CN111127149B - Bulk commodity supply information recommendation method and device and computer equipment - Google Patents
Bulk commodity supply information recommendation method and device and computer equipment Download PDFInfo
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Abstract
The invention relates to a method, a device, a computer device and a storage medium for recommending bulk commodity supply information, which are based on the characteristics that unit price and quantity are mainly considered in bulk commodity transaction, a bulk commodity supply information set containing supply quantity and unit price and a user historical transaction record are obtained according to a bulk commodity supply information recommendation request of a demand party, then the user historical transaction record is analyzed to obtain a predicted value of the demand quantity of bulk commodities, whether the user transaction preference quantity is prior or price is prior is identified, and then the supply information meeting the demand party requirement is pertinently recommended to the demand party from the bulk commodity supply information set containing the supply quantity and the unit price according to the bulk commodity demand predicted value and the identified user transaction preference, so that transaction obstacles and transaction risks are reduced, and the transaction success rate is improved.
Description
Technical Field
The present application relates to the field of data processing technologies, and in particular, to a method and an apparatus for recommending supply information of a large commodity, a computer device, and a storage medium.
Background
With the continuous progress of science and technology, the commodity transaction by human-computer online interaction is more and more popular.
The trade mode of the bulk goods is gradually changed from off-line trade to on-line electronic trade. The electronic transaction of bulk commodities adopts a transaction mode of synchronous centralized bidding, unified matching, unified settlement and real-time display of price quotation in the same-commodity different places of a computer network organization, and brings convenience to the life of people.
However, based on the multi-layer and multi-element characteristics of the bulk commodity transaction objects, the transaction matching only based on the characteristics of the commodities cannot meet various requirements of the buyers. Moreover, the achievement of the bulk commodity transaction is limited by factors such as market behavior normative, reputation, transaction risk and the like, so that the bulk commodity transaction is more and more difficult to be promoted from the demands of purchasing parties and commodity information.
Accordingly, there is a need to provide a recommendation scheme for bulk supply information that can quickly facilitate the fulfillment of a bulk transaction.
Disclosure of Invention
In view of the above, it is necessary to provide a method, an apparatus, a computer device and a storage medium for recommending bulk commodity supply information, which can rapidly facilitate the achievement of a bulk commodity transaction, in order to solve the problem that the achievement of a bulk commodity transaction is more and more difficult.
A bulk commodity supply information recommendation method comprises the following steps:
obtaining a massive commodity supply information recommendation request, wherein the massive commodity supply information recommendation request carries target massive commodity identification information and user identification information;
respectively acquiring a bulk commodity supply information set and a user historical transaction record in a preset transaction platform database according to the target bulk commodity identification information and the user identification information, wherein the bulk commodity supply information set comprises bulk commodity supply quantity and bulk commodity unit price;
analyzing the historical transaction records of the users to obtain a predicted value of the demand quantity of the bulk commodities and transaction preferences of the users, wherein the transaction preferences of the users comprise transaction preferences with a prior quantity and transaction preferences with a prior unit price;
and generating and pushing a target bulk commodity supply set according to the predicted value of the demand quantity of the bulk commodity and the transaction preference.
In one embodiment, generating and pushing the target block commodity supply set according to the block commodity demand quantity predicted value and the transaction preference comprises:
when the user transaction preference is the transaction preference with the number priority, screening an initial bulk commodity supply information set of which the difference value between the bulk commodity supply number and the bulk commodity demand predicted value meets a first preset range, sequencing the initial bulk commodity supply information set according to the unit price of a bulk commodity, and generating and pushing a first target bulk commodity supply set;
and when the user transaction preference is a price-first transaction preference, sequencing the bulk commodity supply information sets according to the unit price of the bulk commodities to obtain an initial bulk commodity supply set, screening out a bulk commodity supply set, wherein the difference between the predicted value of the demand quantity of the bulk commodities and the quantity of the bulk commodities meets a second preset range, and generating and pushing a second target bulk commodity supply set.
In one embodiment, analyzing the historical transaction records of the user to obtain the forecast value of the demand of the bulk commodity comprises:
extracting transaction data of purchasing a target bulk commodity all the time in a user historical transaction record;
acquiring the average transaction quantity of the target bulk commodities based on the transaction data of the target bulk commodities purchased all the time, and extracting the transaction quantity of the target bulk commodities in a preset time period;
and analyzing the average transaction quantity and the transaction quantity of the target bulk commodity in a preset time period by adopting a weighted moving average method to obtain a predicted value of the demand quantity of the bulk commodity.
In one embodiment, analyzing the user's historical transaction records to obtain the user's transaction preferences comprises:
extracting bulk commodity information in the user historical transaction record, wherein the bulk commodity information comprises a traded target bulk commodity identifier and a corresponding bulk commodity trading unit price;
acquiring a corresponding market average price on a third-party data platform according to the traded target bulk commodity identification;
if the market average price of the traded bulk commodities exceeding the preset proportion is lower than the trading unit price of the bulk commodities, determining that the trading preference of the user is the trading preference with the prior quantity;
and if the market average price of the transaction bulk commodity exceeding the preset proportion is higher than the transaction unit price of the bulk commodity, determining that the transaction preference of the user is the transaction preference with the prior price.
