CN111091416A - Method and device for predicting probability of hotel purchase robot - Google Patents
Method and device for predicting probability of hotel purchase robot Download PDFInfo
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Abstract
The embodiment of the application provides a method and a device for predicting probability of a hotel purchasing robot, wherein the method comprises the following steps: obtaining hotel parameters to be predicted; the method comprises the steps of inputting hotel parameters to be predicted into a pre-trained purchase prediction model to obtain a prediction result, wherein the prediction result comprises the probability that a target hotel corresponding to the hotel parameters to be predicted purchases a robot, the pre-trained purchase prediction model is obtained according to sample data, and the sample data comprises a plurality of hotel parameters to be trained and purchase identifications corresponding to the hotel parameters to be trained in the hotel parameters to be trained. According to the technical scheme, the accuracy of predicting the hotel purchase intention can be improved.
Description
Technical Field
The application relates to the technical field of computers, in particular to a method and a device for predicting probability of a hotel purchasing robot.
Background
At present, before recommending robots to a plurality of hotels, robot sales staff often evaluate the purchase willingness of each hotel in advance according to own experience, so that marketing efficiency is improved.
In the process of implementing the invention, the inventor finds that the following problems exist in the prior art: since the sales experience of different salespeople is different, the evaluation results of different salespeople may also be different, and therefore, the prior art has at least the problem of inaccurate evaluation of the buying desire of the hotel.
Disclosure of Invention
The embodiment of the application aims to provide a method and a device for predicting probability of a hotel purchasing robot, so as to solve the problem that in the prior art, the hotel purchasing intention is not accurately evaluated.
In a first aspect, an embodiment of the present application provides a method for predicting a probability of a hotel purchasing a robot, where the method includes: obtaining hotel parameters to be predicted; the method comprises the steps of inputting hotel parameters to be predicted into a pre-trained purchase prediction model to obtain a prediction result, wherein the prediction result comprises the probability that a target hotel corresponding to the hotel parameters to be predicted purchases a robot, the pre-trained purchase prediction model is obtained according to sample data, and the sample data comprises a plurality of hotel parameters to be trained and purchase identifications corresponding to the hotel parameters to be trained in the hotel parameters to be trained.
Therefore, according to the embodiment of the application, the prediction result containing the probability of the target hotel purchasing robot corresponding to the hotel parameter to be predicted is obtained by obtaining the hotel parameter to be predicted and inputting the hotel parameter to be predicted into the pre-trained purchasing prediction model, and therefore, the accuracy of predicting the hotel purchasing intention can be improved through the technical scheme.
In one possible embodiment, before obtaining hotel parameters to be predicted, the method further comprises: acquiring sample data; and training the sample data by using a preset decision tree algorithm to obtain a pre-trained purchase prediction model.
Therefore, the prediction result can be directly obtained subsequently by training and purchasing the prediction model in advance, and the model does not need to be established before the prediction result is determined.
In one possible embodiment, the method further comprises: comparing the preset probability with the probability of the target hotel purchasing robot; and under the condition that the probability of the target hotel for purchasing the robot is greater than or equal to the preset probability, dividing the target hotel into an optimal hotel set.
Therefore, the preferred customer is accurately determined through the pre-trained purchase prediction model, and for a salesperson of the robot, the robot recommendation is identified to the preferred customer, so that the success rate of the robot recommendation can be improved.
In one possible embodiment, the method further comprises: determining a hotel type of a target hotel; and matching the robot recommendation information corresponding to the hotel type of the target hotel from the database according to the hotel type of the target hotel, wherein the database stores mapping relations between different hotel types and different robot recommendation information.
Therefore, the embodiment of the application realizes the accurate recommendation of the robot through a database matching mode, and the salesperson does not need to guess the hotel requirements according to own experience.
In one possible embodiment, the hotel parameters include at least one of the following parameters: hotel brand, hotel city, hotel location, hotel star level, number of hotel rooms, number of hotel employees, hotel occupancy, and hotel average house cost.
Therefore, the prediction performance of the purchase prediction model is improved through various hotel parameters.
In a second aspect, an embodiment of the present application provides an apparatus for predicting a probability of a hotel purchasing a robot, the apparatus including: the system comprises an acquisition module, a prediction module and a prediction module, wherein the acquisition module is used for acquiring hotel parameters to be predicted; the input module is used for inputting the hotel parameters to be predicted into a pre-trained purchase prediction model to obtain a prediction result, wherein the prediction result comprises the probability of the target hotel purchase robot corresponding to the hotel parameters to be predicted, the pre-trained purchase prediction model is obtained according to sample data, and the sample data comprises a plurality of hotel parameters to be trained and purchase identifications corresponding to the hotel parameters to be trained in the hotel parameters to be trained.
