CN114282121A - Service node recommendation method, system, device and storage medium - Google Patents
Service node recommendation method, system, device and storage medium Download PDFInfo
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Abstract
The invention provides a service node recommendation method, a system, equipment and a storage medium, wherein the method comprises the following steps: and responding to the user input, extracting node relation data of the service class from a database, constructing a node relation network according to the node relation data, connecting a service demand side node and a service provider side node together through connecting edges in the node relation network, clustering each node in the node relation network by using node attribute data of each node in the node relation network, acquiring neighborhood node information of each node in a node community where the node in the node relation network is located, calculating the Euclidean distance between each node and the neighborhood node according to the neighborhood node information, assigning capacity values to the nodes according to the Euclidean distance, and outputting node recommendation information according to the capacity values of each node in the node relation network. The invention can accurately recommend the nodes through the capacity values, accurately respond to the user requirements and improve the user service experience.
Description
Technical Field
The present invention relates to the field of computers, and in particular, to a method, a system, a device, and a storage medium for recommending a service node.
Background
In the context of business services, nodes are an important concept. From the function differentiation, the nodes are divided into service demander nodes and service provider nodes, and the service provider nodes can provide service for the service demander nodes.
By adopting a computer technology processing means, the nodes can be classified according to the node attributes of the service nodes, so that accurate node matching can be provided for a service demander.
It is to be noted that the information disclosed in the above background section is only for enhancement of understanding of the background of the present invention and therefore may include information that does not constitute prior art known to a person of ordinary skill in the art.
Disclosure of Invention
Aiming at the problems in the prior art, the invention aims to provide a service node recommendation method, a system, equipment and a storage medium, which overcome the difficulties in the prior art, can accurately respond to the user requirements and improve the service experience of the user.
The embodiment of the invention provides a service node recommendation method, which comprises the following steps:
receiving a user input;
responding to user input, acquiring user demand information carrying a service class, extracting node relation data of the service class from a database, constructing a node relation network according to the node relation data, and connecting a service demand party node and a service provider node together through a connecting edge in the node relation network;
clustering nodes in the node relation network by using node attribute data of the nodes in the node relation network to obtain at least one node community;
acquiring neighborhood node information of each node in a node community in a node relation network, calculating the Euclidean distance between each node and the neighborhood nodes according to the neighborhood node information, and assigning the capacity value of the nodes according to the Euclidean distance;
and outputting node recommendation information according to the capability value of each node in the node relation network.
Optionally, before clustering nodes in the node relationship network by using the node attribute data of each node in the node relationship network, the service node recommendation method further includes:
vectorizing each node in the node relation network according to the node attribute data to obtain a network representation vector;
clustering each node in the node relation network by using the node attribute data of each node in the node relation network, wherein the clustering comprises the following steps:
and clustering each node in the node relation network by using the network characterization vector of each node in the node relation network.
Optionally, before vectorization representation is performed on each node in the node relationship network according to the node attribute data, the service node recommendation method includes:
each node in the node relation network is characterized by using a network adjacency matrix, each value in the network adjacency matrix is used for representing the number of service services to adjacent nodes, and the number of the service services is counted into node attribute data;
vectorizing each node in the node relationship network according to the node attribute data, including:
and compressing the network adjacency matrix of each node in the node relation network into a vector space to obtain a network characterization vector.
Optionally, obtaining neighborhood node information in a node community where each node is located in the node relationship network, and calculating an euclidean distance between each node and a neighborhood node according to the neighborhood node information, includes:
acquiring a node community CK (i) where the node i is located at each node i in the node relation network, acquiring all node neighborhoods S (i) in neighborhoods of which the shortest path distance to the node i is not more than a target distance from the node i in a clustering result, and acquiring a target neighborhood SC (i) ═ CK (i) # S (i), wherein SC (i) describes node neighborhood information;
and calculating the Euclidean distance of a neighborhood node j in the node i and the target neighborhood SC (i), wherein j belongs to SC (i), and xi is the network representation vector of the node i.
Optionally, performing capability value assignment on the node according to the euclidean distance, including:
and calculating the total influence of the node i in the target neighborhood to represent the proportion of the number of the nodes in the node community to the number of the nodes in the whole network, wherein the calculation result is the capability value.