In one embodiment, the method further comprises the following steps:
if the historical transaction record is not obtained according to the user identification information, obtaining attribute data of the stock user and obtaining attribute data of the target user according to the user identification information;
calculating the similarity between the target user and the stock user based on the attribute data of the stock user and the attribute data of the target user to obtain a similar stock user with the highest similarity with the target user;
acquiring historical transaction records of users with similar stock;
and analyzing historical transaction records of users with similar stock quantities to obtain a large commodity demand quantity predicted value and user transaction preference.
In one embodiment, calculating the similarity between the target user and the inventory user based on the attribute data of the inventory user and the attribute data of the target user, and obtaining a similar inventory user with the highest similarity to the target user comprises:
respectively constructing an inventory user attribute vector and a target user attribute vector based on the attribute data of the inventory user and the attribute data of the target user;
obtaining an inventory user attribute vector and an Euclidean distance of a target user attribute vector by adopting an Euclidean distance algorithm;
and screening out the similar inventory user with the highest similarity to the target user based on the Euclidean distance.
In one embodiment, the target block of merchandise supply set includes supply quality data for a block of merchandise; after obtaining the target bulk product supply set, the method further comprises:
and when the supply quality data of the bulk commodity is judged to be the data authenticated by the third-party authentication mechanism, pushing the target bulk commodity supply set.
A bulk goods supply information recommendation device, the device comprising:
the recommendation request acquisition module is used for acquiring a bulk commodity supply information recommendation request which carries target bulk commodity identification information and user identification information;
the key data acquisition module is used for respectively acquiring a bulk commodity supply information set and a user historical transaction record according to the target bulk commodity identification information and the user identification information, wherein the bulk commodity supply information set comprises bulk commodity supply quantity and bulk commodity unit price;
the data analysis module is used for analyzing the historical transaction records of the user to obtain a predicted value of the demand quantity of the bulk commodity and transaction preferences of the user, wherein the transaction preferences of the user comprise transaction preferences with a first quantity and transaction preferences with a first unit price;
and the data pushing module is used for generating and pushing a target bulk commodity supply set according to the bulk commodity demand predicted value and the transaction preference.
In one embodiment, the data analysis module is further configured to extract transaction data of a target bulk commodity purchased in a user history transaction record in a past time, obtain an average transaction amount of the target bulk commodity based on the transaction data of the target bulk commodity purchased in the past time, extract the transaction amount of the target bulk commodity in a preset time period, and analyze the average transaction amount and the transaction amount of the target bulk commodity in the preset time period by using a weighted moving average method to obtain a predicted value of the demand volume of the bulk commodity.
A computer device comprising a memory and a processor, the memory storing a computer program, the processor implementing the following steps when executing the computer program:
acquiring a massive commodity supply information recommendation request, wherein the massive commodity supply information recommendation request carries target massive commodity identification information and user identification information;
respectively acquiring a bulk commodity supply information set and a user historical transaction record in a preset transaction platform database according to target bulk commodity identification information and user identification information, wherein the bulk commodity supply information set comprises bulk commodity supply quantity and bulk commodity unit price;
analyzing the historical transaction records of the users to obtain a predicted value of the demand quantity of the bulk commodities and transaction preferences of the users, wherein the transaction preferences of the users comprise transaction preferences with a prior quantity and transaction preferences with a prior unit price;
and generating and pushing a target bulk commodity supply set according to the predicted value of the demand quantity of the bulk commodity and the transaction preference.
A computer-readable storage medium, on which a computer program is stored which, when executed by a processor, carries out the steps of:
acquiring a massive commodity supply information recommendation request, wherein the massive commodity supply information recommendation request carries target massive commodity identification information and user identification information;
respectively acquiring a bulk commodity supply information set and a user historical transaction record in a preset transaction platform database according to target bulk commodity identification information and user identification information, wherein the bulk commodity supply information set comprises bulk commodity supply quantity and bulk commodity unit price;
analyzing historical transaction records of the user to obtain a predicted value of the demand quantity of the bulk commodity and transaction preferences of the user, wherein the transaction preferences of the user comprise transaction preferences with a first quantity and transaction preferences with a first unit price;
and generating and pushing a target bulk commodity supply set according to the predicted value of the demand quantity of the bulk commodities and the transaction preference.
According to the method, the device, the computer equipment and the storage medium for recommending the bulk commodity supply information, starting from the characteristic that bulk commodity transaction mainly considers unit price and quantity, according to a bulk commodity supply information recommendation request of a demand party, a bulk commodity supply information set containing supply quantity and unit price and a user historical transaction record are obtained, then the user historical transaction record is analyzed, a bulk commodity demand predicted value is obtained, whether the user transaction preference quantity is prior or price is prior is identified, and further according to the bulk commodity demand predicted value and the identified user transaction preference, supply information meeting the demand party requirement is recommended to a demand party in a targeted mode from the bulk commodity supply information set containing the supply quantity and the unit price, transaction barriers and transaction risks are reduced, and the transaction success rate is improved.
