CN112132618A - Commodity price determining method, device and equipment and readable storage medium - Google Patents
Commodity price determining method, device and equipment and readable storage medium Download PDFInfo
- Publication number
- CN112132618A CN112132618A CN202011011465.9A CN202011011465A CN112132618A CN 112132618 A CN112132618 A CN 112132618A CN 202011011465 A CN202011011465 A CN 202011011465A CN 112132618 A CN112132618 A CN 112132618A
- Authority
- CN
- China
- Prior art keywords
- commodity
- priced
- price
- determining
- volume
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0283—Price estimation or determination
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0201—Market modelling; Market analysis; Collecting market data
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0201—Market modelling; Market analysis; Collecting market data
- G06Q30/0202—Market predictions or forecasting for commercial activities
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Development Economics (AREA)
- Strategic Management (AREA)
- Finance (AREA)
- Accounting & Taxation (AREA)
- Theoretical Computer Science (AREA)
- Entrepreneurship & Innovation (AREA)
- General Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Physics & Mathematics (AREA)
- Game Theory and Decision Science (AREA)
- Economics (AREA)
- Marketing (AREA)
- General Business, Economics & Management (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Bioinformatics & Computational Biology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Artificial Intelligence (AREA)
- General Engineering & Computer Science (AREA)
- Life Sciences & Earth Sciences (AREA)
- Evolutionary Computation (AREA)
- Evolutionary Biology (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The application discloses a commodity price determining method, a commodity price determining device, commodity price determining equipment and a computer readable storage medium, wherein the method comprises the following steps: obtaining historical sales data of commodities of the same type as the commodities to be priced, and determining preference information of a user according to the historical sales data; extracting characteristic data from the preference information, and training the characteristic data to obtain a browsing amount prediction model; determining target characteristic data according to commodity information of commodities to be priced, inputting the target characteristic data into a browsing volume prediction model, and calculating predicted browsing volume of the commodities to be priced; and inputting the predicted browsing amount into a pre-established volume conversion model, calculating the predicted volume of the commodity to be priced, and determining the price of the commodity to be priced according to the predicted volume. According to the technical scheme disclosed by the application, the price of the commodity to be priced is automatically determined by utilizing the historical sales data of the commodity and the commodity information of the commodity to be priced without manual intervention, so that the accuracy and the efficiency of determining the price of the commodity to be priced are improved.
Description
Technical Field
The present application relates to the field of commodity pricing technologies, and in particular, to a method, an apparatus, a device, and a computer-readable storage medium for determining a commodity price.
Background
Sales platforms need to price goods before they can be sold so that the goods can be better sold.
At present, commodities are often priced manually according to experience, but as sku (Stock Keeping Unit) of the commodities is more and more abundant, workload of manual pricing is more and more large, so that efficiency of commodity pricing can be reduced by manual pricing, accuracy of commodity pricing can be reduced due to interference of human factors and the like by a mode of manually pricing according to experience, particularly when a platform carries out sales promotion activities, if pricing is too low, the profit of a merchant is insufficient, economic benefits are not obtained, and if pricing is too high, the sales quantity of the commodities cannot be well improved, so that economic benefits are also not obtained.
In summary, how to improve the accuracy and efficiency of determining the price of a commodity is a technical problem to be urgently solved by those skilled in the art.
Disclosure of Invention
In view of the above, an object of the present application is to provide a method, an apparatus, a device and a computer-readable storage medium for determining a commodity price, which are used to improve the accuracy and efficiency of commodity price determination.
In order to achieve the above purpose, the present application provides the following technical solutions:
a method of price determination for an item, comprising:
obtaining historical sales data of commodities of the same type as the commodities to be priced, and determining preference information of a user according to the historical sales data;
extracting characteristic data from the preference information, and training the characteristic data to obtain a browsing amount prediction model;
determining target characteristic data according to the commodity information of the commodity to be priced, inputting the target characteristic data into the browsing volume prediction model, and calculating the predicted browsing volume of the commodity to be priced;
inputting the predicted browsing amount into a pre-established volume conversion model, calculating the predicted volume of the commodity to be priced, and determining the price of the commodity to be priced according to the predicted volume.
Preferably, the determining the price of the commodity to be priced according to the predicted volume includes:
and calculating the price of the commodity to be priced when the profit is the maximum according to the predicted volume of transaction and the cost of the commodity to be priced, and determining the price of the commodity to be priced when the profit is the maximum as the price of the commodity to be priced.