Optionally, constructing a node relationship network according to the node relationship data includes:
and constructing a node relation network based on the undirected weight-bearing complex network according to the node relation data.
Optionally, clustering each node in the node relationship network by using the node attribute data of each node in the node relationship network includes:
and clustering the nodes in the node relation network by using the node attribute data of the nodes in the node relation network according to the set number of the node communities, and sequencing the node communities according to the number of the nodes in the node communities.
The embodiment of the invention also provides a service node recommendation system, which is used for realizing the service node recommendation method and comprises the following steps:
a receiving module for receiving user input;
the response module is used for responding to user input, acquiring user requirement information carrying business classes, extracting node relation data of the business classes from the database, constructing a node relation network according to the node relation data, and connecting a business demander node and a business provider node together through a connecting edge in the node relation network;
the clustering module is used for clustering each node in the node relation network by using the node attribute data of each node in the node relation network to obtain at least one node community;
the capacity value assignment module is used for acquiring adjacent domain node information in a node community where each node is located in the node relation network, calculating Euclidean distance between each node and the adjacent domain nodes according to the adjacent domain node information, and assigning the capacity value to the nodes according to the Euclidean distance;
and the node recommendation module outputs node recommendation information according to the capability value of each node in the node relation network.
An embodiment of the present invention further provides a service node recommendation device, including:
a processor;
a memory having stored therein executable instructions of the processor;
wherein the processor is configured to perform the steps of the service node recommendation method described above via execution of executable instructions.
An embodiment of the present invention further provides a computer-readable storage medium for storing a program, where the program is executed to implement the steps of the service node recommendation method.
The invention aims to provide a service node recommendation method, a system, equipment and a storage medium, which can divide all nodes in a node relation network into different node communities through clustering, wherein each community comprises a plurality of similar nodes. The neighborhood node information of each node in the node community indicates the influence depth range of the node, and the Euclidean distance between each node and the neighborhood nodes represents the influence on the neighborhood nodes. Under the condition, the influence use capacity value of each node on other nodes in the node relation network is quantized, instead of simply classifying the nodes by service classes, so that the nodes can be accurately recommended by the capacity values, the user requirements are accurately responded, and the user service experience is improved.
Drawings
Other features, objects and advantages of the present invention will become more apparent upon reading of the following detailed description of non-limiting embodiments thereof, with reference to the accompanying drawings.
FIG. 1 is a flow chart of one embodiment of a service node recommendation method of the present invention;
FIG. 2 is a flow chart of a second embodiment of a service node recommendation method of the present invention;
FIG. 3 is a block diagram of one embodiment of a service node recommendation system of the present invention;
FIG. 4 is a block diagram of a second embodiment of a service node recommendation system of the present invention;
FIG. 5 is a block diagram of a third embodiment of a service node recommendation system of the present invention;
fig. 6 is a schematic diagram of the operation of the service node recommendation system of the present invention.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art.
The drawings are merely schematic illustrations of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus their repetitive description will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in the form of software, or in one or more hardware forwarding modules or integrated circuits, or in different networks and/or processor devices and/or microcontroller devices.
In addition, the flow shown in the drawings is only an exemplary illustration, and not necessarily includes all the steps. For example, some steps may be divided, some steps may be combined or partially combined, and the actual execution sequence may be changed according to the actual situation. The use of "first," "second," and the like in this detailed description does not denote any order, quantity, or importance, but rather the terms first, second, and the like are used to distinguish one element from another. It should be noted that features of the embodiments of the invention and of the different embodiments may be combined with each other without conflict.
The present inventors have encountered in practice that nodes of the respective service classes can be matched by means of node attributes and provided to users. However, the nodes meeting the service class matching condition may not necessarily provide the matched service, and sometimes the quality of the service provided by these nodes is not high.
Therefore, how to more accurately recommend nodes in response to user demands is a technical problem to be solved by the embodiment of the invention.
