CN117649256A - Ecological product sales information analysis method suitable for karst region - Google Patents
Ecological product sales information analysis method suitable for karst region Download PDFInfo
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Abstract
The invention relates to the technical field of data processing, in particular to a method for analyzing sales information of ecological products in a karst region, which comprises the following steps: acquiring consumption information data of a plurality of clients and information data of a plurality of purchased goods of a store platform; acquiring importance degree of each customer of the shop platform on each purchased commodity; acquiring any two purchased commodities as a preference factor of a sales combination; and obtaining all binding combinations of the shop platform according to the preference factors for sale. The invention can promote the sales of the shop platform while stimulating the customer to buy the product.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a method for analyzing sales information of ecological products in karst regions.
Background
The karst region is a special geological landform type, and the karst landform is taken as a main characteristic, so that the surface hydrologic characteristic is more prominent; in such areas, ecological products may have unique attributes and resource advantages, which may be favored by the market due to their uniqueness and environmental context; the analysis of the sales information of the ecological products is helpful for in-depth understanding of the current development situation and potential of local property industry, providing data support for developing development strategies and improving regional industry competitiveness, and is helpful for defining product positioning, market strategies and promotion directions, so that sales efficiency and competitiveness are improved.
With the popularization of online shopping and the difference of the geographic features of karst regions, a large number of clients are attracted to online shopping, but due to the fact that the purchasing behaviors and preferences of different clients are different, the online commodity of the store platform cannot cause the purchasing desire of the clients at random, and the sales rate of the store platform cannot be improved at late stage.
Disclosure of Invention
In order to solve the problems, the invention provides a method for analyzing sales information of ecological products in karst regions, which comprises the following steps:
acquiring consumption information data of a plurality of clients and information data of a plurality of purchased goods of a store platform; the consumption information data of each customer comprises the total consumption amount, the total consumption times, the number of times of forming a list of a plurality of purchased commodities and the time of each consumption; the information data of each purchased commodity comprises the total browsing times and the amount of the purchased commodity;
acquiring importance degree of each customer on each purchased commodity according to total consumption amount, total consumption times, number of times of forming each purchased commodity, time interval between two adjacent times of consumption, total browsing times of each purchased commodity and number of times of forming each commodity;
acquiring any two purchased goods as the preferred factors of the sales combination according to the importance degree of each customer on each purchased goods and the occurrence times of each purchased goods in all purchased goods of each customer;
and carrying out combined sales on the purchased goods of the shop platform according to the preference factors.
Preferably, the method for obtaining the importance degree of each customer to each purchased commodity according to the total amount of consumption, the total number of consumption, the number of times of forming each purchased commodity, the time interval between two adjacent consumption times, the total number of browsing each purchased commodity and the number of times of forming each purchased commodity comprises the following specific steps:
obtaining the first time according to the total consumption amount, the total consumption times, the single times of each purchased commodity and the time interval of two adjacent consumption times of each customerImportance degree of individual clients to store platform; acquiring +.>Individual customer pair->Customer conversion rate for individual purchased goods; will be->Importance of individual customers to store platform and +.>Individual customer pair->Product of customer conversion of the purchased goods as +.>Individual customer pair->The importance of the individual purchased goods.
Preferably, the method comprises the steps of obtaining the first time according to the total consumption amount of each customer, the total consumption times, the single times of each purchased commodity and the time interval between two adjacent consumption timesThe specific formulas of the importance degree of each customer to the shop platform are as follows:
in the method, in the process of the invention,indicate->Importance degree of individual clients to store platform; />Indicate->The total amount consumed by the individual customers;indicate->Total number of consumption by individual customers; />Indicate->No. 5 of individual clients>Time of the secondary consumption; />Indicate->No. 5 of individual clients>Time of the secondary consumption; />The representation takes absolute value.