Drawings
FIG. 1 is a diagram of an application environment of a method in a method for recommending bulk commodity supply information in one embodiment;
FIG. 2 is a flow diagram that illustrates a method in the method for recommending block merchandise supply information, according to one embodiment;
FIG. 3 is a schematic flowchart illustrating a method for recommending bulk commodity supply information in another embodiment;
FIG. 4 is a flow chart illustrating the bulk product demand forecasting step in one embodiment;
FIG. 5 is a block diagram of a block product supply information recommendation device in one embodiment;
FIG. 6 is a block diagram showing a block diagram of a block product supply information recommending apparatus according to another embodiment;
FIG. 7 is a diagram of the internal structure of a computer device in one embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more clearly understood, the present application is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of and not restrictive on the broad application.
The recommendation method for the bulk commodity supply information can be applied to the application environment shown in fig. 1. Wherein the terminal 102 communicates with the server 104 via a network. Specifically, the buyer inputs the information of a large commodity to be purchased on a graphical user interface of the terminal 102, sends a large commodity supply information recommendation request to the server 104 through a series of graphical interface operations, the large commodity supply information recommendation request carries target large commodity identification information and user identification information, the server 104 obtains the request, respectively obtains a large commodity supply information set (large commodity supply quantity and large commodity unit price) and a user historical transaction record in a preset transaction platform database according to the target large commodity identification information and the user identification information, analyzes the user historical transaction record to obtain a large commodity demand predicted value and a user transaction preference, and then generates and pushes the target large commodity supply set according to the large commodity demand predicted value and the transaction preference. The terminal 102 may be, but not limited to, various personal computers, notebook computers, smart phones, tablet computers, and portable wearable devices, and the server 104 may be implemented by an independent server or a server cluster formed by a plurality of servers.
In one embodiment, as shown in fig. 2, a method for recommending supply information of bulk goods is provided, which is described by taking the method as an example applied to the server 104 in fig. 1, and includes the following steps:
step S200, a massive commodity supply information recommendation request is obtained, and the massive commodity supply information recommendation request carries target massive commodity identification information and user identification information.
The bulk commodity refers to a material commodity which can enter the circulation field but is not a retail link, has commodity attributes and is used for large-batch buying and selling in industrial and agricultural production and consumption. In the financial investment market, bulk commodities refer to homogeneous, tradeable and widely used as industrial basic raw materials, such as crude oil, nonferrous metals, steel, agricultural products, iron ore, coal and the like. The target bulk product identification information refers to a generic name of various expressions and indications for indicating bulk product information, which may be mainly expressed as data such as a name, a type, a number or a label of a bulk product. The user identification information may include information such as a user name, an identification number, or an account nickname. In this embodiment, the identification information of the target bulk commodity is identification information of a bulk commodity (hereinafter referred to as a target bulk commodity) that the purchasing party wants to purchase at this time.
Step S400, according to the target bulk commodity identification information and the user identification information, a bulk commodity supply information set and a user historical transaction record are respectively obtained from a preset transaction platform database, wherein the bulk commodity supply information set comprises bulk commodity supply quantity and bulk commodity unit price.
The bulk supply information set includes the quantity, quality (which may be a third-party certified quality file), unit price, and total amount of the bulk product that each supplier can supply. The user historical transaction record is historical transaction data of the buyer about the target bulk commodity, and can include data such as transaction time, transaction quantity, transaction unit price, transaction amount and the like. In specific implementation, the preset transaction platform database stores supply information of various bulk commodities and historical transaction data of users, wherein corresponding supply information can be searched in a correlated manner according to the identification information of the target bulk commodity. After the target bulk commodity identification information and the user identification information are obtained, a corresponding bulk commodity supply information set can be obtained in a preset transaction platform database according to the target bulk commodity identification information (serial number), and all historical transaction records of users involved by the users pointed by the user identification information are obtained in the preset transaction platform database according to the user identification information.
Step S600, analyzing the historical transaction records of the user to obtain a predicted value of the demand quantity of the bulk commodity and transaction preferences of the user, wherein the transaction preferences of the user comprise transaction preferences with a first quantity and transaction preferences with a first unit price.
The predicted value of the demand quantity of the bulk commodity refers to the predicted quantity of the target bulk commodity which the user may need to purchase at this time. The user transaction preference is a rational and inclined selection made by the user when considering goods and services, and is a comprehensive result of cognitive, psychological feeling and rational economic balance of the user. In practical application, in order to improve user experience, user transaction preference can be analyzed and further processed in combination with the user preference. Specifically, historical transaction behavior records of the user can be analyzed, the number of target bulk commodities purchased all the time is extracted, a predicted value of the demand quantity of the bulk commodities is predicted, and data such as commodity information purchased by the user, transaction time, transaction unit price and payment mode are analyzed to obtain transaction preference of the user. In this embodiment, the user transaction preferences may be summarized in two types, including price first and quantity first. Price priority means that the transaction time of the user is sufficient, the transaction mainly considers the price factor, and quantity priority means that the transaction time of the user is more urgent, so that the demand quantity of goods is met as the priority factor.