Preferably, the process of pre-establishing the traffic conversion model includes:
acquiring historical browsing amount of the same type of commodity to be priced and final transaction amount corresponding to the historical browsing amount, and taking the historical browsing amount and the final transaction amount corresponding to the historical browsing amount as a sample set;
dividing the sample set into a training set, a verification set and a test set;
and training by using the training set, verifying the traffic conversion model obtained by training by using the verification set, testing the traffic conversion model obtained by training by using the test set, and determining the final traffic conversion model according to the test.
Preferably, the training of the feature data includes:
and training the characteristic data by using a logistic regression model and/or a tree model algorithm.
Preferably, after determining the price of the item to be priced according to the predicted volume, the method further includes:
acquiring the purchasing behavior of the user on the commodity to be priced;
and adjusting the price of the commodity to be priced according to the purchasing behavior.
Preferably, before extracting feature data from the preference information, the method further includes:
judging whether the preference information has the characteristic data or not, if so, determining the preference information as a positive sample, and if not, determining the preference information as a negative sample;
accordingly, extracting feature data from the preference information includes:
extracting the feature data from the positive sample.
An article price determining apparatus comprising:
the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring historical sales data of commodities of the same type as commodities to be priced and determining preference information of a user according to the historical sales data;
the training module is used for extracting characteristic data from the preference information and training the characteristic data to obtain a browsing amount prediction model;
the first calculation module is used for determining target characteristic data according to the commodity information of the commodity to be priced, inputting the target characteristic data into the browsing volume prediction model and calculating the predicted browsing volume of the commodity to be priced;
and the second calculation module is used for inputting the predicted browsing volume into a pre-established volume conversion model, calculating the predicted volume of the commodity to be priced, and determining the price of the commodity to be priced according to the predicted volume.
Preferably, the second calculation module includes:
and the calculating unit is used for calculating the price of the commodity to be priced when the profit is maximum according to the predicted volume of transaction and the cost of the commodity to be priced, and determining the price of the commodity to be priced when the profit is maximum as the price of the commodity to be priced.
An article price determining apparatus comprising:
a memory for storing a computer program;
a processor for implementing the steps of the item price determination method according to any one of the above when executing the computer program.
A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the item price determination method according to any one of the preceding claims.
The application provides a commodity price determining method, a commodity price determining device and a computer readable storage medium, wherein the method comprises the following steps: obtaining historical sales data of commodities of the same type as the commodities to be priced, and determining preference information of a user according to the historical sales data; extracting characteristic data from the preference information, and training the characteristic data to obtain a browsing amount prediction model; determining target characteristic data according to commodity information of commodities to be priced, inputting the target characteristic data into a browsing volume prediction model, and calculating predicted browsing volume of the commodities to be priced; and inputting the predicted browsing amount into a pre-established volume conversion model, calculating the predicted volume of the commodity to be priced, and determining the price of the commodity to be priced according to the predicted volume.
The technical scheme disclosed by the application determines the preference information of a user according to historical sales data of commodities of the same type as the commodities to be priced, trains characteristic data extracted from the preference information to obtain a browsing volume prediction model, calculates the predicted browsing volume of the commodities to be priced by using target characteristic data determined from the commodity information of the commodities to be priced and the obtained browsing volume prediction model, calculates the predicted volume of the commodities to be priced by using a pre-established volume conversion model, determines the price of the commodities to be priced according to the predicted volume of the trades, automatically calculates the predicted volume of the commodities to be priced according to the historical sales data of the commodities, the commodity information of the commodities to be priced and the pre-established volume conversion model, and automatically determines the price of the commodities to be priced according to the predicted volume of the commodities to be priced without human intervention, the method and the device have the advantages that the influence of human factors on the process of determining the price of the commodity to be priced is avoided, so that the accuracy and the reasonability of determining the price of the commodity to be priced are improved, the labor consumption and the labor cost of determining the price of the commodity to be priced are reduced, and the efficiency of determining the price of the commodity to be priced is improved.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, it is obvious that the drawings in the following description are only embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
Fig. 1 is a flowchart of a method for determining a price of a commodity according to an embodiment of the present application;
fig. 2 is a schematic structural diagram of a commodity price determining apparatus according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of a commodity price determining apparatus according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
Referring to fig. 1, which shows a flowchart of a method for determining a price of a commodity according to an embodiment of the present application, a method for determining a price of a commodity according to an embodiment of the present application may include:
s11: historical sales data of the commodities of the same type as the commodities to be priced are obtained, and preference information of the user is determined according to the historical sales data.
