CN110992097A - Processing method and device for revenue product price, computer equipment and storage medium - Google Patents
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Abstract
The application relates to a processing method, a device, computer equipment and a storage medium for the prices of revenue and earn products, wherein the method comprises the steps of obtaining the historical data of users sharing articles; the user history data comprises a user identification; inputting the historical data of the user into a decision tree forest model for prediction to obtain a plurality of prices of revenue products and purchased probabilities corresponding to the prices; calculating expected values according to the prices and the purchased probabilities corresponding to the prices, and determining recommended prices of revenue products according to the calculated expected values; and storing the recommended price, the user identification and the corresponding relation between the recommended price and the user identification in a database. The method and the device realize the purpose of providing the recommended price of the revenue products for the user according to the requirement of the user on the shared goods, improve the conversion rate of purchasing the revenue products by the user, and solve the technical problem of low card purchasing conversion rate in the process of issuing the electronic ticket to the user.
Description
Technical Field
The present application relates to the field of computer technologies, and in particular, to a method and an apparatus for processing revenue product prices, a computer device, and a storage medium.
Background
With the development of internet technology, more and more shared articles such as a shared bicycle, a shared automobile, a shared power bank, a shared umbrella, and the like are appeared. The current revenue products for sharing objects mainly have various forms such as annual cards, half-year cards, quarter cards, month cards, secondary cards and the like. For users with relatively rigid requirements, the shared article needs to be frequently used, and the users are relatively economical and practical by purchasing products such as monthly cards, seasonal cards and the like; for a service party sharing goods, the stickiness of a user can be increased by selling products such as monthly cards and seasonal cards.
In general, in order to encourage users to purchase revenue products such as monthly cards and quarterly cards of shared goods, the service provider of shared goods often issues various electronic tickets to users on a large scale through the consumer applications operated by the service provider, including: cash vouchers, coupons, discount coupons, red packs, and the like.
However, in the conventional technology, the electronic ticket is issued to the user, and the card purchase conversion rate is low.
Disclosure of Invention
Therefore, it is necessary to provide a method, an apparatus, a computer device, and a storage medium for processing the prices of revenue products, in order to solve the technical problem of low card purchase conversion rate in the conventional technology of issuing electronic tickets to users.
A method of processing revenue product prices, the method comprising:
acquiring user historical data of shared articles; the user history data comprises a user identification;
inputting the historical user data into a decision tree forest model for prediction to obtain a plurality of prices of the revenue products and the purchased probability corresponding to each price;
calculating expected values according to the prices and the purchased probabilities corresponding to the prices, and determining the recommended prices of the revenue products according to the calculated expected values;
and storing the recommended price, the user identification and the corresponding relation between the recommended price and the user identification into a database.
In one embodiment, the calculating an expected value according to each price and the probability of being purchased corresponding to each price, and determining the recommended price of the revenue product according to the calculated expected value includes:
calculating expected values according to the prices and the purchase probabilities corresponding to the prices to obtain the expected values corresponding to the prices;
and selecting the price corresponding to the maximum expected value as the recommended price of the revenue product from the expected values corresponding to the prices.
In one embodiment, the method further comprises:
acquiring the configuration price of the revenue product and an operation instruction for entering a purchase page of the revenue product; the operation instruction carries the user identification;
searching a corresponding recommended price in the database according to the user identification;
calculating according to the searched recommended price and the configuration price to obtain a preferential amount corresponding to the bought revenue product;
and generating an electronic coupon corresponding to the bought revenue product according to the preferential amount, and sending the electronic coupon to a terminal corresponding to the user identifier.
In one embodiment, the step of generating the decision tree forest model includes:
constructing a user data sample set, wherein the user data sample set comprises a user data positive sample and a user data negative sample;
marking a first category value of purchasing behavior and a recommended price of the revenue product in each user data positive sample;
marking a second category value of purchasing behavior and the recommended price of the revenue product in each user data negative sample;
and training the decision tree forest model according to each user data positive sample, each user data negative sample, a first category value and a recommended price corresponding to each user data positive sample, and a second category value and a recommended price corresponding to each user data negative sample.
In one embodiment, the constructing the user data negative example includes:
collecting user data samples corresponding to the second category values; the user data sample comprises a time of entry into the purchase page;
screening the user data sample corresponding to the second category value according to the user identification and the time of entering the purchase page and a preset rule;
and constructing a user data negative sample by using the user data sample corresponding to the second class value obtained by screening.
In one embodiment, the shared item is a shared bicycle; the user data samples further comprise at least one of user riding data, data of the revenue products purchased by the user, user attribute data, user registered cities, data influencing user riding willingness, and user behavior data occurring in the purchase page.