Preferably, the method obtains the first item according to the total browsing times and the amount of the purchased goodsIndividual customer pair->The specific formula of the customer conversion rate of each purchased commodity is:
in the method, in the process of the invention,indicate->Individual customer pair->Customer conversion rate for individual purchased goods; />Indicate store platform->A listing amount of each purchased commodity; />Indicate store platform->Total number of browses for individual purchased goods; />Indicate->No. 5 of individual clients>The number of times the goods are purchased; />An exponential function based on a natural constant; />Representing a preset hyper-parameter.
Preferably, the method for obtaining any two purchased goods as the preferred factors of the sales combination according to the importance degree of each customer for each purchased goods and the occurrence times of each purchased goods in all purchased goods of each customer includes the following specific steps:
acquisition of the firstPersonal purchase and->A combined confidence of the individual purchased goods;
acquiring the first item according to the occurrence times of each purchased commodity in all purchased commodities of each customerPersonal purchase and->The likelihood of individual purchased goods as sales combinations;
according to the firstPersonal purchase and->Individual purchasersCombination confidence of products->Purchasing commodity, firstThe possibility of each purchased commodity as a sales combination and the importance degree of each customer for each purchased commodity, the +.>Personal purchase and->The preference degree of the individual purchased goods as the sales combination;
and acquiring all any two purchased commodities as the preference degrees of the sales combination, and recording each preference degree obtained by linearly normalizing all the preference degrees as a preference factor.
Preferably, the acquiring a firstPersonal purchase and->The combined confidence of each purchased commodity comprises the following specific methods:
will be the firstThe order quantity and +.>The single amount of each purchased commodity is input into an association rule mining algorithm to obtain the +.>Personal purchase and->Combined confidence of individual purchased goods.
Preferably, the method comprises the step of purchasing goods according to each purchasing commodity at each customer's premisesWith the number of occurrences in the purchased goods, obtain the firstPersonal purchase and->The possibility of purchasing goods as sales combination comprises the following specific methods:
acquisition of the firstPersonal purchase and->The combined frequency value of the individual purchased goods, < ->Personal purchase and->The calculation method of the possibility of purchasing goods as sales combinations comprises the following steps:
in the method, in the process of the invention,indicate->Personal purchase and->The likelihood of individual purchased goods as sales combinations;indicate->Personal purchase and->A combined frequency value for each purchased commodity; />Representing the total number of all customers of the store platform; />Indicate->The purchasing commodity is at->The number of occurrences in all purchased goods for the individual customer;indicate->The purchasing commodity is at->The number of occurrences in all purchased goods for the individual customer.
Preferably, the acquiring a firstPersonal purchase and->The method for combining frequency values of the purchased goods comprises the following specific steps:
for any one customer of the store platform, the firstPersonal purchase and->When the purchased goods are simultaneously appeared in the purchased goods of the client, the client is marked as a first client, and the number of all the first clients in all clients of the shop platform is used as +.>Personal purchase and->A combined frequency value for each purchased commodity.
Preferably, the method according to the first aspectPersonal purchase and->Combined confidence of individual purchased goods +.>Personal purchase of goods, th->The possibility of each purchased commodity as a sales combination and the importance degree of each customer for each purchased commodity, the +.>Personal purchase and->The specific formula of the preference degree of the individual purchased goods as the sales combination is:
in the method, in the process of the invention,indicate->Personal purchase and->The preference degree of the individual purchased goods as the sales combination;indicate->Individual customer pair->Importance of individual purchased goods; />Indicate->Individual customer pair->Importance of individual purchased goods; />Representing the total number of all customers of the store platform; />Indicate->Item purchase and the firstThe likelihood of individual purchased goods as sales combinations; />Indicate->Personal purchase and->Combined confidence of individual purchased goods.
Preferably, the method for selling the purchased goods of the shop platform in combination according to the preference factor comprises the following specific steps:
presetting a preferred threshold valueFor any two purchased goods of the shop platform, if the preference factor of the two purchased goods as a sales combination is greater than or equal to the preference threshold +.>When the two purchased commodities are recorded as binding combination; and (5) selling all binding combinations of the shop platform on line.