And step S800, generating and pushing a target bulk commodity supply set according to the predicted value of the demand quantity of the bulk commodities and the transaction preference.
After the predicted value of the demand quantity of the bulk commodity and the transaction preference are obtained, the predicted value of the demand quantity of the bulk commodity of the user can be combined according to the transaction preference to which the user belongs specifically, so that the demand of the user is met as a main principle, and the recommendation is carried out in a targeted manner.
According to the method for recommending the bulk commodity supply information, on the basis of the characteristic that bulk commodity transaction mainly considers unit price and quantity, a bulk commodity supply information set containing supply quantity and unit price and a user historical transaction record are obtained according to a bulk commodity supply information recommendation request of a demand party, then the user historical transaction record is analyzed, a bulk commodity demand quantity predicted value is obtained, whether the user transaction preference quantity is prior or price is prior is identified, and further supply information meeting the demand party requirement is recommended to the demand party in a targeted mode from the bulk commodity supply information set containing the supply quantity and the unit price according to the bulk commodity demand quantity predicted value and the identified user transaction preference, so that transaction barriers and transaction risks are reduced, and the transaction success rate is improved.
In one embodiment, as shown in fig. 3, generating and pushing the target block product supply set according to the block product demand forecast value and the transaction preference includes:
step S820, when the user transaction preference is a number-first transaction preference, screening an initial bulk commodity supply information set of which the difference value between the bulk commodity supply quantity and the bulk commodity demand quantity predicted value meets a first preset range, sequencing the initial bulk commodity supply information set according to the unit price of a bulk commodity, and generating and pushing a first target bulk commodity supply set;
and step S840, when the user transaction preference is a price-first transaction preference, sorting the bulk commodity supply information sets according to the unit price of the bulk commodities to obtain an initial bulk commodity supply set, screening out a bulk commodity supply set, wherein the difference between the predicted value of the demand quantity of the bulk commodities and the quantity of the bulk commodities meets a second preset range, and generating and pushing a second target bulk commodity supply set.
As with the above embodiment, when it is determined that the user transaction preference is a volume-first transaction preference, it can be understood that, in general, the transaction time of the user is more urgent to satisfy the demand volume of the goods as a priority factor. For the situation, according to the predicted large commodity demand predicted value, an initial large commodity supply information set with the supply quantity of the target large commodity closest to the large commodity demand predicted value is screened from the large commodity supply information set. In this embodiment, the target bulk commodity supply set is a pre-recommended set including suppliers and relevant data of suppliers. When the user transaction preference is price first, it can be understood that the user generally has sufficient transaction time and does not need to conduct high-price transaction in order to meet the demand of goods, and the price is taken as a main consideration. For the situation, the bulk commodity supply information sets are sorted according to the unit prices of bulk commodities given by various bulk commodity suppliers to obtain an initial bulk commodity supply set, meanwhile, according to the bulk commodity demand quantity predicted value, the bulk commodity supply set with the difference value between the bulk commodity demand quantity predicted value and the bulk commodity quantity meeting the range of +/-20% is screened out from the initial bulk commodity supply set, and a second target bulk commodity supply set is generated and pushed to the user terminal. In this embodiment, the target bulk commodity supply set is pushed according to the user transaction preference, which can facilitate the achievement of the transaction more quickly.
In one embodiment, as shown in fig. 4, analyzing the historical transaction records of the user to obtain the predicted value of the demand of the bulk commodity includes:
step S620, extracting transaction data of purchasing a target bulk commodity all the time in the historical transaction record of the user;
step S640, acquiring the average transaction quantity of the target bulk commodities based on the transaction data of the target bulk commodities purchased all the time, and extracting the transaction quantity of the target bulk commodities in a preset time period;
and step S660, analyzing the average transaction quantity and the transaction quantity of the target bulk commodity in a preset time period by adopting a weighted moving average method to obtain a predicted value of the demand quantity of the bulk commodity.
The weighted moving average method is to give different weights according to the influence degree of data in different time in the same moving segment on the predicted value, and then to carry out average moving to predict the future value. Unlike the simple moving average method, the weighted moving average method treats the data in the moving period equally when calculating the average value, but treats each data in the moving period differently according to the characteristic that the more recent data has a greater influence on the predicted value. More weight is given to recent data and less weight is given to more distant data, thus making up for the shortcomings of the simple moving average method. In this embodiment, the bulk commodity demand predicted value may be transaction data of a target bulk commodity purchased all the time in a user historical transaction record, where the extracted transaction data includes transaction time, transaction quantity, transaction amount and the like, the average transaction quantity of the target bulk commodity is calculated based on the transaction data of the target bulk commodity purchased all the time, and the transaction quantity in the transaction record in the last three months is extracted at the same time, and then, according to the time, the transaction quantities in the last three months are respectively weighted to be 0.2,0.3 and 0.5, and then, according to the weighted weights, weighted calculation is performed to obtain the bulk commodity demand predicted value. It is understood that in other embodiments, the transaction data in the last three transaction records or the transaction amount in the transaction records in the last half year may be extracted, and the prediction algorithm may also be a regression analysis prediction algorithm or other algorithms, which may be determined according to the actual situation and is not limited herein. In the embodiment, when the demand of the bulk commodity is neither rapidly increased nor rapidly decreased, and seasonal factors do not exist, the demand quantity of the bulk commodity is predicted by adopting a weighted moving average method, and random fluctuation in prediction can be effectively eliminated.