In one embodiment, the decision tree forest model is a LightGBM model, and the LightGBM model runs in a distributed computing environment.
A processing apparatus for revenue product prices, the apparatus comprising:
the acquisition module is used for acquiring user historical data of the shared articles; the user history data comprises a user identification;
the prediction module is used for inputting the historical user data into a decision tree forest model for prediction to obtain a plurality of prices of the revenue products and the purchase probability corresponding to each price;
the calculation module is used for calculating expected values according to the prices and the purchased probabilities corresponding to the prices and determining the recommended prices of the revenue products according to the calculated expected values;
and the storage module is used for storing the recommended price, the user identification and the corresponding relation between the recommended price and the user identification into a database.
A computer-readable storage medium having stored thereon a computer program which, when executed by a processor, causes the processor to carry out the steps of the method of any of the embodiments described above.
A computer device comprising a memory and a processor, the memory having stored thereon a computer program that, when executed by the processor, causes the processor to perform the steps of the method of any of the above embodiments.
The processing method, the device, the computer equipment and the storage medium for the prices of the revenue and earn products acquire the user historical data of the shared goods; the user history data comprises a user identification; inputting the historical data of the user into a decision tree forest model for prediction to obtain a plurality of prices of revenue products and purchased probabilities corresponding to the prices; calculating expected values according to the prices and the purchased probabilities corresponding to the prices, and determining recommended prices of revenue products according to the calculated expected values; and storing the recommended price, the user identification and the corresponding relation between the recommended price and the user identification in a database. The method and the device realize the purpose of providing the recommended price of the revenue products for the user according to the requirement of the user on the shared goods, improve the conversion rate of purchasing the revenue products by the user, and solve the technical problem of low card purchasing conversion rate in the process of issuing the electronic ticket to the user.
Drawings
FIG. 1 is a diagram of an exemplary environment for implementing the method of processing the price of a revenue product;
FIG. 2 is a flow diagram illustrating a method for processing revenue product prices according to one embodiment;
FIG. 3 is a flow diagram illustrating a method for processing the prices of revenue products in one embodiment;
FIG. 4 is a schematic flow diagram of generating a decision tree forest model in one embodiment;
FIG. 5 is a schematic flow diagram illustrating construction of negative examples of user data in one embodiment;
FIG. 6 is a flow diagram illustrating a method for processing revenue product prices in one embodiment;
FIG. 7 is a block diagram of a processing device for revenue product pricing in one embodiment;
FIG. 8 is a diagram illustrating an internal structure of a computer device according to an embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
As described in the background art, in order to encourage users to purchase revenue products such as monthly cards and quarterly cards of shared goods, the service provider of shared goods often issues various types of electronic tickets to users on a large scale through consumer applications operated by the service provider. These revenue products are pre-paid and the user may use the shared goods any number of times within the agreed time frame after payment. However, the degree of the demand of different users for the shared goods is different, that is, the number of times of using the shared goods is different, and if each user pays the same fee for the revenue product, it is obviously unfair for the users, and in general, since the electronic ticket does not have the real demand for each user, some users choose to ignore various types of electronic tickets issued by the service party of the shared goods. For the service party sharing the goods, the technical problem that the conversion rate of buying the revenue product is low when the electronic ticket is issued to the user exists.
Based on this, the embodiment of the present application provides a processing method for calculating the prices of revenue products in a targeted manner according to the actual demands of users on shared goods, and can be applied to the application environment shown in fig. 1. Wherein, the terminal 110 performs data interaction with the server 120 through the network. The server 120 obtains user history data of the shared item; the user history data comprises a user identification; inputting the historical data of the user into a decision tree forest model for prediction to obtain a plurality of prices of revenue products and purchased probabilities corresponding to the prices; calculating expected values according to the prices and the purchased probabilities corresponding to the prices, and determining recommended prices of revenue products according to the calculated expected values; and storing the recommended price, the user identification and the corresponding relation between the recommended price and the user identification in a database.
The terminal 110 may be configured to display an application page of the application program, where the application page is provided with a button corresponding to the revenue product purchase, and the user triggers the corresponding button to send an operation instruction to enter the purchase page of the revenue product. The server 120 obtains the configured price of the revenue product and an operation instruction entering a purchase page of the revenue product, wherein the operation instruction carries a user identifier, and a corresponding recommended price is searched in a database according to the user identifier; calculating according to the searched recommended price and the configuration price to obtain a preferential amount corresponding to the bought revenue product; and generating an electronic coupon corresponding to the bought revenue product according to the preferential amount, and sending the electronic coupon to the terminal 110 corresponding to the user identifier.