Fig. 1 is a flowchart of a node recommendation method according to an embodiment of the present invention, where an execution entity of the method is a node recommendation system, and the node recommendation system does not belong to a computer device and can run a node recommendation program. Referring to fig. 1, the node recommendation method includes the steps of:
step 110: receiving a user input;
step 120: responding to user input, acquiring user requirement information carrying a service class, extracting node relation data of the service class from a database, constructing a node relation network according to the node relation data, and connecting a service requirement party node and a service provider node together through a connecting edge in the node relation network;
step 130: clustering nodes in the node relation network by using node attribute data of the nodes in the node relation network to obtain at least one node community;
step 140: acquiring neighborhood node information in a node community where each node is located in a node relation network, calculating the Euclidean distance between each node and the neighborhood nodes according to the neighborhood node information, and assigning the capacity value of each node according to the Euclidean distance;
step 150: and outputting node recommendation information according to the capability value of each node in the node relation network.
In the embodiment of the invention, the service demander node and the service provider node do not refer to a certain node, but refer to the service traffic relation between the two nodes, and the node relation data refers to the collection of a large amount of data of the service traffic relation.
Each node may be a service demander node or a service provider node, or may be both a service demander node and a service provider node.
Clustering is the partitioning of a data set into different classes or clusters according to a certain criterion (e.g. distance), so that the similarity of data objects in the same cluster is as large as possible, and the difference of data objects not in the same cluster is also as large as possible.
By using the embodiment of the invention, all nodes in the node relation network are divided into different node communities through clustering, and each community comprises a plurality of similar nodes. The neighborhood node information of each node in the node community indicates the influence depth range of the node, and the Euclidean distance between each node and the neighborhood nodes represents the influence on the neighborhood nodes.
Under the condition, the influence use capacity value of each node on other nodes in the node relation network is quantized, instead of simply classifying the nodes by service classes, so that the nodes can be accurately recommended by the capacity values, the user requirements are accurately responded, and the user service experience is improved.
In an alternative embodiment, a user interaction interface may be provided to receive user input. The form of the user input may be text input, control triggering or clicking, etc., and is not limited in particular.
In this case, the user can input specific service requirement information such as a required service class through the user interactive interface.
In an optional embodiment, the step of outputting the service node recommendation information may specifically be displaying the recommended node information for confirmation or selection by the user.
In an optional embodiment, before clustering nodes in the node relationship network by using node attribute data of the nodes in the node relationship network to obtain at least one node community, the service node recommendation method may further include:
vectorizing each node in the node relation network according to the node attribute data to obtain a network representation vector;
clustering each node in the node relation network by using the node attribute data of each node in the node relation network, wherein the clustering comprises the following steps:
and clustering each node in the node relation network by using the network characterization vector of each node in the node relation network.
By using the embodiment, the computer equipment can abstract the node attribute data into vectorized representation, so that clustering operation with high feasibility is realized.
In an optional embodiment, before vectorizing each node in the node relationship network according to the node attribute data, the service node recommendation method may include:
each node in the node relation network is characterized by using a network adjacency matrix, each value in the network adjacency matrix is used for representing the number of service services to adjacent nodes, and the number of the service services is counted into node attribute data;
vectorizing each node in the node relationship network according to the node attribute data, including:
and compressing the network adjacency matrix of each node in the node relation network into a vector space to obtain a network characterization vector.
In an alternative embodiment, constructing a node relationship network from node relationship data includes:
and constructing a node relation network based on the undirected weight-bearing complex network according to the node relation data.
In a complex network without directional ownership, there is no pointing information between nodes.
In an optional embodiment, clustering nodes in the node relationship network by using node attribute data of the nodes in the node relationship network includes:
and clustering the nodes in the node relation network by using the node attribute data of the nodes in the node relation network according to the set number of the node communities, and sequencing the node communities according to the number of the nodes in the node communities.
In an optional embodiment, obtaining neighbor node information in a node community where each node is located in a node relationship network and calculating an euclidean distance between each node and a neighbor node according to the neighbor node information includes:
acquiring a node community CK (i) where the node i is located at each node i in the node relation network, acquiring all node neighborhoods S (i) in neighborhoods of which the shortest path distance to the node i is not more than a target distance from the node i in a clustering result, and acquiring a target neighborhood SC (i) ═ CK (i) # S (i), wherein SC (i) describes node neighborhood information;
calculating Euclidean distance d of a neighborhood node j in a node i and a target neighborhood SC (i)ij＝(xi-xj)2J ∈ SC (i), xi is the network characterization vector of node i.
