CN112053213A - Commodity recommendation method, system and related device - Google Patents
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Abstract
The application provides a commodity recommendation method, which comprises the following steps: acquiring the purchase quantity of a target commodity of a target user in a past preset time period and the actual sales quantity of the target commodity in the preset time period; determining the purchasing permeability of the target user according to the purchasing quantity; determining the predicted purchasing amount of the target commodity according to the purchasing permeability; determining the suggested purchase quantity of the target commodity according to the actual sales quantity, the predicted purchase quantity and the temperature coefficient; and taking the target commodity of which the suggested purchase amount is larger than a preset threshold value as a recommended commodity. The method and the device comprehensively consider the influence of the sales rate, the purchase quantity and the temperature of the target commodity on the sales volume, and determine the recommended commodity based on the suggested purchase quantity, so that the sales volume of the recommended commodity is improved. The application also provides a commodity recommendation system, a computer readable storage medium and an e-commerce management terminal, which have the beneficial effects.
Description
Technical Field
The present application relates to the field of e-commerce technologies, and in particular, to a method, a system, and a related device for recommending a commodity.
Background
In the shopping process, recommended commodities usually exist, however, in the traditional method, a salesperson can recommend commodities according to the purchase record of a shop owner, for example, black tea from a Master kang is purchased 3 months ago, so that a certain recommendation success rate can be increased, but the effect is still very limited. In addition, many commodities are greatly influenced by seasons or temperatures, so that the currently recommended commodities are not accurately recommended, the commodity recommendation effect is good and bad, and the sale of the commodities is influenced.
Disclosure of Invention
The commodity recommendation method, the commodity recommendation system, the computer readable storage medium and the e-commerce management terminal can accurately determine recommended commodities.
In order to solve the technical problem, the application provides a commodity recommendation method, which has the following specific technical scheme:
acquiring the purchase quantity of a target commodity of a target user in a past preset time period and the actual sales quantity of the target commodity in the preset time period;
determining the purchasing permeability of the target user according to the purchasing quantity;
determining the predicted purchasing amount of the target commodity according to the purchasing permeability;
determining the suggested purchase quantity of the target commodity according to the actual sales quantity, the predicted purchase quantity and the temperature coefficient;
and taking the target commodity of which the suggested purchase amount is larger than a preset threshold value as a recommended commodity.
Optionally, the method further includes:
establishing a mapping relation table of the temperature and the temperature coefficient corresponding to the target commodity;
before determining the proposed purchase amount of the target commodity according to the actual sales amount, the predicted purchase amount and the temperature coefficient, the method further comprises the following steps:
and determining the temperature coefficient according to the current temperature and the mapping relation table.
Optionally, determining the purchase permeability of the target user according to the purchase quantity includes:
determining a user rating of the target user;
determining the purchase penetration rate according to the user rating of the target user;
the purchasing permeability is the ratio of the target commodities purchased by the target user from the target platform to the total quantity of the target commodities purchased in all purchasing channels within the past preset time period.
Optionally, determining the proposed purchase amount of the target commodity according to the actual sales amount, the predicted purchase amount and the temperature coefficient includes:
determining the suggested purchase quantity of the target commodity according to the actual sales quantity, the predicted purchase quantity, the temperature coefficient and the suggested purchase quantity model;
the proposed purchase quantity model is R ═ S.C- (P-S);
wherein, R is the suggested purchase quantity, S is the actual sales quantity of the target commodity in the preset time period, C is the temperature coefficient, and P is the predicted purchase quantity.
Optionally, if the recommended commodity has a limited number, before the target commodity of which the recommended purchase amount is greater than the preset threshold is taken as the recommended commodity, the method further includes:
and determining the preset threshold according to the limit quantity.
Optionally, after the target commodity of which the suggested purchase amount is greater than the preset threshold is taken as the recommended commodity, the method further includes:
and pushing the recommended commodity to a platform user corresponding to the target platform.
The present application further provides a merchandise recommendation system, including:
the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring the purchase quantity of a target user on a target commodity in a past preset time period and the actual sale quantity of the target commodity in the preset time period;
the second acquisition module is used for determining the purchasing permeability of the target user according to the purchasing quantity;
the prediction module is used for determining the predicted purchasing amount of the target commodity according to the purchasing permeability;
the suggestion module is used for determining the suggested purchase amount of the target commodity according to the actual sales amount, the predicted purchase amount and the temperature coefficient;
and the screening module is used for taking the target commodity of which the suggested purchase amount is greater than a preset threshold value as a recommended commodity.