In view of the fact that the accuracy of commodity pricing can be reduced due to the influence of human factors and the efficiency of commodity pricing can be reduced when commodity price is determined manually according to experience in the prior art, the commodity price determining method is provided and used for improving the accuracy and efficiency of commodity price determination.
Specifically, historical sales data of the same type of goods as the goods to be priced (which may be all goods of the same type) may be obtained first, where the historical sales data mentioned here may include categories, sales numbers, sales times, and the like, and preference information of the user may be determined according to the historical sales data of the same type of goods as the goods to be priced, where the preference information of the user mentioned here includes basic information of the user and behavior information of the user, the basic information of the user may include age, region, gender, and the like, and the behavior information of the user may include an access record of the user, an obtaining frequency of the goods clicked by the user, a category of the goods clicked by the user, and the like.
In the above process, the users may be divided into groups with different characteristics, and the liveness and the tendency of the groups with different characteristics may have different expressions, for example, a commodity with a large click rate is determined as the preference information of the user for the commodity.
S12: and extracting characteristic data from the preference information, and training the characteristic data to obtain a browsing amount prediction model.
After step S11 is completed, required feature data may be extracted from the determined user preference information, where the required feature data is feature data related to the event for processing commodity price determination, specifically feature data related to browsing amount determination of commodities, and the feature data is interaction information of the user and commodity contents quantized and represented by multi-dimensional vectors, each feature referring to each component in the multi-dimensional vectors.
After the feature data is extracted, the feature data may be trained to obtain a browsing amount prediction model for predicting a browsing amount of the commodity.
S13: and determining target characteristic data according to the commodity information of the commodity to be priced, inputting the target characteristic data into a browsing volume prediction model, and calculating the predicted browsing volume of the commodity to be priced.
After step S12 is executed, the target feature data that can be the input amount of the browsing amount prediction model obtained in step S12 may be determined according to the commodity information (e.g., category, audience, etc.) of the commodity to be priced, that is, the target feature data for inputting into the browsing amount prediction model obtained in step S12 may be determined according to the commodity information of the commodity to be priced, and then the target feature data may be input into the browsing amount prediction model, and the predicted browsing amount of the commodity to be priced may be calculated by the browsing amount prediction model.
S14: and inputting the predicted browsing amount into a pre-established volume conversion model, calculating the predicted volume of the commodity to be priced, and determining the price of the commodity to be priced according to the predicted volume.
And inputting the calculated predicted browsing amount of the commodity to be priced and the predicted browsing amount of the commodity to be priced into a pre-established trading volume conversion model so as to calculate the predicted trading volume of the commodity to be priced, and then determining the price of the commodity to be priced according to the calculated predicted trading volume of the commodity to be priced.
According to the method and the device, the price of the commodity to be priced can be automatically determined based on the historical sales data of the commodity of the same type as the commodity to be priced and the commodity information of the commodity to be priced, and manual participation is not needed, so that the influence of human factors on the commodity price determination can be reduced, the accuracy of commodity price determination can be improved, the commodity price determination efficiency can be improved, and the labor consumption, the manual workload and the labor cost can be reduced.
The technical scheme disclosed by the application determines the preference information of a user according to historical sales data of commodities of the same type as the commodities to be priced, trains characteristic data extracted from the preference information to obtain a browsing volume prediction model, calculates the predicted browsing volume of the commodities to be priced by using target characteristic data determined from the commodity information of the commodities to be priced and the obtained browsing volume prediction model, calculates the predicted volume of the commodities to be priced by using a pre-established volume conversion model, determines the price of the commodities to be priced according to the predicted volume of the trades, automatically calculates the predicted volume of the commodities to be priced according to the historical sales data of the commodities, the commodity information of the commodities to be priced and the pre-established volume conversion model, and automatically determines the price of the commodities to be priced according to the predicted volume of the commodities to be priced without human intervention, the method and the device have the advantages that the influence of human factors on the process of determining the price of the commodity to be priced is avoided, so that the accuracy and the reasonability of determining the price of the commodity to be priced are improved, the labor consumption and the labor cost of determining the price of the commodity to be priced are reduced, and the efficiency of determining the price of the commodity to be priced is improved.