In one embodiment, analyzing the user's historical transaction records to obtain the user's transaction preferences comprises: extracting bulk commodity information in a user historical trading record, wherein the bulk commodity information comprises a traded target bulk commodity identifier and a corresponding bulk commodity trading unit price, acquiring a corresponding market average price according to the traded target bulk commodity identifier, determining that the user trading preference is a trading preference with a prior quantity if the market average price of the traded bulk commodity exceeding a preset proportion is lower than the bulk commodity trading unit price, and determining that the user trading preference is a trading preference with a prior price if the market average price of the traded bulk commodity exceeding the preset proportion is higher than the bulk commodity trading unit price.
In specific implementation, the judgment of the user transaction preference may be to extract all purchased large commodity information in the user historical transaction record, specifically, the large commodity information includes a traded target large commodity number and a corresponding large commodity trading unit price, then, according to the traded target large commodity number, obtain a corresponding market average price on a third-party data platform, and if the large commodity number indicates that the commodity is steel, access an international steel product quotation platform by means of interface calling, obtain a corresponding market quotation according to the large commodity number, and in this way, obtain the market average price of all purchased large commodities. Further, the trade preference of the user is judged by comparing the market average price with the trade unit price purchased by the user. According to the characteristics of bulk commodity transaction, if the market average price of more than 60% of the traded bulk commodities is lower than the bulk commodity trading price, the price can be determined not to be the main consideration of the user, the price deviation can be accepted, the trading time of the user is generally more urgent, the quantity of the commodities is satisfied as the main consideration, in sum, the trading preference of the user is determined to be the trading preference with the prior quantity, namely the quantity is prior, if the market average price of more than 60% of the traded bulk commodities is higher than the bulk commodity trading price, the price can be determined to be the main consideration of the trading preference of the user, the large price deviation can not be accepted, the trading time of the user is generally more sufficient, so that sufficient time is provided for selection, in most cases, only if the market average price of the bulk commodities is lower than the market average price, trading can be achieved, and in sum, the trading preference of the user can be determined to be the trading preference with the prior quantity, namely the price is preferred. In the embodiment, based on the characteristics of the bulk commodity transaction, the transaction preference of the user can be conveniently judged by comparing the market average price and the historical transaction unit price of the same bulk commodity.
In one embodiment, the method further comprises the following steps: if the historical transaction record is not obtained according to the user identification information, obtaining attribute data of the stock user, obtaining attribute data of the target user according to the user identification information, calculating the similarity between the target user and the stock user based on the attribute data of the stock user and the attribute data of the target user, obtaining a similar stock user with the highest similarity with the target user, obtaining the historical transaction record of the similar stock user, and analyzing the historical transaction record of the similar stock user to obtain a large commodity demand predicted value and user transaction preference.
In practical applications, users generally include core enterprises, buyers who have a need to purchase a large amount of commodities, and suppliers of large amounts of commodities. If the buyer (target user) is a newly registered user and the transaction record of the user does not exist in the transaction platform database, acquiring attribute data of the inventory user in the preset transaction platform database, acquiring the attribute data of the user according to the user identification information, wherein the attribute data comprises but is not limited to a registration place, a company scale, an operation range, a company demand and the like, then calculating the similarity between the attribute data of the inventory user and the attribute data of the user, finding out the inventory user closest to the company scale, the company type and the company demand of the target user, and then analyzing the historical transaction record of the inventory user to obtain a predicted value of the large commodity demand and user preference. In another embodiment, a first user attribute vector (inventory user) and a second user attribute vector may be respectively constructed based on the attribute data of the inventory user and the attribute data of the target user, and an euclidean distance algorithm is adopted to obtain euclidean distances between the first user attribute vector and the second user attribute vector, where the calculation formula may be:
wherein s is a Is the attribute vector, s, of the target user b The closer the euclidean distance between the attribute vector of the target user and the attribute vector of the inventory user, the higher the similarity, which is the attribute vector of the inventory user. And performing ascending arrangement on the calculated Euclidean distances, and screening out the similar inventory users with the closest distance to the target user, namely the highest similarity. Then theAnd analyzing the screened historical transaction records of the inventory user to obtain a bulk commodity demand quantity predicted value and user preference. In the embodiment, the distance between the target user and the stock user is calculated through the Euclidean distance algorithm, the stock user which is closest to the scale, the operation range, the demand and the like of the target user is further found out, the predicted value of the demand of bulk commodities of the target user and the user transaction preference are obtained through historical transaction record analysis of the stock user, and the user experience degree can be better improved.