The terminal 110 may be, but not limited to, various personal computers, notebook computers, smart phones, tablet computers, and portable wearable devices, and the server 120 may be implemented by an independent server or a server cluster formed by a plurality of servers.
In one embodiment, as shown in fig. 2, the present application provides a processing method for revenue product price, which is described by taking the server in the application environment of fig. 1 as an example, and includes the following steps:
s210, obtaining user historical data of the shared articles.
The server side of the shared goods provides a consumer application program for the user, the user obtains the authority of using the shared goods through the application program, and the application program can record behavior data (such as using times, using duration and the like) of the user using the shared goods and record behavior data (such as clicking operation behavior, access time and the like) of the user using the application program. The user history data is data on various aspects such as information on the use of the shared article by the user, a revenue product for purchasing the shared article, and user attribute information recorded by a consumer application operated by a service provider of the shared article. The user history data includes a user identification. The user history data may be stored locally at the server or may be stored at a server communicatively coupled to the server.
Specifically, in order to promote the user to purchase revenue products, price strategies of different revenue products are formulated according to different user requirements, and the benefits of both the user and the service party are maintained. In order to estimate the use demand of the shared article by the user, the user history data needs to be analyzed, so that the server acquires the user history data of the shared article from the server locally or acquires the user history data of the shared article from the server in communication connection with the server.
S220, inputting the historical data of the user into the decision tree forest model for prediction to obtain a plurality of prices of the revenue products and the purchased probability corresponding to each price.
The decision tree forest model integrates a plurality of tree decisions through an Ensemble Learning idea, a basic unit of the decision tree forest model is a decision tree, and the essence of the decision tree forest model belongs to an Ensemble Learning (Ensemble Learning) method of machine Learning. The decision tree forest model is a model with prediction capability obtained by training in advance. The user historical data can reflect the real demand of the user on the shared goods, because the user historical data is stored in the server locally or is communicated with the server, the user historical data can be used for predicting the price acceptable by the user, and the reasonable and feasible price can be provided for the user according to the real demand of the user. The probability of being purchased is the probability that the decision tree forest model predicts that the user purchases a revenue product. Specifically, the decision tree forest model is deployed in a server, the server obtains user historical data, inputs the user historical data into the decision tree forest model for feature division, and predicts a plurality of prices of corresponding revenue products and the probability of purchasing the revenue products at the prices of the users.
And S230, calculating expected values according to the prices and the purchased probabilities corresponding to the prices, and determining the recommended prices of the revenue products according to the calculated expected values.
Wherein the expected value is a product of the prediction result and the prediction probability. Specifically, a decision tree forest model deployed on a server can be triggered at a preset time, a plurality of prices at which a user may purchase revenue products and the purchase probabilities corresponding to the prices are predicted by the decision tree forest model according to user historical data of the user, calculation is performed by using the predicted prices and the purchase probabilities corresponding to the prices to obtain expected values corresponding to the prices, and then the recommended prices of the revenue products are determined according to the calculated expected values to maximize benefits of the user and the server.
S240, storing the recommended price, the user identification and the corresponding relation between the recommended price and the user identification into a database.
Specifically, the recommended prices of revenue products may be calculated in advance according to the demands of the users, for example, the server automatically triggers the operation of the decision tree forest model at a preset time point, inputs the historical data of the users into the decision data model, and predicts the prices of the revenue products and the probability of being purchased corresponding to each price. And the server calculates according to the prices and the probability of being purchased corresponding to each price to determine the recommended price of the revenue product. And storing the recommended price, the user identification and the corresponding relation between the recommended price and the user identification in a database. Therefore, when the user purchases the revenue product, the terminal sends the operation instruction including the user identification to the server, and the server searches the database according to the user identification to quickly obtain the recommended price corresponding to the revenue product purchased by the user. It can be understood that when the user purchases the revenue product, the operation of the decision tree forest model is triggered by the operation instruction sent by the terminal to the server to predict the recommended price of the revenue product.
In the embodiment, the historical data of the user sharing the article is obtained; the user history data comprises a user identification; inputting the historical data of the user into a decision tree forest model for prediction to obtain a plurality of prices of revenue products and purchased probabilities corresponding to the prices; calculating expected values according to the prices and the purchased probabilities corresponding to the prices, and determining recommended prices of revenue products according to the calculated expected values; and storing the recommended price, the user identification and the corresponding relation between the recommended price and the user identification in a database. The method and the device realize the purpose of providing the recommended price of the revenue products for the user according to the requirement of the user on the shared goods, improve the conversion rate of purchasing the revenue products by the user, and solve the technical problem of low card purchasing conversion rate in the process of issuing the electronic ticket to the user.