The commodity price determining method provided by the embodiment of the application, which determines the price of a commodity to be priced according to a predicted volume of transaction, may include:
and calculating the price of the commodity to be priced when the profit is the maximum according to the predicted volume of transaction and the cost of the commodity to be priced, and determining the price of the commodity to be priced when the profit is the maximum as the price of the commodity to be priced.
When the price of the commodity to be priced is determined according to the predicted volume of transaction, the price corresponding to the maximum profit of the commodity to be priced can be calculated according to the predicted volume of transaction of the commodity to be priced and the cost of the commodity to be priced, and the price corresponding to the maximum profit of the commodity to be priced can be determined as the price of the commodity to be priced, so that the sales profit of the commodity to be priced can be improved.
It should be noted that the predicted volume of the determined product to be priced is a function related to the price of the product to be priced, that is, x ═ f (p) can be obtained, where x is the predicted volume of the product to be priced, p is the price of the product to be priced, and f () represents the functional relationship that the predicted volume of the product to be priced is related to the price of the product to be priced, and at this time, the profit u (p) ═ f (p) of the product to be priced is obtained, then, the profit u (p) of the product to be priced is derived about p, and p' obtained when du (p)/dp is 0 is determined as the price of the product to be priced.
In the method for determining commodity price provided by the embodiment of the present application, the process of pre-establishing the volume conversion model may include:
acquiring historical browsing amount of the same type of goods to be priced and final transaction amount corresponding to the historical browsing amount, and taking the historical browsing amount and the final transaction amount corresponding to the historical browsing amount as a sample set;
dividing a sample set into a training set, a verification set and a test set;
and training by using the training set, verifying the traffic conversion model obtained by training by using the verification set, testing the traffic conversion model obtained by training by using the test set, and determining the final traffic conversion model according to the test.
In the present application, the process of pre-establishing the traffic conversion model specifically includes: the method comprises the steps of obtaining historical browsing amount of the same type of goods to be priced and final volume of deals corresponding to the historical browsing amount, taking the obtained historical browsing amount and the final volume of deals corresponding to the historical browsing amount as sample sets, dividing the sample sets into three sets, namely a training set, a verification set and a testing set, training models by using the training sets, verifying volume conversion models obtained by training by using the verification sets, continuously adjusting according to conditions, selecting the best model, training a final model by using the training sets and the verification sets, evaluating and testing by using the testing sets, determining the final volume of deals conversion model according to the tests, and facilitating the prediction of the volume of the goods to be priced by using the finally determined volume of deals conversion model.
After the training set is obtained by division, preprocessing such as data cleaning and data feature scaling (standardization or normalization) can be performed on the training set, so that model training can be performed on the preprocessed training set better.
The commodity price determining method provided by the embodiment of the application trains the characteristic data, and can include the following steps:
the feature data is trained using logistic regression models and/or tree model algorithms.
When the extracted feature data is trained, the feature data may be specifically trained by using a logistic regression model and/or a tree model algorithm to obtain a browsing amount prediction model.
The commodity price determining method provided by the embodiment of the application, after determining the price of the commodity to be priced according to the predicted volume of the transaction, may further include:
acquiring the purchasing behavior of a commodity to be priced by a user;
and adjusting the price of the commodity to be priced according to the purchasing behavior.
According to the method and the device, after the price of the commodity to be priced is determined according to the predicted volume of transaction and the commodity to be priced is sold for a period of time, the purchasing behavior of the commodity to be priced by the user can be obtained, the price of the commodity to be priced can be adjusted according to the purchasing behavior of the user, the price of the commodity can be customized reasonably, the accuracy of commodity price determination is improved, and the sales volume of the commodity is improved.
Before extracting the feature data from the preference information, the method for determining the price of the commodity provided by the embodiment of the application may further include:
judging whether the preference information has characteristic data or not, if so, determining the preference information as a positive sample, and if not, determining the preference information as a negative sample;
accordingly, extracting feature data from the preference information may include:
feature data is extracted from the positive sample.