In one embodiment, the target commodity supply set comprises supply quality data for a commodity; after obtaining the target bulk product supply set, the method further comprises: and when the supply quality data of the bulk commodity is judged to be the data authenticated by the third-party authentication mechanism, pushing the target bulk commodity supply set.
In practical application, the screened target bulk commodity supply set comprises supply quality data of bulk commodities, in order to ensure the safety of bulk commodity transaction, before the target bulk commodity supply set is pushed to a target user, quality verification can be carried out on the supply quality data of the bulk commodities, and after the quality verification is passed, the target bulk commodity supply set is pushed to the user so that the user can select the target bulk commodity supply set. Specifically, the quality verification may be to check whether the supply quality data carries certification mark data of a third-party certification authority, and if yes, it indicates that the quality of the bulk goods provided by the supplier meets the requirement, and may be recommended. In the embodiment, the transaction risk can be effectively reduced by verifying the quality of the bulk goods provided by the supplier.
It should be understood that although the various steps in the flow diagrams of fig. 2-4 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least some of the steps in fig. 2-4 may include multiple sub-steps or multiple stages that are not necessarily performed at the same time, but may be performed at different times, and the order of performance of the sub-steps or stages is not necessarily sequential, but may be performed in turn or alternating with other steps or at least some of the sub-steps or stages of other steps.
In one embodiment, as shown in fig. 5, there is provided a bulk goods supply information recommending apparatus, including: a recommendation request acquisition module 510, a key data acquisition module 520, a data analysis module 530, and a data push module 540, wherein:
a recommendation request obtaining module 510, configured to obtain a bulk commodity supply information recommendation request, where the bulk commodity supply information recommendation request carries target bulk commodity identification information and user identification information;
a key data obtaining module 520, configured to obtain a bulk commodity supply information set and a user historical transaction record according to the target bulk commodity identification information and the user identification information, where the bulk commodity supply information set includes a bulk commodity supply quantity and a bulk commodity unit price;
the data analysis module 530 is configured to analyze the historical transaction records of the user to obtain a predicted value of the demand volume of the bulk commodity and user transaction preferences, where the user transaction preferences include number-first transaction preferences and unit price-first transaction preferences;
and the data pushing module 540 is configured to generate and push a target bulk commodity supply set according to the predicted value of the demand quantity of the bulk commodity and the transaction preference.
In one embodiment, the data pushing module 540 is further configured to, when the user transaction preference is a volume-first transaction preference, screen out an initial bulk commodity supply information set in which a difference between a bulk commodity supply volume and a bulk commodity demand prediction value meets a first preset range, sort the initial bulk commodity supply information set according to a bulk commodity unit price, and generate and push a first target bulk commodity supply set; and when the user transaction preference is a price-first transaction preference, sequencing the bulk commodity supply information sets according to the unit price of the bulk commodities to obtain an initial bulk commodity supply set, screening out a bulk commodity supply set, wherein the difference between the predicted value of the demand quantity of the bulk commodities and the quantity of the bulk commodities meets a second preset range, and generating and pushing a target bulk commodity supply set.
In one embodiment, the data analysis module 530 is further configured to extract transaction data of previous target bulk goods purchases in the user historical transaction record, obtain an average transaction amount of the target bulk goods based on the transaction data of previous target bulk goods purchases, extract the transaction amount of the target bulk goods within a preset time period, and analyze the average transaction amount and the transaction amount of the target bulk goods within the preset time period by using a weighted moving average method to obtain a predicted value of the demand amount of the bulk goods.
In one embodiment, the data analysis module 530 is further configured to extract bulk commodity information in the user historical transaction record, where the bulk commodity information includes a target bulk commodity identifier that has been traded and a corresponding bulk commodity trading unit price, obtain, according to the target bulk commodity identifier that has been traded, a corresponding market average price on the third-party data platform, determine, if the market average price of the bulk commodity that has been traded that exceeds a preset proportion is lower than the bulk commodity trading unit price, that the user trading preference is a trading preference with a preferred amount, and determine, if the market average price of the bulk commodity that has been traded that exceeds the preset proportion is higher than the bulk commodity trading unit price, that the user trading preference is a trading preference with a preferred price.
In one embodiment, as shown in fig. 6, the apparatus further includes an exception handling module 550, configured to, if a historical transaction record is not obtained according to the user identification information, obtain attribute data of the stock user, obtain attribute data of the target user according to the user identification information, calculate a similarity between the target user and the stock user based on the attribute data of the stock user and the attribute data of the target user, obtain a similar stock user with the highest similarity to the target user, obtain a historical transaction record of the similar stock user, and analyze the historical transaction record of the similar stock user to obtain a large commodity demand predicted value and a user transaction preference.