In one embodiment, the calculating of the expected value according to each price and the probability of being purchased corresponding to each price, and the determining of the recommended price of the revenue product according to the calculated expected value includes: calculating expected values according to the prices and the purchased probabilities corresponding to the prices to obtain the expected values corresponding to the prices; and selecting the price corresponding to the maximum expected value as the recommended price of the revenue product from the expected values corresponding to the prices.
Specifically, the server acquires user historical data of shared articles, inputs the acquired user historical data into a decision tree forest model for prediction, and obtains a plurality of prices of revenue products and purchase probabilities corresponding to the prices. And calculating the product of the prices and the purchase probability corresponding to each price to obtain the expected value corresponding to each price. In order to maximize the benefits of both the user and the service provider, the price corresponding to the maximum expected value among the expected values corresponding to the prices is selected as the recommended price of the revenue product.
Illustratively, the shared goods are shared bicycles, and the revenue product is a bicycle monthly card provided by the service provider. Inputting the historical data of the user A into a decision tree forest model for prediction to obtain prices of the bicycle monthly cards which can be 40 yuan, 50 yuan and 60 yuan, wherein the purchase probabilities corresponding to 40 yuan, 50 yuan and 60 yuan are 60%, 50% and 30%, respectively; the expected values of the prices of the bicycle monthly cards and the probability of being purchased corresponding to the prices are calculated to be 24, 25, and 18, respectively. Therefore, the recommended price of the bicycle monthly card should be 50 dollars.
In one embodiment, as shown in fig. 3, the method further comprises the steps of:
s310, acquiring a configuration price of the revenue product and an operation instruction for entering a purchase page of the revenue product;
s320, searching a corresponding recommended price in a database according to the user identification;
s330, calculating according to the searched recommended price and the configuration price to obtain a preferential amount corresponding to the bought revenue product;
s340, generating an electronic coupon corresponding to the bought revenue product according to the preferential amount, and sending the electronic coupon to the terminal corresponding to the user identification.
The configured price refers to the same unit price established by the service party for the revenue product, and the configured price can be related to factors such as cities, for example, one city corresponds to one configured price. The purchase webpage is a page in an application program provided by the service party for showing information of the purchased revenue product to the user, and may include configuration value of the revenue product, product introduction and the like. Specifically, the server local or the server in communication connection with the server stores the configuration price of the revenue product in advance, and the configuration price of the revenue product is acquired from the server local or the server in communication connection with the server. The terminal is provided with an application program, other pages of the application program such as a main page are provided with corresponding buttons, a user triggers the buttons, and the terminal displays a purchase page of the revenue product. Namely, the server obtains an operation instruction entering a purchase page of the revenue product, and the operation instruction carries the user identifier. Since the recommended price of the revenue product is predicted in advance according to the historical data of the user, and the corresponding relation between the recommended price and the user identifier is stored in the database, the server can search the corresponding recommended price in the database according to the user identifier. In order to determine the preferential amount provided for the user, the server calculates according to the searched recommended price and the configured price, and after the preferential amount corresponding to the bought revenue product is obtained through calculation, the server generates an electronic coupon corresponding to the bought revenue product according to the preferential amount and sends the electronic coupon to the terminal corresponding to the user identifier. For example, the server calculates the difference value between the recommended price and the configured price, generates a coupon with the amount equal to the difference value, and sends the coupon to the corresponding terminal; or the server calculates the ratio of the recommended price to the configured price, generates a discount coupon with the amount equal to the ratio, and sends the discount coupon to the corresponding terminal. It is understood that the database in this embodiment may be deployed locally to the server or may be deployed to a server connected to the server.
In one embodiment, as shown in fig. 4, the step of generating the decision tree forest model includes:
s410, constructing a user data sample set, wherein the user data sample set comprises a user data positive sample and a user data negative sample.
The user data sample set comprises a plurality of user data samples, the user data samples can reflect user images, the user data samples comprise behavior data of a user entering a purchase page, and the user data samples further comprise data of other aspects, such as: shared goods data used by the user, data of the user purchasing revenue products, user attribute data, user registered city, etc. The user enters the purchase page to send out purchasing behavior, including purchasing revenue products and not purchasing revenue products. And the user data entering the purchase page to purchase the revenue product is used as a user data positive sample, and the user data entering the purchase page without purchasing the revenue product is used as a user data negative sample.