Before extracting features from the preference information, whether the preference information has required feature data (namely, feature data related to a transaction for processing commodity price determination) or not can be judged, if the preference information has the required feature data, the preference information can be determined as a positive sample, if the preference information does not have the required feature data, the preference information can be determined as a negative sample, and then, when extracting the feature data from the preference information, the required feature data can be extracted from the determined positive sample only, and the negative sample is not subjected to the operation, so that the commodity price determination efficiency can be improved.
An embodiment of the present application further provides a product price determining apparatus, see fig. 2, which shows a schematic structural diagram of the product price determining apparatus provided in the embodiment of the present application, and the product price determining apparatus may include:
the first acquisition module 21 is configured to acquire historical sales data of commodities of the same type as the commodity to be priced, and determine preference information of a user according to the historical sales data;
the training module 22 is used for extracting characteristic data from the preference information and training the characteristic data to obtain a browsing amount prediction model;
the first calculation module 23 is configured to determine target feature data according to the commodity information of the commodity to be priced, input the target feature data into the browsing volume prediction model, and calculate a predicted browsing volume of the commodity to be priced;
and the second calculating module 24 is used for inputting the predicted browsing volume into a pre-established volume conversion model, calculating the predicted volume of the commodity to be priced, and determining the price of the commodity to be priced according to the predicted volume.
In an apparatus for determining a price of a commodity according to an embodiment of the present application, the second calculating module 24 may include:
and the calculating unit is used for calculating the price of the commodity to be priced when the profit is the maximum according to the predicted volume of transaction and the cost of the commodity to be priced, and determining the price of the commodity to be priced when the profit is the maximum as the price of the commodity to be priced.
The commodity price determining apparatus provided in the embodiment of the present application may further include a modeling module for establishing a volume conversion model in advance, where the modeling module may include:
an acquisition unit configured to: acquiring historical browsing amount of the same type of goods to be priced and final transaction amount corresponding to the historical browsing amount, and taking the historical browsing amount and the final transaction amount corresponding to the historical browsing amount as a sample set;
the dividing unit is used for dividing the sample set into a training set, a verification set and a test set;
and the obtaining model unit is used for training by using the training set, verifying the traffic conversion model obtained by training by using the verification set, testing the traffic conversion model obtained by training by using the test set, and determining the final traffic conversion model according to the test.
In an apparatus for determining a price of a commodity according to an embodiment of the present application, the training module 22 may include:
and the training unit is used for training the characteristic data by utilizing a logistic regression model and/or a tree model algorithm.
The commodity price determining device provided by the embodiment of the application can further comprise:
the second acquisition module is used for acquiring the purchasing behavior of the commodity to be priced by the user after the price of the commodity to be priced is determined according to the predicted volume of the transaction;
and the adjusting module is used for adjusting the price of the commodity to be priced according to the purchasing behavior.
The commodity price determining device provided by the embodiment of the application can further comprise:
the judging module is used for judging whether the characteristic data exists in the preference information before extracting the characteristic data from the preference information, if so, the preference information is determined as a positive sample, and if not, the preference information is determined as a negative sample;
accordingly, training module 22 may include:
and the extraction unit is used for extracting the characteristic data from the positive sample.
An embodiment of the present application further provides a product price determining device, see fig. 3, which shows a schematic structural diagram of the product price determining device provided in the embodiment of the present application, and the device may include:
a memory 31 for storing a computer program;
the processor 32, when executing the computer program stored in the memory 31, may implement the following steps:
obtaining historical sales data of commodities of the same type as the commodities to be priced, and determining preference information of a user according to the historical sales data; extracting characteristic data from the preference information, and training the characteristic data to obtain a browsing amount prediction model; determining target characteristic data according to commodity information of commodities to be priced, inputting the target characteristic data into a browsing volume prediction model, and calculating predicted browsing volume of the commodities to be priced; and inputting the predicted browsing amount into a pre-established volume conversion model, calculating the predicted volume of the commodity to be priced, and determining the price of the commodity to be priced according to the predicted volume.