In a possible embodiment, the obtaining module is further configured to obtain sample data; and the training module is used for training the sample data by utilizing a preset decision tree algorithm to obtain a pre-trained purchase prediction model.
In one possible embodiment, the apparatus further comprises: the comparison module is used for comparing the preset probability with the probability of the target hotel purchasing robot; and the dividing module is used for dividing the target hotel into an optimal hotel set under the condition that the probability of the target hotel for purchasing the robot is greater than or equal to the preset probability.
In one possible embodiment, the apparatus further comprises: the determining module is used for determining the hotel type of the target hotel; and the matching module is used for matching the robot recommendation information corresponding to the hotel type of the target hotel from the database according to the hotel type of the target hotel, wherein the database stores mapping relations between different hotel types and different robot recommendation information.
In one possible embodiment, the hotel parameters include at least one of the following parameters: hotel brand, hotel city, hotel location, hotel star level, number of hotel rooms, number of hotel employees, hotel occupancy, and hotel average house cost.
In a third aspect, an embodiment of the present application provides a storage medium, where a computer program is stored on the storage medium, and when the computer program is executed by a processor, the computer program performs the method according to the first aspect or any optional implementation manner of the first aspect.
In a fourth aspect, an embodiment of the present application provides an electronic device, including: a processor, a memory and a bus, the memory storing machine-readable instructions executable by the processor, the processor and the memory communicating via the bus when the electronic device is running, the machine-readable instructions when executed by the processor performing the method of the first aspect or any of the alternative implementations of the first aspect.
In a fifth aspect, the present application provides a computer program product which, when run on a computer, causes the computer to perform the method of the first aspect or any possible implementation manner of the first aspect.
In order to make the aforementioned and other objects, features and advantages of the present invention comprehensible, preferred embodiments accompanied with figures are described in detail below.
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In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments of the present application will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and that those skilled in the art can also obtain other related drawings based on the drawings without inventive efforts.
Fig. 1 shows a flowchart of a method for predicting a probability of a hotel purchasing a robot according to an embodiment of the present application;
fig. 2 is a block diagram illustrating a structure of an apparatus for predicting a probability of a hotel purchasing robot according to an embodiment of the present disclosure;
fig. 3 shows a block diagram of an electronic device according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be described below with reference to the drawings in the embodiments of the present application.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures. Meanwhile, in the description of the present application, the terms "first", "second", and the like are used only for distinguishing the description, and are not to be construed as indicating or implying relative importance.
In order to solve the problem that the hotel purchase intention assessment is inaccurate in the prior art, the embodiment of the application skillfully provides a scheme for predicting the probability of the hotel purchase robot, and a prediction result containing the probability of the target hotel purchase robot corresponding to the hotel parameter to be predicted is obtained by obtaining the hotel parameter to be predicted and inputting the hotel parameter to be predicted into a pre-trained purchase prediction model, so that the accuracy of predicting the hotel purchase intention can be improved through the technical scheme.
Referring to fig. 1, fig. 1 is a flowchart illustrating a method for predicting a probability of a hotel purchasing a robot according to an embodiment of the present disclosure. It should be understood that the method shown in fig. 1 may be performed by an apparatus for predicting the probability of a hotel purchasing a robot, which may correspond to the apparatus shown in fig. 2 below, and the apparatus may be various devices capable of performing the method, such as a personal computer, a server, or a network device, for example, and the embodiments of the present application are not limited thereto, and specifically include the following steps:
step S110, sample data is acquired. The sample data comprises a plurality of hotel parameters to be trained and purchase identifications corresponding to each hotel parameter to be trained in the hotel parameters to be trained.
It should be understood that the sample data may be input into the device by the user when the model is trained, or may be pre-stored in the device before the model is trained, and the embodiment of the present application is not limited thereto.
It should also be understood that specific parameters included in hotel parameters to be trained may be set according to actual needs, and the embodiments of the present application are not limited thereto.
