher behavior preference for a resource provider that provides an item with a mean price of 30 yuan, and the value of the virtual resource to be allocated is a full 50 minus 6, the probability that the first user uses the virtual resource to be allocated provided by the resource provider is higher; if the first user has a higher behavior preference on a resource provider that provides an item with a mean price of 30 yuan, but the value of the virtual resource to be allocated is 1000 minus 100, the probability that the first user uses the virtual resource to be allocated provided by the resource provider is lower.
Step S404: and when the probability is greater than a preset threshold, the virtual resource to be allocated is allocated to the first user.
In addition, in a preferred embodiment of the present invention, historical transaction data of the resource provider may be further obtained, and according to the historical behavior data of the first user and the historical transaction data of the resource provider, the probability that the first user uses the virtual resource to be allocated provided by the resource provider is predicted, so as to further improve accuracy of the prediction result, and further improve accuracy of allocating the virtual resource to be allocated provided by the resource provider to the target first user. As shown in fig. 5, an embodiment of the present invention provides a method for allocating virtual resources, which mainly includes the following steps S501 to S505:
Step S501: acquiring historical behavior data of a first user and historical transaction data of a resource provider, wherein the historical behavior data comprises: the first user's characteristics with respect to the virtual resource; the historical transaction data includes: the corresponding resource provider provides the characteristics of the virtual resource.
In the embodiment of the invention, when the historical behavior data of the first user not only comprises the characteristics of the first user about the virtual resource, but also comprises the characteristics of the first user irrelevant to the virtual resource, the characteristics of the first user about the virtual resource can be screened out from the historical behavior data of the first user by using the characteristic selector, and then the subsequent steps are carried out. Similarly, when the historical transaction data of the resource provider includes both the feature that the resource provider provides the virtual resource and the feature that the resource provider does not have a relation to the virtual resource, the feature selector may be used to screen the feature that the resource provider provides the virtual resource from the historical transaction data of the resource provider, and then the subsequent steps are performed.
In an embodiment of the present invention, the characteristics of the virtual resource provided by the resource provider may include: the value of the virtual resource provided by the resource provider, the number of times the virtual resource provided by the resource provider, the number of virtual resources that the virtual resource provided by the resource provider is to be taken, the number of virtual resources that the resource provider provides to be used, the taking-use rate of the virtual resource provided by the resource provider, and the like.
Step S502: and respectively determining attribute preference of the resource provider about the virtual resource according to the historical transaction data by using the second classification model.
In the embodiment of the invention, the second classification model is trained according to historical transaction data of a plurality of second users by using a clustering algorithm, and the clustering algorithm can be DBSCAN (Dens ity-Based Spatial Clustering of Applications with Noise, a spatial clustering algorithm with noise based on density) or K-means algorithm. In a preferred embodiment of the invention, the second classification model is also based on DBSCAN training. The training process for the second classification model is similar to that for the first classification model, and will not be described in detail herein.
In the embodiment of the invention, because resource providers with similar historical transaction data may have similar attribute preference for virtual resources with different attributes, the resource providers can be classified by using a second classification model to obtain the attribute preference of the resource provider about the virtual resources. The second classification model may compare historical transaction data of the resource provider with parameters corresponding to existing user categories in the second classification model, and classify the resource provider into appropriate user categories according to the comparison result, so as to obtain attribute preference of the resource provider about the virtual resource according to attribute preference of the user to the virtual resource. For example, the historical transaction data of a resource provider shows that the resource provider has sent the virtual resource related to the literature twice within one month, and the second classification model can compare the data with the parameters corresponding to the existing user category to obtain that the resource provider belongs to the user category of "frequently issued literature virtual resource", so that the attribute preference of the resource provider about the virtual resource is "frequently issued literature virtual resource" according to the attribute preference of the user category, and the resource provider also has the attribute preference of "frequently issued literature virtual resource".
Step S503: and obtaining the behavior preference of the first user about the resource provider according to the historical behavior data by using the first classification model.
In the embodiment of the invention, because the first users with similar historical behavior data may have similar behavior preferences for different resource providers, the first user can be classified by using the first classification model to obtain the behavior preferences of the first user about the resource providers. For example, according to the historical behavior data of a first user, the first user has used virtual resources related to the originality provided by a resource provider twice in a month, and the first classification model may classify the first user into a user category of "like browsing the resource provider of the originality category" to indicate that the first user has a higher behavior preference for the resource provider providing the originality category.