Specifically, the terminal is provided with an application program, a server in communication connection with the terminal not only collects data of shared articles used by the user, data of user behaviors and data of products bought by the user, but also collects information of user attribute data, city registered by the user and the like, and the server stores the collected information to construct a user data sample. And the purchasing behavior of the user comprises purchasing revenue products and not purchasing revenue products, the user data sample set comprises a user data positive sample and a user data negative sample.
And S420, marking the first category value of the purchasing behavior and the recommended price of the revenue product in each user data positive sample.
The user data positive sample comprises recommended prices of revenue products, and the recommended prices are that when a user enters a purchase page, the purchase page displays the prices of the revenue products to the user. Specifically, the user data entering the purchase page for purchasing revenue products is used as a user data positive sample, the purchasing behavior for purchasing revenue products is provided with a corresponding first class value, and the user data positive sample is labeled by using the first class value. But also the recommended price of the revenue product. Namely, the labels corresponding to the user data positive sample are the first category value of the purchasing behavior and the recommended price of the revenue product respectively.
And S430, marking the second category value of the purchasing behavior and the recommended price of the revenue product in each user data negative sample.
The user data negative sample comprises recommended prices of revenue products, and the recommended prices are that when the user enters a purchase page, the purchase page displays the prices of the revenue products to the user. Specifically, the user data entering the purchase page without purchasing revenue products is used as a user data negative sample, the purchasing behavior without purchasing revenue products is provided with a corresponding second category value, and the user data positive sample is labeled by using the second category value. But also the recommended price of the revenue product. Namely, the labels corresponding to the user data negative examples are the second category value of the purchasing behavior and the recommended price of the revenue product respectively.
S440, training a decision tree forest model according to the user data positive samples, the user data negative samples, the first category values and the recommended prices corresponding to the user data positive samples, and the second category values and the recommended prices corresponding to the user data negative samples.
Specifically, the structure of the decision tree forest model can be set through a server or other computer equipment, the user data sample set comprises a user data positive sample and a user data negative sample, labels corresponding to the user data positive sample are a first category value of a purchasing behavior and a recommended price of a revenue product respectively, and labels corresponding to the user data negative sample are a second category value of the purchasing behavior and the recommended price of the revenue product respectively. The server trains the decision tree forest model by using each user data positive sample, each user data negative sample, the first category value and the recommended price corresponding to each user data positive sample, and the second category value and the recommended price corresponding to each user data negative sample. And adjusting model parameters of the decision tree forest model according to the difference between the result output by the decision tree forest model and the label value, and continuing training until the training stopping condition is met.
For example, the model may be optimized by setting parameters such as the depth of the decision tree, the minimum number of samples per leaf node, the number of iterations, the learning step size, and the like. For example, the depth of the decision tree is set to be 10 layers, the minimum number of samples of each leaf node can be hundreds of thousands to 2000 thousands, the iteration number is 500, and the learning step size is 0.1.
In the embodiment, a user data sample set is constructed, and the user data sample set comprises a user data positive sample and a user data negative sample; marking a first category value of purchasing behavior and a recommended price of a revenue product in each user data positive sample; marking a second category value of the purchasing behavior and the recommended price of the revenue product in each user data negative sample; therefore, the decision tree forest model is trained by utilizing the user data positive samples, the user data negative samples, the first class values and the recommended prices corresponding to the user data positive samples and the second class values and the recommended prices corresponding to the user data negative samples, the performance of the decision tree forest model is improved, and the accuracy of a prediction result is ensured.
In one embodiment, as shown in FIG. 5, constructing a negative sample of user data includes the steps of:
s510, collecting a user data sample corresponding to the second category value; the user data sample includes a time of entry into the purchase page;
s520, screening the user data sample corresponding to the second category value according to the user identification and the time of entering the purchase page and a preset rule;
and S530, constructing a user data negative sample by using the user data sample corresponding to the second class value obtained by screening.
In this case, since the behavior of the user entering the purchase page may include much noise, not all behaviors of the user entering the purchase page without purchasing revenue products may be used to train the decision tree forest model. In particular, repeated operational behavior is frequently issued by the user over a period of time. For example, the user a may enter the purchase page many times within a few days before and after purchasing revenue products, and none of the revenue products is purchased, and the user data entered into the purchase page cannot be used as a negative sample. For another example, user B may enter the purchase page for several days in succession but does not ultimately purchase revenue products, and the user data entered into the purchase page may not be used as a negative sample in its entirety, but only once. For another example, the user C may enter the purchase page many times within a few days before purchasing revenue products, but does not purchase revenue products, and finally the user purchases revenue products at the same recommended price, which does not indicate that the user C cannot receive the recommended price, and therefore, the user data entered into the purchase page cannot be used as a negative sample. If these noisy data, which are frequently entered into the purchase pages, are used as negative examples, this not only does not comply with objective rules, but also affects the training of the model. Therefore, the user data samples corresponding to the second category value need to be filtered.
