CN113962771A - Enterprise welfare recommendation method, device, equipment and storage medium - Google Patents
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Abstract
The application discloses a method, a device, equipment and a storage medium for recommending enterprise welfare products. The method comprises the following steps: acquiring attribute parameters of each reference enterprise; calculating the similarity between the reference enterprises and the target enterprises according to the attribute parameters of each reference enterprise; and determining the welfare of the target enterprise according to the similarity and the welfare of the reference enterprise. When the welfare are determined for the target enterprise, the welfare of the target enterprise are determined according to the welfare of the reference enterprise, and the welfare of the reference enterprise with high similarity has great reference value for the target enterprise. The scientificity and the rationality of welfare determination of the target enterprise are improved.
Description
Technical Field
The application relates to the technical field of computers, in particular to a method, a device, equipment and a storage medium for recommending enterprise welfare products.
Background
In the prior art, the commodity recommendation method depends on subjective selection of products by system operators, and the subjectivity is too high. However, if the enterprise selects the commodity for the first time, no historical data is used as a reference, and the problem of cold start exists. It is difficult to make scientific and reasonable choice of welfare products.
Disclosure of Invention
The method, the device, the equipment and the storage medium for recommending the benefits of the enterprise are provided, so that the problem that the selection of the benefits is difficult when the enterprise purchases the benefits is solved, and the selection and purchasing process of the benefits of the enterprise is optimized.
In order to achieve the above object, according to one aspect of the present application, there is provided an enterprise welfare product recommendation method.
An enterprise welfare recommendation method comprising:
acquiring attribute parameters of each reference enterprise;
calculating the similarity between the reference enterprises and the target enterprises according to the attribute parameters of each reference enterprise;
and determining the welfare of the target enterprise according to the similarity and the welfare of the reference enterprise.
In one embodiment, the attribute parameters include: and the enterprise scale, the enterprise industry, the enterprise budget, the enterprise region and the purchasing festival respectively correspond to characteristic values.
In one embodiment, calculating the similarity between each reference enterprise and the target enterprise according to the attribute parameters of the reference enterprise comprises:
and calculating the similarity between the reference enterprises and the target enterprises according to the attribute parameters of each reference enterprise by adopting a cosine distance algorithm.
In one embodiment, determining the welfare for the target business based on the similarity and the welfare for the reference business comprises:
sequencing the similarity of each reference enterprise from big to small;
determining an enterprise set with similarity greater than a preset similarity threshold;
and determining the welfare of the target enterprise according to the commodities purchased by each enterprise in the enterprise set.
In one embodiment, determining the welfare of the target business from the commodities purchased by each business in the set of businesses comprises:
querying a commodity set purchased by each enterprise in the enterprise set;
and (3) obtaining an intersection of the commodity sets purchased by each enterprise to obtain a commodity set p (u, k):
p(u，k)＝W1∩W2∩W3……∩Wk；
wherein p (u, k) represents k enterprises with the highest similarity to the enterprise u, the purchased repeated commodity set, and Wk represents the commodity set purchased by the kth enterprise;
and determining the welfare of the target enterprise according to the commodity set.
In one embodiment, determining the welfare for the target business from the set of items includes:
counting the number of times of repetition of each commodity in the commodity set;
sequencing according to the sequence of the repetition times from large to small;
and determining the commodities with the repetition times larger than a preset repetition time threshold value as welfare commodities of the target enterprise.
In one embodiment, the welfare product recommendation lists of the target enterprises are generated by sorting according to the sequence of the repetition times from large to small.
In order to achieve the above object, according to another aspect of the present application, there is provided an enterprise welfare product recommendation device.
An enterprise welfare recommendation device comprising:
the acquisition module is used for acquiring the attribute parameters of each reference enterprise;
the calculation module is used for calculating the similarity between the reference enterprises and the target enterprises according to the attribute parameters of each reference enterprise;
and the welfare determining module is used for determining the welfare of the target enterprise according to the similarity and the welfare of the reference enterprise.
In one embodiment, the computing module is further configured to: and calculating the similarity between the reference enterprises and the target enterprises according to the attribute parameters of each reference enterprise by adopting a cosine distance algorithm.
In one embodiment, the welfare determining module is further configured to rank the similarity of each reference enterprise in descending order;
determining an enterprise set with similarity greater than a preset similarity threshold;
and determining the welfare of the target enterprise according to the commodities purchased by each enterprise in the enterprise set.