In one embodiment, the exception handling module 550 is further configured to construct an inventory user attribute vector and a target user attribute vector respectively based on the attribute data of the inventory user and the attribute data of the target user, obtain an euclidean distance between the inventory user attribute vector and the target user attribute vector by using a euclidean distance algorithm, and screen out a similar inventory user with the highest similarity to the target user based on the euclidean distance.
For specific limitations of the apparatus for recommending bulk commodity supply information, reference may be made to the above limitations of the method for recommending bulk commodity supply information, which are not described in detail herein. The modules in the above-mentioned bulk goods supply information recommending apparatus may be implemented in whole or in part by software, hardware, or a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as shown in fig. 7. The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operating system and the computer program to run on the non-volatile storage medium. The database of the computer device is used for storing data such as historical transaction records of users and bulk commodity supply information sets. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a bulk goods supply information recommendation method.
Those skilled in the art will appreciate that the architecture shown in fig. 7 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer device is provided, which includes a memory and a processor, the memory stores a computer program, and the processor implements the steps of the recommendation method for bulk goods supply information when executing the computer program.
In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, and the computer program, when executed by a processor, implements the steps of the above-mentioned bulk goods supply information recommendation method.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above may be implemented by hardware instructions of a computer program, which may be stored in a non-volatile computer-readable storage medium, and when executed, may include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), rambus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, and these are all within the scope of protection of the present application. Therefore, the protection scope of the present patent application shall be subject to the appended claims.
Claims (10)
1. A bulk goods supply information recommendation method, the method comprising:
obtaining a bulk commodity supply information recommendation request, wherein the bulk commodity supply information recommendation request carries target bulk commodity identification information and user identification information;
respectively acquiring a bulk commodity supply information set and a user historical transaction record in a preset transaction platform database according to the target bulk commodity identification information and the user identification information, wherein the bulk commodity supply information set comprises bulk commodity supply quantity and bulk commodity unit price;
analyzing the historical transaction records of the user to obtain a predicted value of the demand quantity of the bulk commodity and transaction preferences of the user, wherein the transaction preferences of the user comprise transaction preferences with a first quantity and transaction preferences with a first unit price;
screening an initial bulk commodity supply information set of which the difference value between the supply quantity of the bulk commodities and the predicted value of the demand quantity of the bulk commodities meets a first preset range when the user transaction preference is a number-first transaction preference, sequencing the initial bulk commodity supply information set according to the unit price of the bulk commodities to generate a first target bulk commodity supply set, and pushing the first target bulk commodity supply set when the supply quality data of the first target bulk commodity supply set is judged to be data authenticated by a third-party organization;
and when the user transaction preference is a transaction preference with unit price priority, sequencing the bulk commodity supply information sets according to the unit price of the bulk commodities to obtain an initial bulk commodity supply set, screening out a bulk commodity supply set of which the difference between the predicted value of the demand quantity of the bulk commodities and the quantity of the bulk commodities meets a second preset range from the initial bulk commodity supply set to generate a second target bulk commodity supply set, and pushing the second target bulk commodity supply set when the supply quality data of the second target bulk commodity supply set is judged to be data authenticated by a third-party organization.
2. The method for recommending block commodity supply information according to claim 1, wherein analyzing said historical transaction records of said user to obtain a predicted value of block commodity demand comprises:
extracting transaction data of purchasing the target bulk commodity all the time in the user historical transaction record;
acquiring the average transaction quantity of the target bulk commodity based on the transaction data of the target bulk commodity purchased in the past, and extracting the transaction quantity of the target bulk commodity in a preset time period;
and analyzing the average transaction quantity and the transaction quantity of the target bulk commodity in the preset time period by adopting a weighted moving average method to obtain a predicted value of the demand quantity of the bulk commodity.
3. The block merchandise supply information recommendation method according to claim 1, wherein analyzing the user historical transaction record to obtain user transaction preferences comprises:
extracting bulk commodity information in the user historical transaction record, wherein the bulk commodity information comprises a transacted target bulk commodity identifier and corresponding bulk commodity transaction unit prices;
acquiring a corresponding market average price on a third-party data platform according to the traded target bulk commodity identification;
if the market average price of the traded bulk commodities exceeding the preset proportion is lower than the trading unit price of the bulk commodities, determining that the trading preference of the user is the trading preference with the prior quantity;
and if the market average price of the transaction bulk commodity exceeding the preset proportion is higher than the transaction unit price of the bulk commodity, determining that the transaction preference of the user is the transaction preference with the prior price.
4. The bulk commodity supply information recommendation method according to claim 1, further comprising:
if the historical transaction record is not obtained according to the user identification information, obtaining attribute data of stock users and obtaining attribute data of a target user according to the user identification information;
calculating the similarity between the target user and the stock user based on the attribute data of the stock user and the attribute data of the target user to obtain a similar stock user with the highest similarity with the target user;
acquiring historical transaction records of the similar stock users;
and analyzing the historical transaction records of the similar stock users to obtain a large commodity demand quantity predicted value and user transaction preference.