An embodiment of the present application further provides a computer-readable storage medium, in which a computer program is stored, and when the computer program is executed by a processor, the following steps may be implemented:
obtaining historical sales data of commodities of the same type as the commodities to be priced, and determining preference information of a user according to the historical sales data; extracting characteristic data from the preference information, and training the characteristic data to obtain a browsing amount prediction model; determining target characteristic data according to commodity information of commodities to be priced, inputting the target characteristic data into a browsing volume prediction model, and calculating predicted browsing volume of the commodities to be priced; and inputting the predicted browsing amount into a pre-established volume conversion model, calculating the predicted volume of the commodity to be priced, and determining the price of the commodity to be priced according to the predicted volume.
The computer-readable storage medium may include: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
For a description of a relevant part in a product price determining device, a device, and a computer-readable storage medium provided in the embodiments of the present application, reference may be made to the detailed description of a corresponding part in a product price determining method provided in the embodiments of the present application, and details are not repeated herein.
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. Furthermore, 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 elements inherent in the list. 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. In addition, parts of the above technical solutions provided in the embodiments of the present application, which are consistent with the implementation principles of corresponding technical solutions in the prior art, are not described in detail so as to avoid redundant description.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A method for determining a price of an article, comprising:
obtaining historical sales data of commodities of the same type as the commodities to be priced, and determining preference information of a user according to the historical sales data;
extracting characteristic data from the preference information, and training the characteristic data to obtain a browsing amount prediction model;
determining target characteristic data according to the commodity information of the commodity to be priced, inputting the target characteristic data into the browsing volume prediction model, and calculating the predicted browsing volume of the commodity to be priced;
inputting the predicted browsing amount into a pre-established volume conversion model, calculating the predicted volume of the commodity to be priced, and determining the price of the commodity to be priced according to the predicted volume.
2. The item price determining method according to claim 1, wherein determining the price of the item to be priced according to the predicted volume of bargain comprises:
and calculating the price of the commodity to be priced when the profit is the maximum according to the predicted volume of transaction and the cost of the commodity to be priced, and determining the price of the commodity to be priced when the profit is the maximum as the price of the commodity to be priced.
3. The commodity price determining method according to claim 1, wherein the process of previously establishing the volume conversion model includes:
acquiring historical browsing amount of the same type of commodity to be priced and final transaction amount corresponding to the historical browsing amount, and taking the historical browsing amount and the final transaction amount corresponding to the historical browsing amount as a sample set;
dividing the sample set into a training set, a verification set and a test set;
and training by using the training set, verifying the traffic conversion model obtained by training by using the verification set, testing the traffic conversion model obtained by training by using the test set, and determining the final traffic conversion model according to the test.
4. The commodity price determining method according to claim 1, wherein training the feature data includes:
and training the characteristic data by using a logistic regression model and/or a tree model algorithm.
5. The item price determining method according to claim 1, further comprising, after determining the price of the item to be priced according to the predicted volume, the step of:
acquiring the purchasing behavior of the user on the commodity to be priced;
and adjusting the price of the commodity to be priced according to the purchasing behavior.
6. The commodity price determining method according to claim 1, further comprising, before extracting feature data from the preference information:
judging whether the preference information has the characteristic data or not, if so, determining the preference information as a positive sample, and if not, determining the preference information as a negative sample;
accordingly, extracting feature data from the preference information includes:
extracting the feature data from the positive sample.
7. An article price determining apparatus, comprising:
the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring historical sales data of commodities of the same type as commodities to be priced and determining preference information of a user according to the historical sales data;
the training module is used for extracting characteristic data from the preference information and training the characteristic data to obtain a browsing amount prediction model;
the first calculation module is used for determining target characteristic data according to the commodity information of the commodity to be priced, inputting the target characteristic data into the browsing volume prediction model and calculating the predicted browsing volume of the commodity to be priced;
and the second calculation module is used for inputting the predicted browsing volume into a pre-established volume conversion model, calculating the predicted volume of the commodity to be priced, and determining the price of the commodity to be priced according to the predicted volume.
8. The commodity price determining apparatus according to claim 7, wherein the second calculation module includes:
and the calculating unit is used for calculating the price of the commodity to be priced when the profit is maximum according to the predicted volume of transaction and the cost of the commodity to be priced, and determining the price of the commodity to be priced when the profit is maximum as the price of the commodity to be priced.