In one embodiment, the welfare determining module is further configured to query, in the set of businesses, the set of items purchased by each business;
and (3) obtaining an intersection of the commodity sets purchased by each enterprise to obtain a commodity set p (u, k):
p(u，k)＝W1∩W2∩W3……∩Wk；
wherein p (u, k) represents k enterprises with the highest similarity to the enterprise u, the purchased repeated commodity set, and Wk represents the commodity set purchased by the kth enterprise;
and determining the welfare of the target enterprise according to the commodity set.
In one embodiment, the welfare determining module is further configured to count, for each item in the set of items, the number of repetitions for the item;
sequencing according to the sequence of the repetition times from large to small;
and determining the commodities with the repetition times larger than a preset repetition time threshold value as welfare commodities of the target enterprise.
In one embodiment, the welfare item determination module is further configured to sort the welfare items according to the sequence of the repetition times from large to small, and generate a welfare item recommendation list of the target enterprise.
In order to achieve the above object, according to a third aspect of the present application, there is provided an electronic apparatus; comprising at least one processor and at least one memory; the memory is to store one or more program instructions; the processor is configured to execute one or more program instructions to perform any of the above steps.
According to a fourth aspect of the present application, there is provided a computer readable storage medium having one or more program instructions embodied therein for performing the steps of any of the above.
In the embodiment of the application, when the welfare are determined for the target enterprise, the welfare of the target enterprise are determined according to the welfare of the reference enterprise, and the welfare of the reference enterprise with higher similarity has great reference value for the target enterprise. The scientificity and rationality of welfare determination are improved.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, serve to provide a further understanding of the application and to enable other features, objects, and advantages of the application to be more apparent. The drawings and their description illustrate the embodiments of the invention and do not limit it. In the drawings:
FIG. 1 is a flow chart of a method for recommending business benefits according to an embodiment of the present application;
FIG. 2 is a schematic structural diagram of an enterprise welfare recommendation device according to an embodiment of the application;
fig. 3 is a schematic structural diagram of an enterprise welfare recommending device according to an embodiment of the application.
Detailed Description
In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only partial 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.
It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It should be understood that the data so used may be interchanged under appropriate circumstances such that embodiments of the application described herein may be used. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Furthermore, the terms "mounted," "disposed," "provided," "connected," and "sleeved" are to be construed broadly. For example, it may be a fixed connection, a removable connection, or a unitary construction; can be a mechanical connection, or an electrical connection; may be directly connected, or indirectly connected through intervening media, or may be in internal communication between two devices, elements or components. The specific meanings of the above terms in the present invention can be understood by those of ordinary skill in the art according to specific situations.
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.
First, the technical terms in the field are introduced.
Cold start: when a new enterprise is created, since the enterprise does not generate behavior data of selections, purchases and the like, the interest of the enterprise cannot be predicted according to the historical behaviors of the enterprise, and therefore personalized recommendations cannot be given.
As shown in fig. 1, the present application proposes an enterprise welfare product recommendation method; the method includes steps S102 to S106 as follows:
step S102, acquiring attribute parameters of each reference enterprise;
wherein the attribute parameters include but are not limited to: the attribute parameters include: and the enterprise scale, the enterprise industry, the enterprise budget, the enterprise region and the purchasing festival respectively correspond to characteristic values.
It is to be emphasized that the above-mentioned characteristic values are preset. And setting corresponding characteristic values for enterprise scale, enterprise industry, enterprise budget, enterprise region and purchasing festival respectively.
Illustratively, an enterprise portrait and characteristics are established for the benefit procurement of the enterprise, and 5 basic attributes of the enterprise are extracted by analyzing the benefit procurement behavior of the enterprise: enterprise scale, enterprise industry, enterprise budget, enterprise region, procurement holiday. Based on these basic attributes of the enterprise, the following datamation models are built:
see tables 1, 2, 3, 4, 5 below, respectively;
| enterprise scale | Numerical value |
| Less than 100 persons | 1 |
| 100-200 people | 2 |
| 200- | 3 |
| 300- | 4 |
| 400-500 people | 5 |
| 500- | 6 |
| 1000-2000 people | 7 |
| 2000-3000 people | 8 |
| Over 3000 people | 9 |
TABLE 1
| Enterprise industry | Numerical value |
| National rabbet | 1 |
| Organ | 2 |
| Education | 3 |
| For the purpose of research | 4 |
| Finance | 5 |
| Energy source | 6 |
| Urban construction | 7 |
| Traffic control system | 8 |
| Others | 9 |
TABLE 2
TABLE 3
| Purchasing holidays | Numerical value |
| New year's egg | 1 |
| Spring festival | 2 |
| Labor saving | 3 |
| Dragon boat festival | 4 |
| Mid-autumn festival | 5 |
| Festival of national day | 6 |
TABLE 4
According to the above rules, the enterprise feature values are extracted and a feature vector table is established, see table 5:
TABLE 5
Step S104, calculating the similarity between the reference enterprises and the target enterprises according to the attribute parameters of each reference enterprise;
and S106, determining the welfare of the target enterprise according to the similarity and the welfare of the reference enterprise.