The technical scheme of the invention has the beneficial effects that: according to the importance degree of each customer on each purchased commodity and the occurrence frequency of each purchased commodity in all purchased commodities of each customer, acquiring any two purchased commodities as the preferred factors of the sales combination, and acquiring all binding combinations of a shop platform for sales according to the preferred factors; according to the importance degree of each customer on each purchased commodity, the purchased commodities are combined, the preference degree of the current combination for online sales is analyzed, the purchased commodities are bound and combined for online sales, and the sales amount of the shop platform is increased while the customer is stimulated to purchase the commodity.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart showing the steps of a method for analyzing sales information of ecological products in karst regions.
Detailed Description
In order to further describe the technical means and effects adopted by the invention to achieve the preset aim, the following is a detailed description of specific implementation, structure, characteristics and effects thereof of the method for analyzing sales information of ecological products in karst regions according to the invention, which is provided by combining the accompanying drawings and the preferred embodiment. In the following description, different "one embodiment" or "another embodiment" means that the embodiments are not necessarily the same. Furthermore, the particular features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
The following describes a specific scheme of the method for analyzing sales information of ecological products in karst regions.
Referring to fig. 1, a flowchart of a method for analyzing sales information of ecological products in karst regions according to an embodiment of the present invention is shown, and the method includes the following steps:
step S001: and acquiring consumption information data of a plurality of clients and information data of a plurality of purchased commodities of the store platform.
It should be noted that, in this embodiment, for the e-commerce sales in the karst region, the historical purchasing behavior and preference of the user are analyzed to perform sales combination on part of the products, and different product combinations are recommended to different users, so that the user is stimulated to purchase and want to increase sales of shops; the shop platform can keep purchasing information of purchasing users of shops in order to facilitate the knowledge of shops on sales conditions of shops.
Specifically, the method includes the steps of collecting consumption information data of a plurality of clients of a store platform and information data of a plurality of purchased commodities of the store platform, wherein the specific process is as follows:
and acquiring consumption information data of a plurality of clients and information data of a plurality of purchased commodities of the store platform through sales account information of electronic commerce sales stores in the karst region.
The consumption information data of each customer comprises total consumption amount, total consumption times, the number of times of forming a list of a plurality of purchased commodities and the time of each consumption; the information data of each purchased commodity includes the total number of browses of the purchased commodity and the amount of the purchase order.
So far, the method is used for obtaining the consumption information data of a plurality of clients and the information data of a plurality of purchased goods of the shop platform.
Step S002: the importance level of each customer of the store platform for each purchased commodity is obtained.
It should be noted that, the classification of the clients is one of important preconditions for the enterprises to formulate marketing strategies, so that the merchants can better understand the clients, recommend different products to different clients, and improve the satisfaction and loyalty of the clients while improving sales of the stores; for example, in some successful marketing cases, users are classified into 'members', 'super members', 'star diamond members', and the like according to the accumulated consumption of the users, different products and preferential strengths are recommended to users of different grades, and even some products can be purchased only by users of partial grades, so that the purchasing desire of the users is stimulated, the shopping experience of the users is improved, and the sales of shops is also improved. It follows that the importance of analyzing the purchasing behavior of the user.
When the product recommendation is carried out on the client under the online condition, the user can communicate with the client at present to know the preference of the client, but when the product recommendation is sold on the online condition, the historical purchasing behavior of the client can be analyzed only to realize the recommendation of the preference of the client; in addition, different clients have different purchasing preferences and habits, the higher the loyalty of the current client is, the greater the contribution degree of the current client to sales of shops is, the greater the possibility of recommending the current client by combining and matching the shopping preferences of the current client is, the current client purchasing rate is stimulated, and other clients are attracted to purchase in advance, so that sales of shops is improved.
Therefore, before the shopping preference of the current customer is combined and put on shelf for sale, the importance degree of the current customer needs to be analyzed first, wherein the method for obtaining the importance degree of the current customer is as follows:
it should be noted that, the importance of the customer can be reflected from multiple aspects, and the more the current customer's consumption amount is, the greater the contribution degree to the total sales of the store is; the higher the current customer's consumption frequency, the higher the loyalty to the store; the shorter the current customer's consumption time interval, the higher the acceptance to the store; the importance of the current clients is also related to the conversion rate of the shop platform, the client groups consumed by different shop platforms are different, and the significance of the corresponding client conversion rate is different; the client conversion rate specifically refers to the relation between sales volume and client browsing volume of a shop platform, and when the sales volume and the client browsing volume of the shop platform are in direct proportion, the client importance degree of the current shop platform is more credible.