5. The method for recommending block commodity supply information according to claim 1, wherein the calculating the similarity between the target user and the inventory user based on the attribute data of the inventory user and the attribute data of the target user, and obtaining a similar inventory user with the highest similarity to the target user comprises:
respectively constructing an inventory user attribute vector and a target user attribute vector based on the attribute data of the inventory user and the attribute data of the target user;
obtaining the Euclidean distance of the user attribute vector of the stock and the target user attribute vector by adopting an Euclidean distance algorithm;
and screening out the similar inventory user with the highest similarity to the target user based on the Euclidean distance.
6. A bulk goods supply information recommendation device, the device comprising:
the recommendation request acquisition module is used for acquiring a bulk commodity supply information recommendation request, wherein the bulk commodity supply information recommendation request carries target bulk commodity identification information and user identification information;
the key data acquisition module is used for respectively acquiring a bulk commodity supply information set and a user historical transaction record according to the target bulk commodity identification information and the user identification information, wherein the bulk commodity supply information set comprises bulk commodity supply quantity and bulk commodity unit price;
the data analysis module is used for analyzing the historical transaction records of the users to obtain a predicted value of the demand quantity of the bulk commodities and user transaction preferences, and the user transaction preferences comprise number-first transaction preferences and unit price-first transaction preferences;
the data pushing module is used for screening an initial bulk commodity supply information set of which the difference value between the bulk commodity supply quantity and the predicted value of the bulk commodity demand quantity meets a first preset range when the user transaction preference is a transaction preference with a preferred quantity, sequencing the initial bulk commodity supply information set according to the unit price of the bulk commodities to generate a first target bulk commodity supply set, and pushing the first target bulk commodity supply set when the supply quality data of the first target bulk commodity supply set is judged to be data authenticated by a third-party organization; and when the user transaction preference is a transaction preference with unit price priority, sequencing the bulk commodity supply information sets according to the unit price of the bulk commodities to obtain an initial bulk commodity supply set, screening out a bulk commodity supply set of which the difference between the predicted value of the demand quantity of the bulk commodities and the quantity of the bulk commodities meets a second preset range from the initial bulk commodity supply set to generate a second target bulk commodity supply set, and pushing the second target bulk commodity supply set when the supply quality data of the second target bulk commodity supply set is judged to be data authenticated by a third-party organization.
7. The device for recommending bulk commodity supply information according to claim 6, wherein the data analysis module is further configured to extract transaction data of the target bulk commodity purchased all the time in the user historical transaction record, obtain an average transaction quantity of the target bulk commodity based on the transaction data of the target bulk commodity purchased all the time, extract the transaction quantity of the target bulk commodity within a preset time period, and analyze the average transaction quantity and the transaction quantity of the target bulk commodity within the preset time period by using a weighted moving average method to obtain a predicted value of the demand quantity of the bulk commodity.
8. The device for recommending bulk commodity supply information according to claim 6, wherein the data analysis module is further configured to extract bulk commodity information in the user historical transaction record, the bulk commodity information includes a traded target bulk commodity identifier and a corresponding bulk commodity trading unit price, a corresponding market average price is obtained on a third-party data platform according to the traded target bulk commodity identifier, if the market average price of the traded bulk commodity exceeding a preset proportion is lower than the bulk commodity trading unit price, the user trading preference is determined as a trading preference with a priority in quantity, and if the market average price of the traded bulk commodity exceeding the preset proportion is higher than the bulk commodity trading unit price, the user trading preference is determined as a trading preference with a priority in price.
9. A computer device comprising a memory and a processor, the memory storing a computer program, wherein the processor when executing the computer program performs the steps of the method according to any of claims 1 to 5.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 5.
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| CN112348553A (en) * | 2020-09-27 | 2021-02-09 | 北京淇瑀信息科技有限公司 | Identity authentication-based business task construction method and device and electronic equipment |
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| CN113409124A (en) * | 2021-07-08 | 2021-09-17 | 山东大学 | Bulk commodity recommendation method and system based on Bayesian regression analysis |
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| CN114066571A (en) * | 2021-11-17 | 2022-02-18 | 中国银行股份有限公司 | Bulk commodity transaction method and device based on big data and 5G messages |
| 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 |
| CN117114686B (en) * | 2023-08-01 | 2024-07-26 | 中资国恒科技有限公司 | Credit supervision method and system based on bulk transaction platform |
| CN117196620A (en) * | 2023-08-18 | 2023-12-08 | 合肥人工智能与大数据研究院有限公司 | Commodity order transaction matching method based on new generation information technology |
| CN117422555B (en) * | 2023-11-22 | 2024-05-28 | 华采科技(北京)有限公司 | Intelligent decision analysis system for large-volume aquatic product transaction based on big data |
| CN117495508B (en) * | 2023-11-23 | 2024-04-30 | 网麒科技(北京)有限责任公司 | Multi-data collaborative purchase screening method, device, equipment and storage medium |
| CN118037394A (en) * | 2024-02-18 | 2024-05-14 | 北京中农亿家资源科技有限公司 | User management method and system of online pork transaction platform |
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