9. An article price determining apparatus, characterized by comprising:
a memory for storing a computer program;
a processor for implementing the steps of the item price determination method according to any of claims 1 to 6 when executing the computer program.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the item price determination method according to any one of claims 1 to 6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011011465.9A CN112132618A (en) | 2020-09-23 | 2020-09-23 | Commodity price determining method, device and equipment and readable storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011011465.9A CN112132618A (en) | 2020-09-23 | 2020-09-23 | Commodity price determining method, device and equipment and readable storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112132618A true CN112132618A (en) | 2020-12-25 |
Family
ID=73839400
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011011465.9A Pending CN112132618A (en) | 2020-09-23 | 2020-09-23 | Commodity price determining method, device and equipment and readable storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112132618A (en) |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112767042A (en) * | 2021-01-26 | 2021-05-07 | 上海乐享似锦科技股份有限公司 | Group generation method and device, electronic equipment and storage medium |
| CN113205282A (en) * | 2021-05-31 | 2021-08-03 | 广州大学 | New retail commodity operation system and device based on big data analysis |
| CN113506127A (en) * | 2021-06-22 | 2021-10-15 | 特赞（上海）信息科技有限公司 | Information generation method and device |
| CN114693368A (en) * | 2022-04-14 | 2022-07-01 | 荃豆数字科技有限公司 | Behavior data-based customer maintenance method and device and storage medium |
| CN116777501A (en) * | 2023-07-18 | 2023-09-19 | 湖南师范大学 | Secondhand commodity price pricing method and device, electronic equipment and storage medium |
-
2020
- 2020-09-23 CN CN202011011465.9A patent/CN112132618A/en active Pending
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112767042A (en) * | 2021-01-26 | 2021-05-07 | 上海乐享似锦科技股份有限公司 | Group generation method and device, electronic equipment and storage medium |
| CN113205282A (en) * | 2021-05-31 | 2021-08-03 | 广州大学 | New retail commodity operation system and device based on big data analysis |
| CN113506127A (en) * | 2021-06-22 | 2021-10-15 | 特赞（上海）信息科技有限公司 | Information generation method and device |
| CN114693368A (en) * | 2022-04-14 | 2022-07-01 | 荃豆数字科技有限公司 | Behavior data-based customer maintenance method and device and storage medium |
| CN116777501A (en) * | 2023-07-18 | 2023-09-19 | 湖南师范大学 | Secondhand commodity price pricing method and device, electronic equipment and storage medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112132618A (en) | Commodity price determining method, device and equipment and readable storage medium | |
| CN110222272B (en) | Potential customer mining and recommending method | |
| CN106485562B (en) | Commodity information recommendation method and system based on user historical behaviors | |
| CN107169806B (en) | Method and device for determining influence degree of commodity attribute on purchase decision | |
| US20140279709A1 (en) | Systems and methods for determining costs of vehicle repairs and times to major repairs | |
| CN110766428A (en) | Data value evaluation system and method | |
| CN111080398A (en) | Commodity recommendation method and device, computer equipment and storage medium | |
| JP2006526858A (en) | System, method and computer program product for modeling demand, supply and related profitability of goods | |
| CN107665448A (en) | For determining the method, apparatus and storage medium of consumption contributed value | |
| CN116757794B (en) | Big data-based product recommendation method in wine selling applet | |
| CN110659926A (en) | Data value evaluation system and method | |
| CN112365295A (en) | Method, device and equipment for predicting order taking probability and readable storage medium | |
| CN116739217A (en) | Retail management method and system based on supply chain big data platform | |
| CN111861605A (en) | Business object recommendation method | |
| US20080208788A1 (en) | Method and system for predicting customer wallets | |
| CN111598588A (en) | Bill-swiping identification method | |
| CN111177581A (en) | Multi-platform-based social e-commerce website commodity recommendation method and device | |
| CN110633919A (en) | Method and device for evaluating business entity | |
| CN111598660A (en) | Computer screening device and method for screening suppliers and storage medium | |
| CN116611796A (en) | Exception detection method and device for store transaction data | |
| CN114817741A (en) | Financial product accurate recommendation method and device | |
| KR20160071899A (en) | Apparatus for evaluating a value of technique, method thereof, and storage media for storing a program evaluating avalue of technique | |
| CN114119107A (en) | Steel trade enterprise transaction evaluation method, device, equipment and storage medium | |
| CN113888244A (en) | Pricing method and device of data commodity, electronic equipment and computer readable storage medium | |
| CN111881355B (en) | Object recommendation method and device, storage medium and processor |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20201225 |