Optionally, the method further includes:
the temperature mapping module is used for establishing a mapping relation table of the temperature and the temperature coefficient corresponding to the target commodity;
and the temperature coefficient determining module is used for determining the temperature coefficient according to the current temperature and the mapping relation table.
The present application also provides a computer-readable storage medium having stored thereon a computer program which, when being executed by a processor, carries out the steps of the method as set forth above.
The application also provides an e-commerce management terminal, which comprises a memory and a processor, wherein the memory stores a computer program, and the processor calls the computer program in the memory to realize the steps of the method.
The application provides a commodity recommendation method, which comprises the following steps: acquiring the purchase quantity of a target commodity of a target user in a past preset time period and the actual sales quantity of the target commodity in the preset time period; determining the purchasing permeability of the target user according to the purchasing quantity; determining the predicted purchasing amount of the target commodity according to the purchasing permeability; determining the suggested purchase quantity of the target commodity according to the actual sales quantity, the predicted purchase quantity and the temperature coefficient; and taking the target commodity of which the suggested purchase amount is larger than a preset threshold value as a recommended commodity.
According to the method and the device, the purchase quantity of the target commodities of the target user in a period of time in the past and the actual sales quantity of the target commodities are obtained, the purchase quantity of the target commodities is predicted, the recommended purchase quantity of the target commodities is obtained by combining the predicted purchase quantity and the temperature, the influences of the sales rate, the purchase quantity and the temperature of the target commodities on the sales quantity are comprehensively considered, compared with the method and the device for recommending single commodities according to historical purchase records, the recommendation success rate is high, the recommended commodities can be used as popular commodities for publicity and sale on the basis, and the improvement of the sales quantity of the recommended commodities is facilitated.
The application also provides a commodity recommendation system, a computer readable storage medium and an e-commerce management terminal, which have the beneficial effects and are not repeated herein.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, it is obvious that the drawings in the following description are only embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
Fig. 1 is a flowchart of a commodity recommendation method according to an embodiment of the present application;
fig. 2 is a schematic structural diagram of a product recommendation system according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
Referring to fig. 1, fig. 1 is a flowchart of a commodity recommendation method according to an embodiment of the present application, where the method includes:
s101: acquiring the purchase quantity of a target commodity of a target user in a past preset time period and the actual sales quantity of the target commodity in the preset time period;
this step is intended to acquire the purchase quantity of the target commodity by the target user. Specifically, the step is mainly directed to the target platform, that is, the target user on the target platform purchases the target commodity. The target platform can be any electric commerce platform or online shopping platform, and the like, and simultaneously determines the actual sales quantity of the commodity in a preset time period. The preset time period is not particularly limited, and may be set by those skilled in the art according to the unit required for big data analysis. For example, if the purchase and sale of the target commodity are statistically analyzed within the past month, the preset time period is one month. Of course, the predetermined time period may be other time periods, such as a week, a quarter, etc.
In addition, the target product is not necessarily only one product, and there may be a plurality of recommended products screened in the embodiment of the present application, and naturally, the target product in this step may be a plurality of products. The goods concerned by the user can be the target goods in the step.
For the e-commerce platform or the online shopping platform, the purchasing and selling processes of each target commodity are recorded in the server, so that the step can directly acquire the purchasing quantity and the actual selling quantity of the target commodity from the background server.
S102: determining the purchasing permeability of the target user according to the purchasing quantity;
this step is directed to determining a predicted purchase amount for the target commodity based on the purchase permeability. The purchase penetration rate is the ratio of the target commodities purchased by the target user from the target platform to the total quantity of the target commodities purchased in all the purchase channels within the past preset time period, in other words, the ratio of the target commodities purchased from the platform to all the purchased target commodities is determined. For an e-commerce platform or an online shopping platform, a corresponding user level is generally established for a user, the user level is related to the purchase amount or the purchase amount of the user on the platform, namely, the larger the purchase amount or the purchase amount is, the higher the user level is. It is readily understood that there is a corresponding purchase penetration for each target commodity for each user level. Namely, the grade of the user is different, the target commodity is the same, and the purchasing permeability of the target commodity is possibly different. And the purchasing penetration rate of the target commodities with the same user grade and different target commodities can also be different.