Specifically, the server or other computer device collects a user data sample corresponding to the second category value, that is, a user data sample corresponding to a situation that the user enters a purchase page and does not purchase revenue products. And the server would collect the time the user entered the purchase page, then each user data sample includes the time entered the purchase page. In order to reduce the influence of frequently-operated user data on the user data with low operation frequency, the server screens the user data sample corresponding to the second category value according to the user identification and the time of entering a purchase page and a preset rule so as to filter the frequently-operated user data or select one from the frequently-operated user data for use. And the server constructs a user data negative sample by using the user data sample corresponding to the second class value obtained by screening.
In the embodiment, the user data samples corresponding to the products which are not bought by the user entering the purchase page and do not buy the revenue are screened, so that the influence of noise is reduced, and the accuracy of the samples used by the training model is improved, so that the performance of the decision tree forest model is improved, and the accuracy of the prediction result is ensured.
In one embodiment, the shared item is a shared bicycle. The revenue product may be a bicycle monthly card, and the probability of being purchased of the revenue product may be a card purchase rate. The user data samples further include at least one of user cycling data, data of the user purchasing revenue products, user attribute data, user registered cities, data affecting the user's cycling willingness, user behavior data occurring within a purchase page.
Specifically, the prediction of card purchase rate and recommended price for a single month card requires the use of many user data to characterize the user. Such as the following:
1) and the user riding data comprises the total riding times of the user in history, the latest riding times, the riding times in the valid period after the user purchases the card in history, the average riding time of the user and the like.
2) The user purchases the card history, such as the card purchasing times, price distribution, etc. in the user history.
3) And the user supplements the consumption history, such as the user supplements the money amount and the like.
4) User stationing points. Such as point of interest (poi) information where the user frequently rides.
5) User basic attributes such as gender, age, etc.
6) The user registers a city, a city that is frequently ridden.
7) The purchase page presents the recommended price to the user.
8) Weather, holidays and the like influence the riding desire of the user.
In one embodiment, the decision tree forest model is a LightGBM model, and the LightGBM model operates in a distributed computing environment.
For example, taking a sharing bicycle as an example, firstly, an algorithm problem needs to be abstracted according to a concrete business scene. The business scene requires to calculate the card purchasing probability of the user under the condition of different bicycle month card prices, so a two-classification problem can be constructed, namely whether the user can purchase the bicycle month card under the given price is estimated. The training time labels may be taken as 0 and 1(0 indicating the purchase of a bicycle monthly card and 1 indicating no purchase of a bicycle monthly card), while the model outputs the probability that the user purchased a bicycle monthly card. The light gbm (light Gradient Boosting machine) model, which is commonly used in the industry at present, can be selected. The LightGBM model is a gradient enhancement framework using a decision tree based learning algorithm. The LightGBM model is a boosting integrated decision tree forest model, and is optimized on the basis of an XGboost (extreme Gradient boosting) model, so that the training and learning efficiency can be greatly improved on the premise of keeping the predictive performance of the XGboost.
Further, since the data volume of the user data is large, the stand-alone model cannot predict the mass data quickly, so a distributed model, such as a Machine Learning library for Apache Spark, is required to be selected for Learning and prediction.
In one embodiment, the present application provides a method for processing the prices of revenue products, which is described by taking as an example the case where a user enters a purchase page to trigger the operation of a forest model of a decision tree and determine the recommended prices of revenue products, as shown in fig. 6, the method includes the following steps:
s610, acquiring a configuration price of the revenue product and an operation instruction for entering a purchase page of the revenue product; the operation instruction carries a user identifier.
Specifically, the server local data stores the configuration price of the revenue product, and the server obtains the configuration price of the revenue product from the database. The terminal displays an application program interface, and in response to an operation request sent by a user in the application program interface, the server acquires an operation instruction entering a purchase page of a revenue product, wherein the operation instruction carries a user identifier.
S620, obtaining user history data of the shared article according to the user identification.
Specifically, the server collects and stores user history data locally at the server or is in communication connection with the server. And when the server receives an operation instruction entering a purchase page of the revenue product, searching and acquiring the user historical data of the shared object from the corresponding database according to the user identification carried by the operation instruction.
S630, inputting the historical data of the user into the decision tree forest model for prediction to obtain a plurality of prices of the revenue products and the purchased probability corresponding to each price.