In an optional embodiment, the assigning the capability value to the node according to the euclidean distance includes:
calculating the sum of influence of the node i in the target neighborhood
And expressing the proportion of the number of the nodes in the node community to the number of the nodes in the whole network, wherein the calculation result is the capability value.
Fig. 2 is a flowchart of an application scenario of a service node recommendation method according to an embodiment of the present invention, where the service node recommendation method may include the following steps:
The embodiment of the invention constructs the industrial ecological cooperative relationship network based on enterprise cooperative information in the industrial ecology, reduces the complexity of the model through network characterization and clustering algorithm, has high evaluation speed and high accuracy on the influence of the enterprise in the large-scale industrial ecological cooperative network, and is beneficial to the development requirement of the enterprise on the industrial ecological cooperation.
The embodiment of the invention obtains the low-dimensional vector representation of enterprise cooperation characteristics by utilizing network characterization learning, constructs an enterprise influence domain and an influence calculation formula by using a clustering algorithm and combining with the neighborhood characteristics of network nodes on the basis, focuses on the enterprise influence range in industrial ecology, and can improve the scientificity and accuracy of enterprise ecological cooperation influence.
Fig. 3 is a module diagram of an embodiment of the service node recommendation system of the present invention. As shown in fig. 3, the service node recommendation system of the present invention includes but is not limited to:
a receiving module 310 that receives a user input;
the response module 320 is used for responding to user input, acquiring user requirement information carrying a service class, extracting node relation data of the service class from a database, constructing a node relation network according to the node relation data, and connecting a service demander node and a service provider node together through a connecting edge in the node relation network;
the clustering module 330 is configured to cluster the nodes in the node relationship network by using the node attribute data of the nodes in the node relationship network to obtain at least one node community;
the ability value assignment module 340 is used for acquiring neighborhood node information in a node community where each node is located in the node relation network, calculating the Euclidean distance between each node and the neighborhood node according to the neighborhood node information, and assigning the ability value to the node according to the Euclidean distance;
and the node recommending module 350 outputs node recommending information according to the capability value of each node in the node relationship network.
The implementation principle of the above module is referred to the related introduction in the service node recommendation method, and is not described herein again.
The service node recommendation system divides each node in the node relation network into different node communities through clustering, and each community comprises a plurality of similar nodes. The neighborhood node information of each node in the node community indicates the influence depth range of the node, and the European distance between each node and the neighborhood node represents the influence on the neighborhood node. Under the condition, the influence use capacity value of each node on other nodes in the node relation network is quantized, the nodes are not simply classified by service classes, and therefore the nodes can be accurately recommended through the capacity values, the user requirements are accurately responded, and the user service experience is improved.
Optionally, the response module 320 is specifically configured to:
and constructing a node relation network based on the undirected weight-bearing complex network according to the node relation data.
Optionally, the clustering module 330 is specifically configured to:
and clustering the nodes in the node relation network by using the node attribute data of the nodes in the node relation network according to the set number of the node communities, and sequencing the node communities according to the number of the nodes in the node communities.
Optionally, compared with fig. 3, the service node recommendation system shown in fig. 4 further includes:
the vector representation module 410 is configured to perform vectorization representation on each node in the node relationship network according to the node attribute data before clustering each node in the node relationship network by using the node attribute data of each node in the node relationship network, so as to obtain a network representation vector;
the clustering module 420 is specifically configured to:
and clustering each node in the node relation network by using the network characterization vector of each node in the node relation network.
Optionally, compared with fig. 4, the service node recommendation system shown in fig. 5 further includes:
a network adjacency matrix representation module 510, which represents each node in the node relationship network by using a network adjacency matrix before vectorizing and representing each node in the node relationship network according to the node attribute data, wherein each value in the network adjacency matrix represents the number of service services to the adjacent node, and the number of service services is counted into the node attribute data;
the vector representation module 520 is specifically configured to:
and compressing the network adjacency matrix of each node in the node relation network into a vector space to obtain a network characterization vector.
Optionally, the capability value assignment module 530 is specifically configured to:
acquiring a node community CK (i) where the node i is located at each node i in the node relation network, acquiring all node neighborhoods S (i) in neighborhoods of which the shortest path distance to the node i is not more than a target distance from the node i in a clustering result, and acquiring a target neighborhood SC (i) ═ CK (i) # S (i), wherein SC (i) describes node neighborhood information;
calculating Euclidean distance d of a neighborhood node j in a node i and a target neighborhood SC (i)ij＝(xi-xj)2J ∈ SC (i), xi is the network characterization vector of node i.