When the method of the invention is used for determining the welfare of the target enterprise, the welfare of the target enterprise is determined according to the welfare of the reference enterprise, and the reference enterprise with higher similarity and the welfare target enterprise of the reference enterprise have great reference value through calculation. The scientificity and rationality of welfare determination are improved.
In one embodiment, calculating the similarity between each reference enterprise and the target enterprise according to the attribute parameters of the reference enterprise comprises:
and calculating the similarity between the reference enterprises and the target enterprises according to the attribute parameters of each reference enterprise by adopting a cosine distance algorithm.
Specifically, the cosine distance calculation formula is as follows:
to illustrate how similar the two enterprises numbered 1001 and 1002 are calculated, the data is brought into the formula
The cosine similarity of the 1001 and 1002 users is equal to 0.684 through calculation, the similarity value ranges from-1 to 1, 1 represents complete similarity between the users, 0 represents independence between the users, 1 represents that the similarity between the two users is just opposite, and the score value between-1 and 1 represents similarity and difference. Similarly, a similarity value between the enterprise 1001 and other enterprises may be calculated.
In one embodiment, determining the welfare for the target business based on the similarity and the welfare for the reference business comprises: sequencing the similarity of each reference enterprise from big to small; determining an enterprise set with similarity greater than a preset similarity threshold; and determining the welfare of the target enterprise according to the commodities purchased by each enterprise in the enterprise set.
In one embodiment, determining the welfare of the target business from the commodities purchased by each business in the set of businesses comprises:
querying a commodity set purchased by each enterprise in the enterprise set;
and (3) obtaining an intersection of the commodity sets purchased by each enterprise to obtain a commodity set p (u, k):
p(u，k)＝W1∩W2∩W3……∩Wk；
wherein k is a positive integer greater than zero;
p (u, k) represents the k enterprises with the highest similarity to the enterprise u, the duplicate set of purchased goods, WkRepresenting the purchased commodity set of the k enterprise;
and determining the welfare of the target enterprise according to the commodity set.
Illustratively, when a welfare choice recommendation needs to be made for the enterprise 1001, the similarity of all enterprises and the 1001 enterprise needs to be calculated according to the above rules and sorted from big to small.
And (4) taking the first few enterprises with the highest similarity, inquiring the commodity set purchased by each enterprise, and taking intersection of commodities purchased by each enterprise.
p(u，k)＝W1∩W2∩W3……∩Wk；
Where p (u, k) represents the k enterprises with the highest similarity to enterprise u, the repeat commodity set purchased, and Wk represents the commodity set purchased by the k-th enterprise. And sequencing each commodity in p (u, k) from high to low according to repeated occurrence times, generating a welfare recommendation result, presenting the welfare recommendation result to enterprise purchasing personnel, and helping the enterprise to find out welfare meeting the requirement of the enterprise more quickly.
The following illustrates the process:
when a new enterprise is resident on the platform, the system operator establishes an enterprise account and perfects the relevant information, and then the welfare product recommendation is carried out according to the following steps.
The enterprise base attributes are established, see table 6.
| Enterprise ID | Enterprise scale | Enterprise industry | Enterprise budget | Enterprise area | Purchasing holidays |
| 1001 | 120 persons | Education | 200 yuan | Huazhong | Mid-autumn festival |
| 1002 | 350 people | Energy source | 150 yuan | North China | Dragon boat festival |
| 1003 | 1100 human being | Finance | 80 Yuan | South China | Mid-autumn festival |
| 1004 | 430 person | Energy source | 330 Yuan | East China | Spring festival |
TABLE 6
The business attributes are converted to datamation feature values according to rules, see table 7.