Specifically, the decision tree forest model is deployed in a server, when the server receives an operation instruction of entering a purchase page of the revenue product, the operation of the decision tree forest model is automatically triggered, the server inputs the acquired user historical data into the decision tree forest model for feature division, and a plurality of prices of the corresponding revenue product and the probability of purchasing the revenue product at the prices of the user are predicted.
S640, calculating expected values according to the prices and the purchased probabilities corresponding to the prices to obtain the expected values corresponding to the prices;
and S650, selecting the price corresponding to the maximum expected value as the recommended price of the revenue product from the expected values corresponding to the prices.
Specifically, after obtaining a plurality of prices of the revenue products and the probability of being purchased corresponding to each price through the decision tree forest model, the server calculates the product of the plurality of prices and the probability of being purchased corresponding to each price to obtain the expected value corresponding to each price. In order to maximize the benefits of both the user and the service provider, the price corresponding to the maximum expected value among the expected values corresponding to the prices is selected as the recommended price of the revenue product.
S660, calculating according to the recommended price and the configured price of the revenue product to obtain a preferential amount corresponding to the purchased revenue product; and S670, generating an electronic coupon corresponding to the bought revenue product according to the preferential amount, and sending the electronic coupon to a terminal corresponding to the user identifier.
Specifically, the server local or the server in communication connection with the server stores the configuration price of the revenue product in advance, and the configuration price of the revenue product is acquired from the server local or the server in communication connection with the server. Since the recommended price of the revenue product is predicted in advance according to the historical data of the user, the server calculates according to the searched configured price and the predicted recommended price, and after the preferential amount corresponding to the purchased revenue product is obtained through calculation, the server generates an electronic coupon corresponding to the purchased revenue product according to the preferential amount and sends the electronic coupon to the terminal corresponding to the user identifier.
In one embodiment, the application provides a processing method for the prices of revenue products, and the purchasing behaviors of the revenue products comprise purchasing and non-purchasing, which respectively correspond to a first category value and a second category value. The method comprises the following steps:
and S702, constructing a user data positive sample.
And S704, marking the first category value of the purchasing behavior and the recommended price of the revenue product in each user data positive sample.
And S706, collecting the user data sample corresponding to the second category value.
Wherein the user data sample includes a time of entry into the purchase page.
And S708, screening the user data sample corresponding to the second category value according to the user identification and the time of entering the purchase page and a preset rule.
And S710, constructing a user data negative sample by using the user data sample corresponding to the second class value obtained by screening.
S712, marking the second category value of the purchasing behavior and the recommended price of the revenue product in each user data negative sample;
s714, training a decision tree forest model according to the user data positive samples, the user data negative samples, the first category values and the recommended prices corresponding to the user data positive samples, and the second category values and the recommended prices corresponding to the user data negative samples.
S716, obtaining user historical data of the shared goods;
wherein the user history data comprises a user identification.
S718, inputting the historical data of the user into the decision tree forest model for prediction to obtain a plurality of prices of the revenue products and the purchased probability corresponding to each price.
S720, calculating expected values according to the prices and the purchase probabilities corresponding to the prices to obtain the expected values corresponding to the prices.
S722, the price corresponding to the maximum expected value is selected as the recommended price of the revenue product from the expected values corresponding to the prices.
S724, storing the recommended price, the user identification and the corresponding relation between the recommended price and the user identification into a database.
And S726, acquiring the configuration price of the revenue product and an operation instruction for entering a purchase page of the revenue product.
Wherein the operation instruction carries a user identifier;
and S728, searching the corresponding recommended price in the database according to the user identification.
And S730, calculating according to the searched recommended price and the configuration price to obtain the preferential amount corresponding to the bought revenue product.
S732, generating an electronic coupon corresponding to the bought revenue product according to the preferential amount, and sending the electronic coupon to a terminal corresponding to the user identifier.
It should be understood that, although the steps in the above-described flowcharts are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least a portion of the steps in the above-described flowcharts may include multiple sub-steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of performing the sub-steps or the stages is not necessarily sequential, but may be performed alternately or alternatingly with other steps or at least a portion of the sub-steps or stages of other steps.
In one embodiment, as shown in FIG. 7, a processing device for revenue product prices is provided. The processing device 700 includes an acquisition module 710, a prediction module 720, a calculation module 730, and a preservation module 740.