A preferred way of performing this step may therefore comprise the following two steps:
s1021: determining a user grade of a target user;
s1022: determining a purchase permeability according to the user grade of the target user;
the user grade of the target user is recorded in the server of the platform, the user grade of the target user can be obtained from the background server, and then the purchasing permeability of the target user is determined according to the user grade. The higher the user rating, the higher the permeability. For example, the permeability of the tea beverage in the small shop with the user rating of V2 can reach 6%, that is, 6 beverages are purchased at the target platform per 100 beverages, the permeability of the small shop with the user rating of V3 reaches 10%, and 10 beverages are purchased at the target platform per 100 beverages.
Of course, the purchase permeability can also be obtained in other manners, that is, the purchase quantity and the total purchase quantity of the target user on the platform are respectively obtained, and the purchase permeability is obtained through calculation.
S103: determining the predicted purchasing amount of the target commodity according to the purchasing permeability;
after the purchase permeability is determined in step S102, the predicted purchase amount of the target product is determined according to the purchase permeability, if the purchase amount of the target product by the target user in the past preset time period is M, the target product permeability corresponding to the user level is O, and then the predicted purchase amount P of the target product in the present preset time period is calculated as M/O.
In other words, the step is to predict the purchase amount of the target commodity in the preset time period of the time according to the historical purchase amount and the target commodity permeability corresponding to the target user.
S104: determining the suggested purchase quantity of the target commodity according to the actual sales quantity, the predicted purchase quantity and the temperature coefficient;
this step is intended to calculate the proposed purchase amount. The actual sales volume, the predicted purchase volume and the temperature coefficient need to be comprehensively considered.
In the present embodiment, a mapping table of the temperature and the temperature coefficient corresponding to the target product is already established by default before the step is executed, for example, as shown in table 1, table 1 is a schematic table of the temperature and the temperature coefficient C. It is understood that table 1 is only a mapping table of temperature and temperature coefficient C corresponding to a target product provided in this embodiment, and the mapping relationship between the temperature and the temperature coefficient C may be different for different target products, and specifically should be determined according to the influence of the temperature on the target product. For example, it is obvious that ice cream in winter has a lower sales volume than in summer, so that the temperature coefficient in winter is lower and the temperature coefficient in summer is higher.
TABLE 1 mapping relationship table of temperature and temperature coefficient C
And after the mapping relation table exists, determining the temperature coefficient according to the current temperature and the mapping relation table.
There is no specific limitation on how to obtain the suggested purchase amount, and those skilled in the art can build a correlation model of the actual sales amount, the predicted purchase amount, and the temperature coefficient to obtain the suggested sales amount.
As a preferred implementation, the present embodiment herein provides a way to determine the proposed purchase amount of the target item:
determining the suggested purchase quantity of the target commodity according to the actual sales quantity, the predicted purchase quantity, the temperature coefficient and the suggested purchase quantity model;
the proposed purchasing quantity model is R ═ S.C- (P-S);
wherein R is the proposed purchase amount, S is the actual sales amount of the target commodity in the preset time period, C is the temperature coefficient, and P is the predicted purchase amount.
The purchase quantity model is only a preferred model provided in this embodiment, and those skilled in the art can establish other models based on the influence relationship among the actual sales quantity, the predicted purchase quantity, and the temperature coefficient on the basis of this embodiment to obtain the proposed purchase quantity of the target commodity, which all should be within the protection scope of this application.
S105: and taking the target commodity of which the suggested purchase amount is larger than a preset threshold value as a recommended commodity.
After obtaining the proposed purchase amount of the target commodity, it is easy to understand that the larger the proposed purchase amount is, the more popular the target commodity is, i.e. the better the target commodity is sold. Therefore, the target commodity of which the recommended purchase amount is larger than the preset threshold value can be taken as the recommended commodity.
The preset threshold is not particularly limited and may be set by those skilled in the art according to the sales status of the actual goods. Or, if the proposed purchase amounts of the plurality of commodities are calculated at the same time, the preset threshold value should be at least larger than the median of all the proposed purchase amounts.