Specifically, the importance degree of each customer of the shop platform on each purchased commodity is obtained according to the total consumption amount, the total consumption times, the number of times of forming each purchased commodity and the time interval between two adjacent times of consumption, and the total browsing times and the number of times of forming each purchased commodity.
As an example, obtain store platformIndividual customer pair->The method for calculating the importance degree of each purchased commodity comprises the following steps:
in the method, in the process of the invention,indicate store platform->Individual customer pair->Importance of individual purchased goods; />Indicate->The total amount consumed by the individual customers; />Indicate->Total number of consumption by individual customers; />Indicate->No. 5 of individual clients>Time of the secondary consumption; />Indicate->No. 5 of individual clients>Time of the secondary consumption; />Indicate store platform->A listing amount of each purchased commodity; />Indicate store platform->Total number of browses for individual purchased goods; />Indicate->No. 5 of individual clients>Individual purchasersThe number of times of product forming; />The representation takes absolute value; />Representing an exponential function based on natural constants, embodiments employModel to present inverse proportional relationship and normalization process, < ->For inputting the model, an implementer can select an inverse proportion function and a normalization function according to actual conditions; />Representing a preset hyper-parameter, preset in this implementation>For preventing denominator from being 0.
It should be noted that the number of the substrates,indicate->The importance of the individual customer to the store platform, the greater this value, the description +.>The shopping frequency of the individual clients on the shop platform is high, and the number of times is high; />Indicate->Individual customer pair->The greater the customer conversion of the individual purchased goods, the more +.>Individual customer purchase->The greater the degree of the shopping commodity of the whole shop platform, the more the individual shopping commodity is, the more the first>The greater the customer conversion for each purchased commodity.
So far, the importance degree of each customer of the shop platform to each purchased commodity is obtained through the method.
Step S003: any two purchased goods are acquired as the preference factors for the sales combination.
It should be noted that, the buying habits of each customer are different from each customer, the importance degree is also different, whether the buying habits of the current customers are selected to be combined and put on the shelf for sale is required to be further analyzed; whether the current customer's purchase preference can be sold on shelf as a combination is also related to the acceptance of the combination by the rest of the customers of the store platform, serving important customers while not serving important customers only, and further increasing the sales of the store platform by considering the rest of the customers and attracting some potential customers.
Therefore, before analyzing whether the current customer's buying habit can make the combined on-shelf sales, consideration needs to be made according to the importance level of the current customer, and the more important the current customer is, the greater the possibility that the current customer can make the combined on-shelf sales. And obtaining the final possibility of putting the combination on shelf for sale according to the acceptability of the buying habit of the current customer.
Specifically, according to the number of occurrences of each purchased article in all purchased articles of each customer, the probability of any two purchased articles being a sales combination is obtained.
For any one customer of the store platform, the firstPersonal purchase and->When the purchased goods are simultaneously appeared in the purchased goods of the client, the client is marked as a first client, and the number of all the first clients in all clients of the shop platform is used as +.>Personal purchase and->A combined frequency value for each purchased commodity.
As an example, obtain the firstPersonal purchase and->The calculation method of the possibility of purchasing goods as sales combinations comprises the following steps:
in the method, in the process of the invention,indicate->Personal purchase and->The likelihood of individual purchased goods as sales combinations;indicate->Personal purchase and->A combined frequency value for each purchased commodity; />Representing the total number of all customers of the store platform; />Indicate->The purchasing commodity is at->The number of occurrences in all purchased goods for the individual customer;indicate->The purchasing commodity is at->The number of occurrences in all purchased goods for the individual customer.