| Enterprise ID | Enterprise scale | Enterprise industry | Enterprise budget | Enterprise area | Purchasing holidays |
| 1001 | 2 | 3 | 3 | 4 | 5 |
| 1002 | 4 | 6 | 2 | 2 | 4 |
| 1003 | 3 | 5 | 1 | 6 | 5 |
| 1004 | 5 | 6 | 4 | 5 | 2 |
Table 7 performs similarity calculation for the enterprise 1001 that needs to be recommended, and other enterprise features:
ranking the enterprises with high-to-low similarity to the enterprise 1001, namely the enterprise 1003, the enterprise 1004 and the enterprise 1002;
querying enterprise 1004 and enterprise 1002 for historical welfare purchases;
w1004 ═ rice, shampoo, notebook, condiment, biscuit, humidifier, bread machine }
W1003 ═ backpack, shampoo, scarf, condiment, office stationery, humidifier, bread machine }
2 enterprises with the highest similarity to the enterprise 1001, namely the enterprise 1003 and the enterprise 1004, are screened, and the intersection set of the welfare purchased by the 2 enterprises is taken to obtain the recommended welfare for the enterprise 1001.
P1001 ═ W1004 ═ W1003 ═ shampoo, seasoning, humidifier, bread maker }
It is worth emphasizing that the shampoo, the seasoning, the humidifier and the bread maker are adopted; the number of repetitions is 2, which means that they are present in the welfare of the company 1003 and the company 1004, respectively.
And selecting and sorting according to the repetition times, so that the recommended welfare products can be selected preferably.
The method uses a collaborative filtering algorithm based on enterprise modeling, establishes a model by taking enterprises as dimensions, extracts characteristic values, and calculates the cosine similarity according to the characteristic value vectors of the enterprises. The number of enterprises is greatly less than that of users and commodities, so that the data calculation amount is greatly reduced, meanwhile, the problem of cold start is avoided by extracting according to the modeling characteristics of the enterprises, the personalized recommendation for different enterprises is realized, and the efficiency of purchasing welfare products of the enterprises is improved.
The method has small data operation amount, and the selection recommendation of new enterprises has no cold start problem. The collaborative filtering recommendation system for the enterprise welfare selections is completed by modeling the basic attributes of the enterprise, extracting the characteristic values, establishing the characteristic value vectors and calculating the similarity of the characteristic value vectors of all the enterprises on the basis of the existing use behavior data of the enterprise and the employees, so that the selection efficiency and the employee satisfaction during purchasing the enterprise welfare selections are effectively improved.
It should be noted that the steps illustrated in the flowcharts of the figures may be performed in a computer system such as a set of computer-executable instructions and that, although a logical order is illustrated in the flowcharts, in some cases, the steps illustrated or described may be performed in an order different than presented herein.
In a second aspect, the present application further provides an enterprise welfare recommending apparatus, which is shown in fig. 2, and is configured to provide a schematic structural diagram of the enterprise welfare recommending apparatus; the device includes:
an obtaining module 21, configured to obtain attribute parameters of each reference enterprise;
the calculation module 22 is used for calculating the similarity between the reference enterprises and the target enterprises according to the attribute parameters of each reference enterprise;
and a welfare determining module 23, configured to determine the welfare of the target enterprise according to the similarity and the welfare of the reference enterprise.
In one embodiment, the calculation module 22 is further configured to: and calculating the similarity between the reference enterprises and the target enterprises according to the attribute parameters of each reference enterprise by adopting a cosine distance algorithm.
In one embodiment, the welfare determining module 23 is further configured to rank the similarity of each reference enterprise in descending order;
determining an enterprise set with similarity greater than a preset similarity threshold;
and determining the welfare of the target enterprise according to the commodities purchased by each enterprise in the enterprise set.
In one embodiment, the welfare determining module 23 is further configured to query, among the set of businesses, the set of items purchased by each business;
and (3) obtaining an intersection of the commodity sets purchased by each enterprise to obtain a commodity set p (u, k):
p(u，k)＝W1∩W2∩W3……∩Wk；
wherein p (u, k) represents k enterprises with the highest similarity to the enterprise u, the purchased repeated commodity set, and Wk represents the commodity set purchased by the kth enterprise;
and determining the welfare of the target enterprise according to the commodity set.
In one embodiment, the welfare determining module 23 is further configured to count, for each item in the set of items, the number of repetitions of the item;
sequencing according to the sequence of the repetition times from large to small;
and determining the commodities with the repetition times larger than a preset repetition time threshold value as welfare commodities of the target enterprise.
In one embodiment, the welfare determining module 23 is further configured to generate a welfare item recommendation list of the target enterprise by sorting according to the order of the repetition times from large to small.