Wherein:
an obtaining module 710, configured to obtain user history data of a shared article; the user history data comprises a user identification;
the prediction module 720 is used for inputting the historical data of the user into the decision tree forest model for prediction to obtain a plurality of prices of the revenue products and the purchased probabilities corresponding to the prices;
the calculation module 730 is used for calculating expected values according to the prices and the purchased probabilities corresponding to the prices, and determining the recommended prices of revenue products according to the calculated expected values;
the storing module 740 is configured to store the recommended price, the user identifier, and the corresponding relationship between the recommended price and the user identifier in the database.
The specific definition of the processing device for the revenue product price can be referred to the definition of the processing method for the revenue product price in the above, and is not described herein again. The modules in the processing device for collecting the product price can be wholly or partially realized by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as shown in fig. 8. The computer device includes a processor, a memory, a network interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a method of processing revenue product prices. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, a key, a track ball or a touch pad arranged on the shell of the computer equipment, an external keyboard, a touch pad or a mouse and the like.
Those skilled in the art will appreciate that the architecture shown in fig. 8 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer device is provided, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the method steps of the above embodiments when executing the computer program.
In an embodiment, a computer-readable storage medium is provided, on which a computer program is stored, which computer program, when being executed by a processor, carries out the method steps of the above-mentioned embodiments.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
The technical features of the embodiments described above may be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the embodiments described above are not described, but should be considered as being within the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. A method for processing the prices of revenue products, the method comprising:
acquiring user historical data of shared articles; the user history data comprises a user identification;
inputting the historical user data into a decision tree forest model for prediction to obtain a plurality of prices of the revenue products and the purchased probability corresponding to each price;
calculating expected values according to the prices and the purchased probabilities corresponding to the prices, and determining the recommended prices of the revenue products according to the calculated expected values;
and storing the recommended price, the user identification and the corresponding relation between the recommended price and the user identification into a database.
2. The method of claim 1, wherein the calculating an expectation value according to the prices and the probability of being purchased corresponding to the prices and determining the recommended prices of the revenue products according to the calculated expectation value comprises:
calculating expected values according to the prices and the purchase probabilities corresponding to the prices to obtain the expected values corresponding to the prices;
and selecting the price corresponding to the maximum expected value as the recommended price of the revenue product from the expected values corresponding to the prices.
3. The method of claim 1, further comprising:
acquiring the configuration price of the revenue product and an operation instruction for entering a purchase page of the revenue product; the operation instruction carries the user identification;
searching a corresponding recommended price in the database according to the user identification;
calculating according to the searched recommended price and the configuration price to obtain a preferential amount corresponding to the bought revenue product;
and generating an electronic coupon corresponding to the bought revenue product according to the preferential amount, and sending the electronic coupon to a terminal corresponding to the user identifier.
4. The method of claim 1, wherein the step of generating the decision tree forest model comprises:
constructing a user data sample set, wherein the user data sample set comprises a user data positive sample and a user data negative sample;
marking a first category value of purchasing behavior and a recommended price of the revenue product in each user data positive sample;
marking a second category value of purchasing behavior and the recommended price of the revenue product in each user data negative sample;
and training the decision tree forest model according to each user data positive sample, each user data negative sample, a first category value and a recommended price corresponding to each user data positive sample, and a second category value and a recommended price corresponding to each user data negative sample.
5. The method of claim 4, wherein the constructing the negative examples of user data comprises:
collecting user data samples corresponding to the second category values; the user data sample comprises a time of entry into the purchase page;
screening the user data sample corresponding to the second category value according to the user identification and the time of entering the purchase page and a preset rule;
and constructing a user data negative sample by using the user data sample corresponding to the second class value obtained by screening.
6. The method of claim 5, wherein the shared item is a shared bicycle; the user data samples further comprise at least one of user riding data, data of the revenue products purchased by the user, user attribute data, user registered cities, data influencing user riding willingness, and user behavior data occurring in the purchase page.
7. The method according to any of claims 1 to 6, wherein the decision tree forest model is a LightGBM model, and wherein the LightGBM model operates in a distributed computing environment.
8. An apparatus for processing prices of revenue products, the apparatus comprising:
the acquisition module is used for acquiring user historical data of the shared articles; the user history data comprises a user identification;
the prediction module is used for inputting the historical user data into a decision tree forest model for prediction to obtain a plurality of prices of the revenue products and the purchase probability corresponding to each price;
the calculation module is used for calculating expected values according to the prices and the purchased probabilities corresponding to the prices and determining the recommended prices of the revenue products according to the calculated expected values;
and the storage module is used for storing the recommended price, the user identification and the corresponding relation between the recommended price and the user identification into a database.
9. A computer device comprising a memory and a processor, the memory storing a computer program, wherein the processor implements the steps of the method of any one of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 7.
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