Further, the first step isThe order quantity and +.>The single amount of each purchased commodity is input into an association rule mining algorithm to obtain the +.>Personal purchase and->A combined confidence of the individual purchased goods; and acquiring any two purchased commodities as a preference factor of a sales combination according to the importance degree of each customer on each purchased commodity and the occurrence times of each purchased commodity in all purchased commodities of each customer.
As an example, obtain the firstPersonal purchase and->The calculation method of the preference degree of the individual purchased goods as the sales combination comprises the following steps:
in the method, in the process of the invention,indicate->Personal purchase and->The preference degree of the individual purchased goods as the sales combination;indicate->Individual customer pair->Importance of individual purchased goods; />Indicate->Individual customer pair->Importance of individual purchased goods; />Representing the total number of all customers of the store platform; />Indicate->Item purchase and the firstThe likelihood of individual purchased goods as sales combinations; />Indicate->Personal purchase and->Combined confidence of individual purchased goods.
And acquiring all any two purchased commodities as the preference degrees of the sales combination, and recording each preference degree obtained by linearly normalizing all the preference degrees as a preference factor.
To this end, any two purchased goods are obtained as the preference factors for the sales combination by the above-described method.
Step S004: and obtaining all binding combinations of the shop platform according to the preference factors for sale.
Presetting a preferred threshold valueWherein the present embodiment is +.>To describe the example, the present embodiment is not particularly limited, wherein +.>Depending on the particular implementation.
Specifically, for any two purchased goods of the shop platform, if the preference factor of the two purchased goods as the sales combination is greater than or equal to the preference thresholdWhen the two purchased commodities are recorded as binding combination; and (5) selling all binding combinations of the shop platform on line.
This embodiment is completed.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the invention, but any modifications, equivalent substitutions, improvements, etc. within the principles of the present invention should be included in the scope of the present invention.
Claims (9)
1. The method for analyzing the sales information of the ecological product in the karst region is characterized by comprising the following steps of:
acquiring consumption information data of a plurality of clients and information data of a plurality of purchased goods of a store platform; the consumption information data of each customer comprises the total consumption amount, the total consumption times, the number of times of forming a list of a plurality of purchased commodities and the time of each consumption; the information data of each purchased commodity comprises the total browsing times and the amount of the purchased commodity;
acquiring importance degree of each customer on each purchased commodity according to total consumption amount, total consumption times, number of times of forming a single for each purchased commodity and time interval between two adjacent times of consumption, and total browsing times and number of times of forming a single for each purchased commodity;
acquiring any two purchased goods as the preferred factors of the sales combination according to the importance degree of each customer on each purchased goods and the occurrence times of each purchased goods in all purchased goods of each customer;
acquiring all binding combinations of the shop platform according to the preference factors for sales;
according to the importance degree of each customer on each purchased commodity and the occurrence frequency of each purchased commodity in all purchased commodities of each customer, any two purchased commodities are obtained as the preferred factors of the sales combination, and the specific method comprises the following steps:
acquisition of the firstPersonal purchase and->A combined confidence of the individual purchased goods;
acquisition of the firstPersonal purchase and->The likelihood of individual purchased goods as sales combinations;
according to the firstPersonal purchase and->Combined confidence of individual purchased goods +.>Item purchase and the firstPossibility of purchasing goods as sales combination, obtain +.>Personal purchase and->The preference degree of the individual purchased goods as the sales combination;
and acquiring all any two purchased commodities as the preference degrees of the sales combination, and recording each preference degree obtained by linearly normalizing all the preference degrees as a preference factor.
2. The method for analyzing sales information of ecological products in karst regions according to claim 1, wherein the method for obtaining importance of each customer to each purchased commodity according to total amount of consumption, total number of consumption, number of times of single purchase and time interval between two adjacent consumption, and total number of browsing and number of single purchase of each purchased commodity comprises the following specific steps:
obtaining the first time according to the total consumption amount, the total consumption times and the time interval between two adjacent consumption times of each customerImportance degree of individual clients to store platform; acquiring the +.>Individual customer pair->Customer conversion rate for individual purchased goods; will be->Importance of individual customers to store platform and +.>Individual customer pair->Product of customer conversion of the purchased goods as +.>Individual customer pair numberThe importance of the individual purchased goods.