Step S504: and predicting the probability of the first user using the virtual resource to be allocated provided by the resource provider by using a prediction model according to the attribute preference, the behavior preference and the attribute of the virtual resource to be allocated.
In the embodiment of the present invention, since it is already known that the virtual resource to be allocated is provided by the resource provider through step S501, the provider thereof may not be included in the attribute of the virtual resource to be allocated.
In the embodiment of the invention, the attribute preference of the resource provider about the virtual resource, the behavior preference of the first user about the resource provider and the attribute of the virtual resource to be allocated are input into the prediction model, and the prediction model can predict the probability of the first user using the virtual resource to be allocated provided by the resource provider according to the corresponding relation of the three. For example, if the resource provider has an attribute preference for "frequently issued literature items" virtual resources, the first user has a higher behavioral preference for the resource provider providing the articles of the literature, the attribute of the virtual resource to be allocated indicates that the value of the virtual resource to be allocated is full 50 minus 6, and the probability that the first user uses the virtual resource to be allocated provided by the resource provider is higher; if the resource provider has the attribute preference of "frequently issuing items of the literature" virtual resource, but the first user has a lower preference for the behavior of the resource provider providing items of the literature, the probability of the first user using the virtual resource to be allocated, which is provided by the resource provider with a value of 50 minus 6 is lower.
Step S505: and when the probability is greater than a preset threshold, the virtual resource to be allocated is allocated to the first user.
In the embodiment of the invention, when the probability predicted by the prediction model is greater than the preset threshold, it is indicated that the first user is likely to use the virtual resource to be allocated, so that the virtual resource to be allocated provided by the resource provider is allocated to the first user, so that the virtual resource can be allocated to the first user with higher use probability, and the use rate of the virtual resource is further improved.
According to the virtual resource allocation method, after the historical behavior data of the first user are obtained, the historical behavior data containing the relevant characteristics of the virtual resources are input into a first classification model to obtain the behavior preference of the first user about the virtual resources, the behavior preference and the attribute of the virtual resources to be allocated are input into a prediction model, the probability that the first user uses the virtual resources to be allocated is predicted, and if the obtained probability is larger than a first threshold, the virtual resources to be allocated are allocated to the first user. As can be seen from the above description, the embodiment of the present invention can divide the first user into corresponding user types according to the historical behavior data of the first user, obtain the behavior preference of the first user according to the behavior preference of the corresponding user types, predict the probability that the first user uses the virtual resource to be allocated according to the behavior preference of the first user about the virtual resource and the attribute of the virtual resource to be allocated, and allocate the virtual resource to be allocated to the first user with high use probability, thereby improving the accuracy of virtual resource allocation and the use rate of the virtual resource.
Fig. 6 is a schematic diagram of main modules of an apparatus for allocating virtual resources according to an embodiment of the present invention.
As shown in fig. 6, a virtual resource allocation apparatus according to an embodiment of the present invention includes: a data acquisition module 601, a classification module 602, a prediction module 603, and an allocation module 604; wherein,
The data obtaining module 601 is configured to obtain historical behavior data of a first user, where the historical behavior data includes: the first user's characteristics with respect to the virtual resource;
The classification module 602 is configured to obtain, according to the historical behavior data acquired by the data acquisition module 601, a behavior preference of the first user about the virtual resource by using the first classification model; the first classification model is trained according to historical behavior data of a plurality of second users;
A prediction module 603, configured to predict, according to the behavior preference and the attribute of the virtual resource to be allocated obtained by the classification module 602, a probability of the first user using the virtual resource to be allocated by using a prediction model, where the prediction model is obtained by training according to historical behavior data of a plurality of second users using the virtual resource and the attribute of the virtual resource used by the second users;
The allocation module 604 is configured to allocate the virtual resource to be allocated to the first user when the probability predicted by the prediction module 603 is greater than a preset threshold.