For example, hotel parameters to be trained may include at least one of the following parameters: hotel brand, city of the hotel (e.g., beijing, etc.), location of the hotel (e.g., one ring, two rings, etc.), hotel star (e.g., a five star hotel, etc.), number of hotel rooms (e.g., 36 rooms), number of hotel employees (e.g., 100 employees, etc.), hotel occupancy, and hotel average house fee (e.g., average house fee for all 10 rooms, etc.).
As another example, the hotel parameters to be trained may further include at least one of the following parameters: a hotel group to which the hotel belongs (for example, a hotel belongs to a hotel group), the number of hotels owned by the hotel group to which the hotel belongs, the distribution of the hotels owned by the hotel group, the amount of financing of the hotel, whether the hotel is a continuous hotel, the average salaries of all employees of the hotel, and the employee density of the hotel (determined according to the per-capita area).
In order to facilitate understanding of step S110, the following description is made by way of specific examples.
In particular, data related to a plurality of hotels may be collected by a data collection system in the device, and the data related to the plurality of hotels may be further aggregated into one data set by the data collection system, so that the device may obtain sample data.
It should be understood that, although the data set is described as the sample data, those skilled in the art should also understand that the data set after preprocessing may also be used as the sample data, and the embodiments of the present application are not limited thereto.
And step S120, training the sample data by using a preset decision tree algorithm to obtain a pre-trained purchase prediction model.
It should be understood that the specific algorithm or implementation process (or training process) of the preset decision tree algorithm may be set according to actual requirements, and the embodiment of the present application is not limited thereto.
Alternatively, the preset decision tree algorithm may be the ID3 algorithm, wherein the ID3 algorithm is a greedy algorithm.
Alternatively, the predetermined decision tree algorithm may be a CART (classification and regression tree) algorithm.
Optionally, a preset decision tree algorithm may grade the sample data according to a preset rule, and then determine whether the corresponding hotel purchases the robot according to the grade after the grade is divided.
It should be understood that the preset rule may be set according to actual requirements, and the embodiment of the present application is not limited thereto.
For example, the user may classify city classes based on a customized rule (e.g., classify a first-line city into 5 th class, etc.), and each city class corresponds to a result that the robot will not be purchased, so that in a case where the sample data includes a city where the hotel is located, it may be determined whether the hotel will purchase the robot by determining the city class where the hotel is located (e.g., the city classes include five classes of 1 st to 5 th classes, where the cities belonging to 3 rd to 5 th classes purchase the robot, and the cities belonging to 1 st to 2 nd classes do not purchase the robot, so that the hotel determines that the robot will not be purchased by determining the city where the hotel is located.
For another example, the user may divide the average salary into a plurality of levels based on a customized rule (e.g., average salary of 3000-.
In addition, since the sample data may include a plurality of parameters, and the result corresponding to each parameter may be different (for example, the sample data includes 10 parameters, where 6 parameters correspond to the purchase of the robot, and 4 parameters correspond to the purchase of the robot), the results corresponding to all the parameters may be counted to determine the final result corresponding to the sample data.
Optionally, the number N of parameters corresponding to the purchasing robot in the plurality of parameters is counted, and the number M of parameters corresponding to the non-purchasing robot in the plurality of parameters is also counted, and the sizes of N and M can be compared. If N is larger than M, the output result corresponding to the current sample data is that the robot can be purchased; and if N is less than or equal to M, the output result corresponding to the current sample data is that the robot cannot be purchased.
It should be appreciated that purchasing a robot or not represents a predictor.
Optionally, the number N of parameters corresponding to the purchasing robot in the plurality of parameters is counted, and N may be compared with a preset threshold. If N is larger than a preset threshold value, the output result corresponding to the current sample data is that the robot can be purchased; and if the N is less than or equal to the preset threshold, the output result corresponding to the current sample data is that the robot cannot be purchased.
It should be understood that the size of the preset threshold may also be set according to actual requirements, and the embodiments of the present application are not limited thereto.
In order to facilitate understanding of step S120, the following description is made by way of specific examples.
Specifically, after the data set is obtained, the data set may be preprocessed by a data preprocessing system in the device to obtain a preprocessed data set. And the preprocessed data set can be divided into a training set and a training set, and the training set is trained through a preset decision tree algorithm, so that a plurality of hotel parameters to be trained in the training set can be used as input, and a purchase identifier corresponding to each hotel parameter to be trained in the training set can be used as output, so that the established initial purchase prediction model is trained through the decision data algorithm, and the trained purchase prediction model is obtained. And, the performance of the trained purchase prediction model may also be tested through the test set.