In order to achieve the above object, according to a third aspect of the present application, there is provided an electronic apparatus; referring to fig. 3, a schematic structural diagram of an electronic device is shown; comprises at least one processor 31 and at least one memory 32; the memory 32 is for storing one or more program instructions; the processor 31 is configured to execute one or more program instructions to perform the following steps:
acquiring attribute parameters of each reference enterprise;
calculating the similarity between the reference enterprises and the target enterprises according to the attribute parameters of each reference enterprise;
and determining the welfare of the target enterprise according to the similarity and the welfare of the reference enterprise.
In one embodiment, the attribute parameters include: and the enterprise scale, the enterprise industry, the enterprise budget, the enterprise region and the purchasing festival respectively correspond to characteristic values.
In one embodiment, the processor 31 is further configured to calculate a similarity between each reference enterprise and the target enterprise according to the attribute parameters of the reference enterprise by using a cosine distance algorithm.
In one embodiment, the processor 31 is further configured to rank the similarity of each reference enterprise in descending order;
determining an enterprise set with similarity greater than a preset similarity threshold;
and determining the welfare of the target enterprise according to the commodities purchased by each enterprise in the enterprise set.
In one embodiment, the processor 31 is further configured to query the set of commodities purchased by each enterprise in the set of enterprises;
and (3) obtaining an intersection of the commodity sets purchased by each enterprise to obtain a commodity set p (u, k):
p(u，k)＝W1∩W2∩W3……∩Wk；
wherein p (u, k) represents k enterprises with the highest similarity to the enterprise u, the purchased repeated commodity set, and Wk represents the commodity set purchased by the kth enterprise;
and determining the welfare of the target enterprise according to the commodity set.
In one embodiment, the processor 31 is further configured to count a number of repetitions of the item for each item in the set of items;
sequencing according to the sequence of the repetition times from large to small;
and determining the commodities with the repetition times larger than a preset repetition time threshold value as welfare commodities of the target enterprise.
In one embodiment, the welfare product recommendation lists of the target enterprises are generated by sorting according to the sequence of the repetition times from large to small.
According to a fourth aspect of the present application, there is provided a computer readable storage medium having one or more program instructions embodied therein, the one or more program instructions for performing the steps of:
acquiring attribute parameters of each reference enterprise;
calculating the similarity between the reference enterprises and the target enterprises according to the attribute parameters of each reference enterprise;
and determining the welfare of the target enterprise according to the similarity and the welfare of the reference enterprise.
In one embodiment, the attribute parameters include: and the enterprise scale, the enterprise industry, the enterprise budget, the enterprise region and the purchasing festival respectively correspond to characteristic values.
In one embodiment, calculating the similarity between each reference enterprise and the target enterprise according to the attribute parameters of the reference enterprise comprises:
and calculating the similarity between the reference enterprises and the target enterprises according to the attribute parameters of each reference enterprise by adopting a cosine distance algorithm.
In one embodiment, determining the welfare for the target business based on the similarity and the welfare for the reference business comprises:
sequencing the similarity of each reference enterprise from big to small;
determining an enterprise set with similarity greater than a preset similarity threshold;
and determining the welfare of the target enterprise according to the commodities purchased by each enterprise in the enterprise set.
In one embodiment, determining the welfare of the target business from the commodities purchased by each business in the set of businesses comprises:
querying a commodity set purchased by each enterprise in the enterprise set;
and (3) obtaining an intersection of the commodity sets purchased by each enterprise to obtain a commodity set p (u, k):
p(u，k)＝W1∩W2∩W3……∩Wk；
wherein p (u, k) represents k enterprises with the highest similarity to the enterprise u, the purchased repeated commodity set, and Wk represents the commodity set purchased by the kth enterprise;
and determining the welfare of the target enterprise according to the commodity set.
In one embodiment, determining the welfare for the target business from the set of items includes:
counting the number of times of repetition of each commodity in the commodity set;
sequencing according to the sequence of the repetition times from large to small;
and determining the commodities with the repetition times larger than a preset repetition time threshold value as welfare commodities of the target enterprise.
In one embodiment, the welfare product recommendation lists of the target enterprises are generated by sorting according to the sequence of the repetition times from large to small.
The various methods, steps and logic blocks disclosed in the embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present invention may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The processor reads the information in the storage medium and completes the steps of the method in combination with the hardware.
The storage medium may be a memory, for example, which may be volatile memory or nonvolatile memory, or which may include both volatile and nonvolatile memory.