In the embodiment of the invention, the attribute of the virtual resource to be allocated comprises: provider information of virtual resources to be allocated; the classification module 602 may be further configured to obtain, using the first classification model, a behavior preference of the first user with respect to the resource provider according to the historical behavior data; the prediction module 603 may be further configured to predict, according to the behavior preference and provider information of the virtual resource to be allocated, a probability that the first user uses the virtual resource to be allocated provided by the resource provider using the prediction model.
In the embodiment of the present invention, the attribute of the virtual resource to be allocated further includes: the value of the virtual resource to be allocated; the prediction module 603 may be further configured to predict, using the prediction model, a probability that the first user uses the virtual resource to be allocated provided by the resource provider according to the behavior preference of the first user with respect to the resource provider and the value of the virtual resource to be allocated.
In an embodiment of the present invention, the data obtaining module 601 may be further configured to obtain historical transaction data of a resource provider, where the historical transaction data includes: the corresponding resource provider provides the characteristics of the virtual resource; the classification module 602 may be further configured to determine attribute preferences of the resource provider with respect to the virtual resource according to the historical transaction data using the second classification model, and obtain behavior preferences of the first user with respect to the resource provider according to the historical behavior data using the first classification model; the prediction module 603 may be further configured to predict, using the prediction model, a probability that the first user uses the virtual resource to be allocated provided by the resource provider according to the attribute preference, the behavior preference of the first user with respect to the resource provider, and the attribute of the virtual resource to be allocated.
In an embodiment of the present invention, the data acquisition module 601 may also be configured to: and determining a plurality of original features of the historical behavior data of the first user, and inputting the plurality of original features into a feature selector to obtain the features of the first user about the virtual resource.
In an embodiment of the invention, the predictive model is trained from a random forest algorithm, and any three of GBDT algorithm, catboost algorithm, lightGBM algorithm, XGBoost algorithm, and LR algorithm.
In an embodiment of the present invention, the prediction module 603 may also be configured to: three initial models are respectively constructed by utilizing any three of GBDT algorithm, catboost algorithm, lightGBM algorithm, XGBoost algorithm and LR algorithm; respectively inputting the historical behavior data of the second user into the three initial models to train the three initial models; and taking the output of the three initial models after training and the behavior preference of the second user about the virtual resources, which is output by the first classification model, as the input of a random forest algorithm so as to train the prediction model.
In the embodiment of the invention, the first classification model and the second classification model are trained based on a clustering algorithm.
In the embodiment of the invention, the feature selector is obtained based on a feature_selection library.
According to the virtual resource allocation device provided by the embodiment of the invention, after the historical behavior data of the first user is obtained, the historical behavior data containing the relevant characteristics of the virtual resource are firstly input into the first classification model to obtain the behavior preference of the first user about the virtual resource, then the behavior preference and the attribute of the virtual resource to be allocated are input into the prediction model to predict the probability that the first user uses the virtual resource to be allocated, and if the obtained probability is larger than the first threshold, the virtual resource to be allocated is allocated to the first user. As can be seen from the above description, the embodiment of the present invention can divide the first user into corresponding user types according to the historical behavior data of the first user, obtain the behavior preference of the first user according to the behavior preference of the corresponding user types, predict the probability that the first user uses the virtual resource to be allocated according to the behavior preference of the first user about the virtual resource and the attribute of the virtual resource to be allocated, and allocate the virtual resource to be allocated to the first user with high use probability, thereby improving the accuracy of virtual resource allocation and the use rate of the virtual resource.
Fig. 7 illustrates an exemplary system architecture 700 to which the virtual resource allocation method or virtual resource allocation apparatus of the embodiment of the present invention may be applied.
As shown in fig. 7, a system architecture 700 may include terminal devices 701, 702, 703, a network 704, and a server 705. The network 704 is the medium used to provide communication links between the terminal devices 701, 702, 703 and the server 705. The network 704 may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others.
A user may interact with the server 705 via the network 704 using the terminal devices 701, 702, 703 to receive or send messages or the like. Various communication client applications, such as shopping class applications, web browser applications, search class applications, instant messaging tools, mailbox clients, social platform software, etc., may be installed on the terminal devices 701, 702, 703.
The terminal devices 701, 702, 703 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smartphones, tablets, laptop and desktop computers, and the like.