On the basis of the embodiment, if the recommended commodity has the limited quantity, before the target commodity of which the recommended purchase quantity is greater than the preset threshold value is taken as the recommended commodity, the preset threshold value can be determined according to the limited quantity. For example, if 5 recommended commodities are defined, after the recommended purchase amounts are arranged in descending order, the preset threshold value may be set to be between the fifth recommended purchase amount and the sixth recommended purchase amount.
On the basis of the present embodiment, after step S105 is executed, the recommended product may also be pushed to the platform user corresponding to the target platform, so as to improve the sales volume of the recommended product.
According to the method and the device, the purchase quantity of the target commodities of the target user in a period of time in the past and the actual sales quantity of the target commodities are obtained, the purchase quantity of the target commodities is predicted, the recommended purchase quantity of the target commodities is obtained by combining the predicted purchase quantity and the temperature, the influences of the sales rate, the purchase quantity and the temperature of the target commodities on the sales quantity are comprehensively considered, compared with the method and the device for recommending single commodities according to historical purchase records, the recommendation success rate is high, the recommended commodities can be used as popular commodities for publicity and sale on the basis, and the improvement of the sales quantity of the recommended commodities is facilitated.
In the following, a description is given of a product recommendation system provided in an embodiment of the present application, and a product recommendation system described below and a product recommendation method described above may be referred to correspondingly.
Fig. 2 is a schematic structural diagram of a product recommendation system provided in an embodiment of the present application, and the present application further provides a product recommendation system, including:
a first obtaining module 100, configured to obtain a quantity of purchases of a target commodity by a target user in a past preset time period, and an actual quantity of sales of the target commodity in the preset time period;
a second obtaining module 200, configured to determine a purchase permeability of the target user according to the purchase quantity;
the prediction module 300 is used for determining the predicted purchasing amount of the target commodity according to the purchasing permeability;
a suggesting module 400 for determining a suggested purchase amount of the target commodity according to the actual sales amount, the predicted purchase amount, and a temperature coefficient;
and the screening module 500 is configured to use the target commodity with the recommended purchase amount larger than a preset threshold as a recommended commodity.
Based on the above embodiment, as a preferred embodiment, the method may further include:
the temperature mapping module is used for establishing a mapping relation table of the temperature and the temperature coefficient corresponding to the target commodity;
and the temperature coefficient determining module is used for determining the temperature coefficient according to the current temperature and the mapping relation table.
Based on the above embodiment, as a preferred embodiment, the second obtaining module 200 includes:
a user grade determining unit, configured to determine a user grade of the target user;
a purchase permeability determining unit, configured to determine the purchase permeability according to the user rating of the target user;
the purchasing permeability is the ratio of the target commodities purchased by the target user from the target platform to the total quantity of the target commodities purchased in all purchasing channels within the past preset time period.
Based on the above embodiments, as a preferred embodiment, suggestion module 400 may be a module for determining a suggested purchase amount of the target commodity according to the actual sales amount, the predicted purchase amount and temperature coefficient, and a suggested purchase amount model;
the proposed purchase quantity model is R ═ S.C- (P-S);
wherein, R is the suggested purchase quantity, S is the actual sales quantity of the target commodity in the preset time period, C is the temperature coefficient, and P is the predicted purchase quantity.
Based on the above embodiment, as a preferred embodiment, the method may further include:
and the threshold value determining module is used for determining the preset threshold value according to the limit quantity before the target commodity with the recommended purchase quantity larger than the preset threshold value is taken as the recommended commodity when the limit quantity exists in the recommended commodity.
Based on the above embodiment, as a preferred embodiment, the method may further include:
and the pushing module is used for pushing the recommended commodities to the platform users corresponding to the target platform.
The present application also provides a computer readable storage medium having stored thereon a computer program which, when executed, may implement the steps provided by the above-described embodiments. The storage medium may include: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
The application also provides an e-commerce management terminal, which can comprise a memory and a processor, wherein the memory stores a computer program, and the processor can realize the steps provided by the embodiment when calling the computer program in the memory. Of course, the e-commerce management terminal can also comprise various network interfaces, power supplies and other components.