It should be understood that the data processing manner included in the preprocessing may be set according to actual requirements, and the embodiment of the present application is not limited thereto.
For example, the pre-processing may include at least one of data screening and data summarization. The data screening refers to screening data in a data set, for example, duplicate data, error data, and the like in the data set can be deleted; the data summarization refers to summarizing data in a data set according to a preset rule, for example, the preset rule may include associating data of the same hotel with a hotel, and the like.
It should also be understood that the specific identifier of the purchase identifier may also be set according to actual requirements, and the embodiment of the present application is not limited thereto.
For example, the purchase identification includes 1 and 0, where 1 identifies the purchasing robot and 0 identifies the non-purchasing robot. As another example, the purchase identification includes A and B, where A identifies the purchasing robot and B identifies the non-purchasing robot.
It should also be understood that the type of the specific model of the purchase prediction model may also be set according to actual needs, as long as it is ensured that the trained purchase prediction model can achieve prediction, and the embodiment of the present application is not limited thereto.
For example, the purchase prediction model may be a decision tree prediction model. As another example, the purchase prediction model may also be a bivariate model. As another example, the purchase prediction model may also be a random forest model.
It should be noted that, although step S110 and step S120 are shown in fig. 1, it should be understood by those skilled in the art that, in the case where no training model is required, that is, when the purchasing prediction model is a purchasing prediction model trained in advance, the training process of the model corresponding to step S110 and step S120 in fig. 1 may be omitted.
And step S130, obtaining hotel parameters to be predicted.
It should be understood that the hotel parameters to be predicted on-side may be relevant parameters for the hotel to be predicted.
It should also be understood that the hotel parameters to be predicted may be input into the device by the user at the time of prediction, or may be pre-stored in the device before prediction, and the embodiment of the present application is not limited thereto.
It should also be understood that the parameters included in the hotel parameters to be predicted may be set according to actual needs, and the embodiments of the present application are not limited thereto.
For example, the hotel parameters to be predicted may include at least one of the following parameters: the hotel brand, the city where the hotel is located, the location of the hotel, the number of hotel rooms, the number of hotel employees, the hotel occupancy rate, and the hotel average house fee.
As another example, the hotel parameters to be predicted may include at least one of the following parameters: the hotel management system comprises a hotel brand, a hotel city, a hotel position, the number of hotel rooms, the number of hotel employees, the hotel rate, the average room charge of the hotel, the hotel group to which the hotel belongs, the number of hotels owned by the hotel group to which the hotel belongs, the distribution of the hotels owned by the hotel group, the amount of hotel financing, whether the hotel is a continuous hotel, the average salary of all employees of the hotel and the employee density of the hotel.
It should be noted that the parameters included in the hotel parameters to be predicted and the parameters included in the hotel parameters to be trained may be the same or different, and the embodiment of the present application is not limited thereto.
And step S140, inputting hotel parameters to be predicted into a pre-trained purchase prediction model to obtain a prediction result. And the prediction result comprises the probability of the target hotel purchasing robot corresponding to the hotel parameter to be predicted.
Specifically, the user may take hotel parameters to be predicted as input, and input the hotel parameters to be predicted into the pre-trained purchase prediction model, so as to obtain the prediction result through the pre-trained purchase prediction model. The predicted result includes: the probability that the target hotel purchased the robot and the probability that the target hotel did not purchase the robot.
For example, in the case that the hotel parameters to be predicted include a five-star hotel, beijing (the city where the target hotel is located), 180 rooms (the number of rooms in the target hotel), and 100 employees (the number of employees in the target hotel), the hotel parameters to be predicted may be input into a pre-trained purchase prediction model, so as to obtain a prediction result. Wherein the prediction result comprises: the probability of the hotel purchasing the robot is 80%, and the probability of the hotel not purchasing the robot is 20%.
And S150, comparing the preset probability with the probability of the robot purchased by the target hotel. And under the condition that the probability of the target hotel for purchasing the robot is greater than or equal to the preset probability, dividing the target hotel into an optimal hotel set.
It should be understood that the specific value of the preset probability may be set according to actual requirements, and the embodiment of the present application is not limited thereto.
For example, the preset probability may be 70%. For another example, the predetermined probability may also be 80%. For another example, the predetermined probability may also be 85%.
In order to facilitate understanding of step S150, the following description is made by way of specific examples.