3. An ecological product sales letter applicable to karst region as claimed in claim 2The information analysis method is characterized in that the first step is obtainedThe specific formulas of the importance degree of each customer to the shop platform are as follows:
；
in the method, in the process of the invention,indicate->Importance degree of individual clients to store platform; />Indicate->The total amount consumed by the individual customers;indicate->Total number of consumption by individual customers; />Indicate->No. 5 of individual clients>Time of the secondary consumption; />Indicate->No. 5 of individual clients>Time of the secondary consumption; />The representation takes absolute value.
4. The method for analyzing sales information of ecological products in karst regions according to claim 2, wherein the first step is obtainedIndividual customer pair->The specific formula of the customer conversion rate of each purchased commodity is:
;
in the method, in the process of the invention,indicate->Individual customer pair->Customer conversion rate for individual purchased goods; />Representing store platformA listing amount of each purchased commodity; />Indicate store platform->Total number of browses for individual purchased goods; />Represent the firstNo. 5 of individual clients>The number of times the goods are purchased; />An exponential function based on a natural constant is represented.
5. The method for analyzing sales information of ecological products in karst regions according to claim 1, wherein the first step is obtainedPersonal purchase and->The combined confidence of each purchased commodity comprises the following specific methods:
will be the firstThe number of occurrences and +.>The number of occurrences of the individual purchased goods in all purchased goods of all customers is input into the association rule mining algorithm to obtain +.>Personal purchase and->Combined confidence of individual purchased goods.
6. The method for analyzing sales information of ecological products in karst regions according to claim 1, wherein the first step is obtainedPersonal purchase and->The possibility of purchasing goods as sales combination comprises the following specific methods:
acquisition of the firstPersonal purchase and->The combined frequency value of the individual purchased goods, < ->Personal purchase and->The calculation method of the possibility of purchasing goods as sales combinations comprises the following steps:
；
in the method, in the process of the invention,indicate->Personal purchase and->The likelihood of individual purchased goods as sales combinations;indicate->Personal purchase and->A combined frequency value for each purchased commodity; />Representing the total number of all customers of the store platform; />Indicate->The purchasing commodity is at->The number of occurrences in all purchased goods for the individual customer; />Indicate->The purchasing commodity is at->The number of occurrences in all purchased goods for the individual customer.
7. The method for analyzing sales information of ecological products in karst regions according to claim 6, wherein the first step is obtainedPersonal purchase and->The method for combining frequency values of the purchased goods comprises the following specific steps:
for any one customer of the store platform, the firstPersonal purchase and->When the purchased goods are simultaneously appeared in the purchased goods of the client, the client is marked as a first client, and the number of all the first clients in all clients of the shop platform is used as +.>Personal purchase and->A combined frequency value for each purchased commodity.
8. A method for analysis of sales information of ecological products in karst regions according to claim 1, wherein the method is characterized in thatPersonal purchase and->Combined confidence of individual purchased goods +.>Personal purchase and->Individual purchased goods as sales combinationsPossibility of obtaining->Personal purchase and->The specific formula of the preference degree of the individual purchased goods as the sales combination is:
;
in the method, in the process of the invention,indicate->Personal purchase and->The preference degree of the individual purchased goods as the sales combination;indicate->Individual customer pair->Importance of individual purchased goods; />Indicate->Individual customer pair->Importance of individual purchased goods; />Representing the total number of all customers of the store platform; />Indicate->Personal purchase and->The likelihood of individual purchased goods as sales combinations; />Indicate->Personal purchase and->Combined confidence of individual purchased goods.
9. The method for analyzing sales information of ecological products in karst regions according to claim 1, wherein the method for obtaining all binding combinations of shop platforms for sales according to the preference factor comprises the following specific steps:
presetting a preferred threshold valueFor any two purchased goods of the shop platform, if the preference factor of the two purchased goods as a sales combination is greater than or equal to the preference threshold +.>When the two purchased commodities are recorded as binding combination; and (5) selling all binding combinations of the shop platform on line.