The embodiments are described in a progressive manner in the specification, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. For the system provided by the embodiment, the description is relatively simple because the system corresponds to the method provided by the embodiment, and the relevant points can be referred to the method part for description.
The principles and embodiments of the present application are explained herein using specific examples, which are provided only to help understand the method and the core idea of the present application. It should be noted that, for those skilled in the art, it is possible to make several improvements and modifications to the present application without departing from the principle of the present application, and such improvements and modifications also fall within the scope of the claims of the present application.
It is further noted that, in the present specification, relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
Claims (10)
1. A method for recommending an article, comprising:
acquiring the purchase quantity of a target commodity of a target user in a past preset time period and the actual sales quantity of the target commodity in the preset time period;
determining the purchasing permeability of the target user according to the purchasing quantity;
determining the predicted purchasing amount of the target commodity according to the purchasing permeability;
determining the suggested purchase quantity of the target commodity according to the actual sales quantity, the predicted purchase quantity and the temperature coefficient;
and taking the target commodity of which the suggested purchase amount is larger than a preset threshold value as a recommended commodity.
2. The article recommendation method according to claim 1, further comprising:
establishing a mapping relation table of the temperature and the temperature coefficient corresponding to the target commodity;
before determining the proposed purchase amount of the target commodity according to the actual sales amount, the predicted purchase amount and the temperature coefficient, the method further comprises the following steps:
and determining the temperature coefficient according to the current temperature and the mapping relation table.
3. The merchandise recommendation method of claim 1, wherein determining the purchase penetration rate of the target user based on the purchase quantity comprises:
determining a user rating of the target user;
determining the purchase penetration rate according to the user rating of the target user;
the purchasing permeability is the ratio of the target commodities purchased by the target user from the target platform to the total quantity of the target commodities purchased in all purchasing channels within the past preset time period.
4. The item recommendation method of claim 1, wherein determining the proposed purchase amount for the target item based on the actual sales amount, the predicted purchase amount, and the temperature coefficient comprises:
determining the suggested purchase quantity of the target commodity according to the actual sales quantity, the predicted purchase quantity, the temperature coefficient and the suggested purchase quantity model;
the proposed purchase quantity model is R ═ S.C- (P-S);
wherein, R is the suggested purchase quantity, S is the actual sales quantity of the target commodity in the preset time period, C is the temperature coefficient, and P is the predicted purchase quantity.
5. The method for recommending commodities of claim 1, wherein if there is a limited number of said recommended commodities, before taking said target commodity whose recommended purchase amount is greater than a preset threshold as a recommended commodity, further comprising:
and determining the preset threshold according to the limit quantity.
6. The item recommendation method according to claim 3, wherein after the target item of which the proposed purchase amount is larger than the preset threshold is taken as the recommended item, the method further comprises:
and pushing the recommended commodity to a platform user corresponding to the target platform.
7. An article recommendation system, comprising:
the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring the purchase quantity of a target user on a target commodity in a past preset time period and the actual sale quantity of the target commodity in the preset time period;
the second acquisition module is used for determining the purchasing permeability of the target user according to the purchasing quantity;
the prediction module is used for determining the predicted purchasing amount of the target commodity according to the purchasing permeability;
the suggestion module is used for determining the suggested purchase amount of the target commodity according to the actual sales amount, the predicted purchase amount and the temperature coefficient;
and the screening module is used for taking the target commodity of which the suggested purchase amount is greater than a preset threshold value as a recommended commodity.
8. The merchandise recommendation system of claim 7, further comprising:
the temperature mapping module is used for establishing a mapping relation table of the temperature and the temperature coefficient corresponding to the target commodity;
and the temperature coefficient determining module is used for determining the temperature coefficient according to the current temperature and the mapping relation table.
9. 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 item recommendation method according to any one of claims 1-6.
10. An e-commerce management terminal comprising a memory in which a computer program is stored and a processor that implements the steps of the merchandise recommendation method according to any one of claims 1-6 when the processor calls the computer program in the memory.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011066161.2A CN112053213A (en) | 2020-09-30 | 2020-09-30 | Commodity recommendation method, system and related device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011066161.2A CN112053213A (en) | 2020-09-30 | 2020-09-30 | Commodity recommendation method, system and related device |
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| CN112053213A true CN112053213A (en) | 2020-12-08 |
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