Specifically, the probability of the target hotel purchasing the robot is compared with the preset probability. If the probability that the target hotel purchases the robot is larger than or equal to the preset probability, the possibility that the target hotel purchases the robot is determined to be high, the target hotel can be divided into the preferred hotel set, and accordingly the hotels in the preferred hotel set can be regarded as preferred customers of the sales promotion robot. And if the probability that the target hotel purchases the robot is smaller than the preset probability, the target hotel can be divided into a non-preferred hotel set.
And step S160, determining the hotel type of the target hotel under the condition that the probability of the target hotel for purchasing the robot is greater than or equal to the preset probability.
It should be understood that the specific type determination manner of the hotel type may be set according to actual requirements, and the embodiment of the present application is not limited thereto.
In order to facilitate understanding of step S160, the following description is made by way of specific examples.
Optionally, the hotel type of the target hotel is determined according to the hotel parameter to be predicted. Wherein the hotel type may include: scenic spot hotels and business hotels.
Therefore, the robot can be accurately recommended to the target hotel by determining the type of the hotel. For example, in the case that the target hotel is determined to be a hotel in a scenic spot, a robot with map query and route guidance functions can be recommended to the hotel in the scenic spot by combining the characteristics of the scenic spot. For another example, in a case where the target hotel is determined to be a business hotel, a robot that can have an application program for placing an order through WeChat or the like may be recommended to the business hotel in combination with the characteristics of the business hotel.
It should be understood that although two hotel types are shown above, one skilled in the art will appreciate that the hotel types may also include other types, and the embodiments of the present application are not limited thereto.
Optionally, the hotel type of the target hotel is determined according to the probability of the target hotel for purchasing the robot, wherein the hotel type may include a hotel with a primary demand and a hotel with a secondary demand, the hotel with the primary demand corresponds to a first preset probability interval, and the hotel with the secondary demand corresponds to a second preset probability interval.
It should be understood that the first preset probability interval and the second preset probability interval may be set according to actual requirements, and the embodiment of the present application is not limited thereto.
For example, the first predetermined probability interval may be 60% to 80% (including 60%, but not including 80%), and the second predetermined probability interval may be 80% to 100% (including 80% and 100%).
Therefore, the robot can be accurately recommended to the target hotel by determining the type of the hotel. For example, in the case where the hotel type includes a hotel with primary demand (the probability of the target hotel purchasing the robot is in a first preset probability interval) and a hotel with secondary demand (the probability of the target hotel purchasing the robot is in a second preset probability interval), a robot with activity (such as discount) may be recommended to the hotel with primary demand, and a multifunctional robot may also be recommended to the hotel with secondary demand, and the like.
And S170, matching the robot recommendation information corresponding to the hotel type of the target hotel from a database according to the hotel type of the target hotel, wherein the database stores mapping relations between different hotel types and different robot recommendation information.
Specifically, because the database stores mapping relationships between different hotel types and different robot recommendation information, the robot recommendation information corresponding to the hotel type of the target hotel can be determined by searching the mapping relationship matched with the hotel type of the target hotel. Therefore, under the condition that the robot recommendation information is determined, the salesperson of the robot can recommend the robot to the related personnel of the target hotel according to the robot recommendation information, and the success rate of selling the robot can be improved.
Therefore, according to the embodiment of the application, the prediction result containing the probability of the target hotel purchasing robot corresponding to the hotel parameter to be predicted is obtained by obtaining the hotel parameter to be predicted and inputting the hotel parameter to be predicted into the pre-trained purchasing prediction model, and therefore, the accuracy of predicting the hotel purchasing intention can be improved through the technical scheme.
It should be understood that the above method for predicting the probability of the hotel purchasing the robot is only exemplary, and those skilled in the art can make various modifications according to the above method, and the solution after the modification is also within the scope of the present application.
Moreover, while the operations of the method of the invention are depicted in the drawings in a particular order, this does not require or imply that the operations must be performed in this particular order, or that all of the illustrated operations must be performed, to achieve desirable results. Rather, the steps depicted in the flowcharts may change the order of execution. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step execution, and/or one step broken down into multiple step executions. For example, for fig. 1, steps S110 to S120 may be omitted.