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|---|---|---|---|---|
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Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140136284A1 (en) * | 2003-08-25 | 2014-05-15 | Tom Reynolds | Determining Strategies for Increasing Loyalty of a Population to an Entity |
| CN110111179A (en) * | 2019-04-28 | 2019-08-09 | 广东技术师范大学 | Recommended method, device and the computer readable storage medium of drug combination |
| CN110675207A (en) * | 2018-07-03 | 2020-01-10 | 阿里巴巴集团控股有限公司 | Image display combination recommendation method, device and equipment |
| CN112116370A (en) * | 2019-06-20 | 2020-12-22 | 上海锐赏文化传播有限公司 | Method and device for determining value attribute of object, storage medium and processor |
| CN113538012A (en) * | 2021-07-26 | 2021-10-22 | 武汉东临碣石电子商务有限公司 | Buyer user intelligent management method based on internet e-commerce platform data analysis |
| CN115578163A (en) * | 2022-10-18 | 2023-01-06 | 李倩文 | Personalized pushing method and system for combined commodity information |
| CN116485503A (en) * | 2023-05-26 | 2023-07-25 | 广州商研网络科技有限公司 | Commodity combination recommendation method, device, equipment and medium thereof |
| CN116562928A (en) * | 2022-01-25 | 2023-08-08 | 北京沃东天骏信息技术有限公司 | Target prediction method, device, equipment and storage medium |
| CN117391820A (en) * | 2023-12-01 | 2024-01-12 | 深圳市思迅网络科技有限公司 | SaaS service comprehensive management method and system |
-
2024
- 2024-01-29 CN CN202410114772.1A patent/CN117649256B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140136284A1 (en) * | 2003-08-25 | 2014-05-15 | Tom Reynolds | Determining Strategies for Increasing Loyalty of a Population to an Entity |
| CN110675207A (en) * | 2018-07-03 | 2020-01-10 | 阿里巴巴集团控股有限公司 | Image display combination recommendation method, device and equipment |
| CN110111179A (en) * | 2019-04-28 | 2019-08-09 | 广东技术师范大学 | Recommended method, device and the computer readable storage medium of drug combination |
| CN112116370A (en) * | 2019-06-20 | 2020-12-22 | 上海锐赏文化传播有限公司 | Method and device for determining value attribute of object, storage medium and processor |
| CN113538012A (en) * | 2021-07-26 | 2021-10-22 | 武汉东临碣石电子商务有限公司 | Buyer user intelligent management method based on internet e-commerce platform data analysis |
| CN116562928A (en) * | 2022-01-25 | 2023-08-08 | 北京沃东天骏信息技术有限公司 | Target prediction method, device, equipment and storage medium |
| CN115578163A (en) * | 2022-10-18 | 2023-01-06 | 李倩文 | Personalized pushing method and system for combined commodity information |
| CN116485503A (en) * | 2023-05-26 | 2023-07-25 | 广州商研网络科技有限公司 | Commodity combination recommendation method, device, equipment and medium thereof |
| CN117391820A (en) * | 2023-12-01 | 2024-01-12 | 深圳市思迅网络科技有限公司 | SaaS service comprehensive management method and system |
Non-Patent Citations (3)
| Title |
|---|
| MORAN BELADEV 等: "Recommender systems for product bundling", 《CEUR WORKSHOP PROCEEDINGS》, 31 December 2016 (2016-12-31), pages 1 - 2 * |
| 郑秋鹛 等: "社区O2O模式下生鲜农产品零售店商品捆绑组合销售决策研", 《农业经济与管理》, no. 6, 15 December 2022 (2022-12-15), pages 88 - 100 * |
| 齐翊杉: "基于数据挖掘的某电商商品销售研究", 《中国优秀硕士学位论文全文数据库 基础科学辑》, no. 2, 15 February 2021 (2021-02-15), pages 002 - 1236 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117994009A (en) * | 2024-04-07 | 2024-05-07 | 快充网科技深圳有限公司 | Digital product removes and fills a supplement system of selecting |
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