The server 705 may be a server providing various services, such as a background management server providing support for shopping-type websites browsed by the user using the terminal devices 701, 702, 703. The background management server can analyze and other processing on the received data such as the product information inquiry request and the like, and feed back processing results (such as target push information and product information) to the terminal equipment.
It should be noted that, the method for allocating virtual resources provided in the embodiment of the present invention is generally executed by the server 705, and accordingly, the apparatus for allocating virtual resources is generally disposed in the server 705.
It should be understood that the number of terminal devices, networks and servers in fig. 7 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
Referring now to FIG. 8, there is illustrated a schematic diagram of a computer system 800 suitable for use in implementing an embodiment of the present invention. The terminal device shown in fig. 8 is only an example, and should not impose any limitation on the functions and the scope of use of the embodiment of the present invention.
As shown in fig. 8, the computer system 800 includes a Central Processing Unit (CPU) 801 that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 802 or a program loaded from a storage section 808 into a Random Access Memory (RAM) 803. In the RAM 803, various programs and data required for the operation of the system 800 are also stored. The CPU 801, ROM 802, and RAM 803 are connected to each other by a bus 804. An input/output (I/O) interface 805 is also connected to the bus 804.
The following components are connected to the I/O interface 805: an input portion 806 including a keyboard, mouse, etc.; an output portion 807 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and a speaker; a storage section 808 including a hard disk or the like; and a communication section 809 including a network interface card such as a LAN card, a modem, or the like. The communication section 809 performs communication processing via a network such as the internet. The drive 810 is also connected to the I/O interface 805 as needed. A removable medium 811 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 810 as needed so that a computer program read out therefrom is mounted into the storage section 808 as needed.
In particular, according to embodiments of the present disclosure, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method shown in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication section 809, and/or installed from the removable media 811. The above-described functions defined in the system of the present invention are performed when the computer program is executed by a Central Processing Unit (CPU) 801.
The computer readable medium shown in the present invention may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present invention, however, the computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with the computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The modules involved in the embodiments of the present invention may be implemented in software or in hardware. The described modules may also be provided in a processor, for example, as: a processor includes a data acquisition module, a classification module, a prediction module, and an allocation module. The names of these modules do not constitute a limitation on the module itself in some cases, and for example, the data acquisition module may also be described as "a module that acquires historical behavior data of the first user".
As another aspect, the present invention also provides a computer-readable medium that may be contained in the apparatus described in the above embodiments; or may be present alone without being fitted into the device. The computer readable medium carries one or more programs which, when executed by a device, cause the device to include: acquiring historical behavior data of a first user, wherein the historical behavior data comprises: the first user's characteristics with respect to the virtual resource; obtaining behavior preference of the first user about the virtual resource according to the historical behavior data by using the first classification model; the first classification model is trained according to historical behavior data of a plurality of second users; predicting the probability of using the virtual resource to be allocated by the first user by using a prediction model according to the behavior preference and the attribute of the virtual resource to be allocated, wherein the prediction model is obtained by training according to historical behavior data of using the virtual resource by a plurality of second users and the attribute of the virtual resource used by the second users; and when the probability is greater than a preset threshold, the virtual resource to be allocated is allocated to the first user.
According to the technical scheme of the embodiment of the invention, after the historical behavior data of the first user is obtained, the historical behavior data containing the relevant characteristics of the virtual resources are firstly input into the first classification model to obtain the behavior preference of the first user about the virtual resources, then the behavior preference and the attribute of the virtual resources to be allocated are input into the prediction model to predict the probability that the first user uses the virtual resources to be allocated, and if the obtained probability is larger than the first threshold, the virtual resources to be allocated are allocated to the first user. As can be seen from the above description, the embodiment of the present invention can divide the first user into corresponding user types according to the historical behavior data of the first user, obtain the behavior preference of the first user according to the behavior preference of the corresponding user types, predict the probability that the first user uses the virtual resource to be allocated according to the behavior preference of the first user about the virtual resource and the attribute of the virtual resource to be allocated, and allocate the virtual resource to be allocated to the first user with high use probability, thereby improving the accuracy of virtual resource allocation and the use rate of the virtual resource.
The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives can occur depending upon design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.