Referring to fig. 2, fig. 2 shows a block diagram of a device 200 for predicting a probability of a hotel purchasing robot according to an embodiment of the present application, it should be understood that the device 200 corresponds to the method embodiment of fig. 1, and is capable of performing the steps of the method embodiment, and specific functions of the device 200 may be referred to the description above, and a detailed description is appropriately omitted herein to avoid redundancy. The device 200 includes at least one software functional module that can be stored in a memory in the form of software or firmware (firmware) or solidified in an Operating System (OS) of the device 200. Specifically, the apparatus 200 includes:
an obtaining module 210, configured to obtain hotel parameters to be predicted; the input module 220 is configured to input hotel parameters to be predicted into a pre-trained purchase prediction model to obtain a prediction result, where the prediction result includes a probability that a target hotel corresponding to the hotel parameters to be predicted purchases the robot, the pre-trained purchase prediction model is obtained according to sample data, and the sample data includes a plurality of hotel parameters to be trained and a purchase identifier corresponding to each hotel parameter to be trained in the hotel parameters to be trained.
In a possible embodiment, the obtaining module 210 is further configured to obtain sample data; and the training module (not shown) is used for training the sample data by using a preset decision tree algorithm to obtain a pre-trained purchase prediction model.
In one possible embodiment, the apparatus 200 further comprises: a comparison module (not shown) for comparing the preset probability with the probability of the target hotel purchasing the robot; and a dividing module (not shown) for dividing the target hotel into the preferred hotel set if the probability that the target hotel purchases the robot is greater than or equal to the preset probability.
In one possible embodiment, the apparatus 200 further comprises: a determination module (not shown) for determining the hotel type of the target hotel; and a matching module (not shown) for matching the robot recommendation information corresponding to the hotel type of the target hotel from the database according to the hotel type of the target hotel, wherein the database stores mapping relationships between different hotel types and different robot recommendation information.
In one possible embodiment, the hotel parameters include at least one of the following parameters: hotel brand, hotel city, hotel location, hotel star level, number of hotel rooms, number of hotel employees, hotel occupancy, and hotel average house cost.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working process of the apparatus described above may refer to the corresponding process in the foregoing method, and will not be described in too much detail herein.
Fig. 3 shows a block diagram of an electronic device 300 according to an embodiment of the present application. As shown in fig. 3, electronic device 300 may include a processor 310, a communication interface 320, a memory 330, and at least one communication bus 340. Wherein the communication bus 340 is used for realizing direct connection communication of these components. The communication interface 320 of the device in the embodiment of the present application is used for performing signaling or data communication with other node devices. The processor 310 may be an integrated circuit chip having signal processing capabilities. The Processor 310 may be a general-purpose Processor, and includes a Central Processing Unit (CPU), a Network Processor (NP), and the like; the integrated Circuit may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete gate or transistor logic device, or discrete hardware components. The various methods, steps, and logic blocks disclosed in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor 310 may be any conventional processor or the like.
The Memory 330 may be, but is not limited to, a Random Access Memory (RAM), a Read Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Read-Only Memory (EPROM), an electrically Erasable Read-Only Memory (EEPROM), and the like. The memory 330 stores computer readable instructions that, when executed by the processor 310, the electronic device 300 may perform the following steps: obtaining hotel parameters to be predicted; inputting the hotel parameters to be predicted into a pre-trained purchase prediction model to obtain a prediction result, wherein the prediction result comprises the probability of the target hotel purchase robot corresponding to the hotel parameters to be predicted, the pre-trained purchase prediction model is obtained according to sample data, and the sample data comprises a plurality of hotel parameters to be trained and purchase identifications corresponding to each hotel parameter to be trained in the hotel parameters to be trained.
The electronic device 300 may further include a memory controller, an input-output unit, an audio unit, and a display unit.
The memory 330, the memory controller, the processor 310, the peripheral interface, the input/output unit, the audio unit, and the display unit are electrically connected to each other directly or indirectly to implement data transmission or interaction. For example, these elements may be electrically connected to each other via one or more communication buses 340. The processor 310 is configured to execute executable modules stored in the memory 330, such as software functional modules or computer programs included in the electronic device 300.
The input and output unit is used for providing input data for a user to realize the interaction of the user and other equipment. The input/output unit may be, but is not limited to, a mouse, a keyboard, and the like.
The audio unit provides an audio interface to the user, which may include one or more microphones, one or more speakers, and audio circuitry.
The display unit provides an interactive interface (e.g. a user interface) between the electronic device and a user or for displaying image data to a user reference. In this embodiment, the display unit may be a liquid crystal display or a touch display. In the case of a touch display, the display can be a capacitive touch screen or a resistive touch screen, which supports single-point and multi-point touch operations. The support of single-point and multi-point touch operations means that the touch display can sense touch operations simultaneously generated from one or more positions on the touch display, and the sensed touch operations are sent to the processor for calculation and processing.