The nonvolatile Memory may be a Read-Only Memory (ROM), a Programmable ROM (PROM), an Erasable PROM (EPROM), an Electrically Erasable PROM (EEPROM), or a flash Memory.
The volatile Memory may be a Random Access Memory (RAM) which serves as an external cache. By way of example and not limitation, many forms of RAM are available, such as Static Random Access Memory (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), SLDRAM (SLDRAM), and Direct Rambus RAM (DRRAM).
The storage media described in connection with the embodiments of the invention are intended to comprise, without being limited to, these and any other suitable types of memory.
Those skilled in the art will appreciate that the functionality described in the present invention may be implemented in a combination of hardware and software in one or more of the examples described above. When software is applied, the corresponding functionality may be stored on or transmitted over as one or more instructions or code on a computer-readable medium. Computer-readable media includes both computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. A storage media may be any available media that can be accessed by a general purpose or special purpose computer.
It will be apparent to those skilled in the art that the modules or steps of the present invention described above may be implemented by a general purpose computing device, they may be centralized on a single computing device or distributed across a network of multiple computing devices, and they may alternatively be implemented by program code executable by a computing device, such that they may be stored in a storage device and executed by a computing device, or fabricated separately as individual integrated circuit modules, or fabricated as a single integrated circuit module from multiple modules or steps. Thus, the present invention is not limited to any specific combination of hardware and software.
The above description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (10)
1. An enterprise welfare recommendation method is characterized by comprising the following steps:
acquiring attribute parameters of each reference enterprise;
calculating the similarity between the reference enterprises and the target enterprises according to the attribute parameters of each reference enterprise;
and determining the welfare of the target enterprise according to the similarity and the welfare of the reference enterprise.
2. The method of claim 1, wherein the attribute parameters comprise: and the enterprise scale, the enterprise industry, the enterprise budget, the enterprise region and the purchasing festival respectively correspond to characteristic values.
3. The enterprise welfare recommendation method of claim 2,
calculating the similarity between the reference enterprise and the target enterprise according to the attribute parameters of each reference enterprise, wherein the similarity comprises the following steps:
and calculating the similarity between the reference enterprises and the target enterprises according to the attribute parameters of each reference enterprise by adopting a cosine distance algorithm.
4. The enterprise welfare recommendation method of claim 3,
determining the welfare of the target enterprise according to the similarity and the welfare of the reference enterprise, comprising:
sequencing the similarity of each reference enterprise from big to small;
determining an enterprise set with similarity greater than a preset similarity threshold;
and determining the welfare of the target enterprise according to the commodities purchased by each enterprise in the enterprise set.
5. The enterprise welfare recommendation method of claim 4,
determining the welfare of the target enterprise according to the commodities purchased by each enterprise in the enterprise set, comprising:
querying a commodity set purchased by each enterprise in the enterprise set;
and (3) obtaining an intersection of the commodity sets purchased by each enterprise to obtain a commodity set p (u, k):
p(u，k)＝W1∩W2∩W3……∩Wk；
wherein p (u, k) represents k enterprises with the highest similarity to the enterprise u, the purchased repeated commodity set, and Wk represents the commodity set purchased by the kth enterprise;
and determining the welfare of the target enterprise according to the commodity set.
6. The enterprise welfare recommendation method of claim 5,
determining the welfare of the target enterprise according to the commodity set, comprising:
counting the number of times of repetition of each commodity in the commodity set;
sequencing according to the sequence of the repetition times from large to small;
and determining the commodities with the repetition times larger than a preset repetition time threshold value as welfare commodities of the target enterprise.
7. The enterprise welfare recommendation method of claim 6,
and sequencing according to the sequence of the repetition times from large to small to generate a welfare commodity recommendation list of the target enterprise.
8. An enterprise welfare recommendation device, comprising:
the acquisition module is used for acquiring the attribute parameters of each reference enterprise;
the calculation module is used for calculating the similarity between the reference enterprises and the target enterprises according to the attribute parameters of each reference enterprise;
and the welfare determining module is used for determining the welfare of the target enterprise according to the similarity and the welfare of the reference enterprise.
9. An enterprise welfare recommendation device, comprising: at least one processor and at least one memory; the memory is to store one or more program instructions; the processor, configured to execute one or more program instructions to perform the method of any of claims 1-7.
10. A computer-readable storage medium having one or more program instructions embodied therein for performing the method of any one of claims 1-7.
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