Claims (10)
1. A method for allocating virtual resources, comprising:
Acquiring historical behavior data of a first user and historical transaction data of a resource provider, wherein the historical behavior data comprises: the first user's characteristics about the virtual resource, the historical transaction data comprising: the corresponding resource provider provides the characteristics of the virtual resource;
obtaining behavior preference of the first user about the resource provider of the virtual resource according to the historical behavior data by using a first classification model; the first classification model is trained according to historical behavior data of a plurality of second users;
determining attribute preferences of the resource provider with respect to the virtual resource, respectively, based on the historical transaction data using a second classification model;
Predicting the probability of the first user using the virtual resource to be allocated provided by the resource provider by using a prediction model according to the attribute preference, the behavior preference and the attribute of the virtual resource to be allocated, wherein the prediction model is obtained by training according to historical behavior data of a plurality of second users using the virtual resource and the attribute of the virtual resource used by the second users;
and when the probability is larger than a preset threshold value, the virtual resources to be allocated are allocated to the first user.
2. The method of claim 1, wherein the attributes of the virtual resource to be allocated comprise: provider information of the virtual resource to be allocated;
Obtaining the behavior preference of the first user about the resource provider according to the historical behavior data by using a first classification model;
and predicting the probability of the first user using the virtual resource to be allocated provided by the resource provider by using a prediction model according to the behavior preference and the provider information of the virtual resource to be allocated.
3. The method of claim 2, wherein the attributes of the virtual resource to be allocated further comprise: the value of the virtual resource to be allocated;
and predicting the probability of the first user using the virtual resources to be allocated provided by the resource provider by using a prediction model according to the behavior preference of the first user about the resource provider and the value of the virtual resources to be allocated.
4. The method according to claim 1, characterized in that the method further comprises:
and determining a plurality of original features of the historical behavior data of the first user, and inputting the plurality of original features into a feature selector to obtain the features of the first user about the virtual resource.
5. The method of claim 2, wherein the predictive model is trained from a random forest algorithm and any three of GBDT algorithm, catboost algorithm, lightGBM algorithm, XGBoost algorithm, and LR algorithm.
6. The method of claim 5, wherein the step of determining the position of the probe is performed,
Three initial models are respectively constructed by utilizing any three of GBDT algorithm, catboost algorithm, lightGBM algorithm, XGBoost algorithm and LR algorithm;
respectively inputting the historical behavior data of the second user into the three initial models to train the three initial models;
And taking the output of the three initial models after training and the behavior preference of the second user about the virtual resource output by the first classification model as the input of the random forest algorithm so as to train the prediction model.
7. A method according to any one of claims 1 to 3, comprising:
The first classification model and the second classification model are obtained based on clustering algorithm training;
and/or the number of the groups of groups,
The feature selector is obtained based on a feature_selection library, and is used for processing original features of the historical behavior data of the first user.
8. A virtual resource allocation apparatus, comprising: the system comprises a data acquisition module, a classification module, a prediction module and an allocation module; wherein,
The data acquisition module is configured to acquire historical behavior data of a first user and historical transaction data of a resource provider, where the historical behavior data includes: the first user's characteristics about the virtual resource, the historical transaction data comprising: the corresponding resource provider provides the characteristics of the virtual resource;
The classification module is used for obtaining the behavior preference of the first user about the resource provider of the virtual resource according to the historical behavior data acquired by the data acquisition module by using a first classification model; the first classification model is trained according to historical behavior data of a plurality of second users; and determining attribute preferences of the resource provider with respect to the virtual resource, respectively, based on the historical transaction data using a second classification model;
The prediction module is configured to predict, according to the attribute preference and the attribute of the virtual resource to be allocated, a probability that the first user uses the virtual resource to be allocated provided by the resource provider by using a prediction model, where the prediction model is obtained by training according to historical behavior data of a plurality of second users using virtual resources and attributes of the virtual resources used by the second users;
And the allocation module is used for allocating the virtual resources to be allocated to the first user when the probability predicted by the prediction module is greater than a preset threshold value.
9. A virtual resource allocation server, comprising:
One or more processors;
storage means for storing one or more programs,
When executed by the one or more processors, causes the one or more processors to implement the method of any of claims 1-7.
10. A computer readable medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the method according to any of claims 1-7.
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