It will be appreciated that the configuration shown in fig. 3 is merely illustrative and that the electronic device 300 may include more or fewer components than shown in fig. 3 or may have a different configuration than shown in fig. 3. The components shown in fig. 3 may be implemented in hardware, software, or a combination thereof.
The present application provides a storage medium having stored thereon a computer program which, when executed by a processor, performs the method of an embodiment.
The present application also provides a computer program product which, when run on a computer, causes the computer to perform the method of the method embodiments.
It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working process of the system described above may refer to the corresponding process in the foregoing method, and will not be described in too much detail herein.
It should be noted that, in the present specification, the embodiments are all described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments may be referred to each other. For the device-like embodiment, since it is basically similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method can be implemented in other ways. The apparatus embodiments described above are merely illustrative, and for example, the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present application. 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 and/or flowchart illustration, and combinations of blocks in the block diagrams and/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.
In addition, functional modules in the embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes. It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The above description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application. It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures.
The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present application, and shall be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. A method of predicting a probability of a hotel purchasing a robot, comprising:
obtaining hotel parameters to be predicted;
inputting the hotel parameters to be predicted into a pre-trained purchase prediction model to obtain a prediction result, wherein the prediction result comprises the probability of the target hotel purchase robot corresponding to the hotel parameters to be predicted, the pre-trained purchase prediction model is obtained according to sample data, and the sample data comprises a plurality of hotel parameters to be trained and purchase identifications corresponding to each hotel parameter to be trained in the hotel parameters to be trained.
2. The method of claim 1, wherein prior to said obtaining hotel parameters to be predicted, the method further comprises:
acquiring the sample data;
and training the sample data by utilizing a preset decision tree algorithm to obtain the pre-trained purchase prediction model.
3. The method according to claim 1 or 2, characterized in that the method further comprises:
comparing the preset probability with the probability of the target hotel purchasing robot;
and under the condition that the probability of the target hotel for purchasing the robot is greater than or equal to the preset probability, dividing the target hotel into an optimal hotel set.
4. The method of claim 3, further comprising:
determining a hotel type of the target hotel;
and matching the robot recommendation information corresponding to the hotel type of the target hotel from a database according to the hotel type of the target hotel, wherein the database stores mapping relationships between different hotel types and different robot recommendation information.
5. The method of claim 1, wherein the hotel parameters comprise at least one of: hotel brand, hotel city, hotel location, hotel star level, number of hotel rooms, number of hotel employees, hotel occupancy, and hotel average house cost.
6. An apparatus for predicting a probability of a hotel purchasing a robot, comprising:
the system comprises an acquisition module, a prediction module and a prediction module, wherein the acquisition module is used for acquiring hotel parameters to be predicted;
the input module is used for inputting the hotel parameters to be predicted into a pre-trained purchase prediction model to obtain a prediction result, wherein the prediction result comprises the probability of the target hotel purchasing robot corresponding to the hotel parameters to be predicted, the pre-trained purchase prediction model is obtained according to sample data, and the sample data comprises a plurality of hotel parameters to be trained and purchase identification corresponding to each hotel parameter to be trained in the hotel parameters to be trained.
7. The apparatus of claim 6, wherein the obtaining module is further configured to obtain the sample data;
and the training module is used for training the sample data by utilizing a preset decision tree algorithm to obtain the pre-trained purchase prediction model.
8. The apparatus of claim 6 or 7, further comprising:
the comparison module is used for comparing the preset probability with the probability of the target hotel purchasing robot;
and the dividing module is used for dividing the target hotel into an optimal hotel set under the condition that the probability of the target hotel for purchasing the robot is greater than or equal to the preset probability.
9. The apparatus of claim 8, further comprising:
a determining module, configured to determine a hotel type of the target hotel;
and the matching module is used for matching the robot recommendation information corresponding to the hotel type of the target hotel from a database according to the hotel type of the target hotel, wherein the database stores mapping relations between different hotel types and different robot recommendation information.
10. The apparatus of claim 6, wherein the hotel parameters comprise at least one of: hotel brand, hotel city, hotel location, hotel star level, number of hotel rooms, number of hotel employees, hotel occupancy, and hotel average house cost.
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