Optionally, the capability value assignment module 530 is further specifically configured to:
calculating the sum of influence of the node i in the target neighborhood
And expressing the proportion of the number of the nodes in the node community to the number of the nodes in the whole network, wherein the calculation result is the capability value.
The embodiment of the invention also provides service node recommendation equipment which comprises a processor. A memory having stored therein executable instructions of the processor. Wherein the processor is configured to perform the steps of the service node recommendation method via execution of the executable instructions.
As described above, the service node recommendation apparatus of this embodiment is capable of dividing the nodes in the node relationship network into different node communities each including a plurality of similar nodes by clustering. The neighborhood node information of each node in the node community indicates the influence depth range of the node, and the Euclidean distance between each node and the neighborhood nodes represents the influence on the neighborhood nodes.
Under the condition, the influence use capacity value of each node on other nodes in the node relation network is quantized, instead of simply classifying the nodes by service classes, so that the nodes can be accurately recommended by the capacity values, the user requirements are accurately responded, and the user service experience is improved.
As will be appreciated by one skilled in the art, aspects of the present invention may be embodied as a system, method or program product. Thus, various aspects of the invention may be embodied in the form of: an entirely hardware embodiment, an entirely software embodiment (including firmware, microcode, etc.) or an embodiment combining hardware and software aspects that may all generally be referred to herein as a "circuit," module "or" platform.
Fig. 6 is a schematic structural diagram of a service node recommendation device of the present invention. An electronic device 600 according to this embodiment of the invention is described below with reference to fig. 6. The electronic device 600 shown in fig. 6 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present invention.
As shown in fig. 6, the electronic device 600 is embodied in the form of a general purpose computing device. The components of the electronic device 600 may include, but are not limited to: at least one processing unit 610, at least one storage unit 620, a bus 630 connecting different platform components (including the storage unit 620 and the processing unit 610), a display unit 640, and the like.
Wherein the storage unit stores program code which can be executed by the processing unit 610, such that the processing unit 610 performs the steps according to various exemplary embodiments of the present invention described in the service node recommendation method section above in this description. For example, processing unit 610 may perform the steps as shown in fig. 1 or 2.
The storage unit 620 may include readable media in the form of volatile memory units, such as a random access memory unit (RAM)621 and/or a cache memory unit 622, and may further include a read only memory unit (ROM) 623.
The storage unit 620 may also include a program/utility 624 having a set (at least one) of program modules 625, such program modules 625 including, but not limited to: a processing system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may comprise an implementation of a network environment.
The electronic device 600 may also communicate with one or more external devices 670 (e.g., keyboard, pointing device, bluetooth device, etc.), with one or more devices that enable a user to interact with the electronic device 600, and/or with any devices (e.g., router, modem, etc.) that enable the electronic device 600 to communicate with one or more other computing devices. Such communication may occur via an input/output (I/O) interface 650.
Also, the electronic device 600 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network such as the Internet) through the network adapter 660. The network adapter 660 may communicate with other modules of the electronic device 600 via the bus 630. It should be appreciated that although not shown, other hardware and/or software modules may be used in conjunction with the electronic device 1000, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage platforms, to name a few.
The embodiment of the invention also provides a computer-readable storage medium for storing a program, and the steps of the service node recommendation method are realized when the program is executed. In some possible embodiments, the various aspects of the invention may also be implemented in the form of a program product comprising program code means for causing a terminal device to carry out the steps according to various exemplary embodiments of the invention described in the service node recommendation method section above in this description, when the program product is run on the terminal device.
According to the program product for implementing the method, the portable compact disc read only memory (CD-ROM) can be adopted, the program code is included, and the program product can be operated on a terminal device, such as a personal computer. However, the program product of the present invention is not limited in this regard and, in the present document, a readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
A computer readable storage medium may include a propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable storage medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out processes of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., through the internet using an internet service provider).
In summary, the present invention is directed to a method, a system, a device and a storage medium for recommending service nodes, which can divide nodes in a node relationship network into different node communities by clustering, wherein each community includes a plurality of similar nodes. The neighborhood node information of each node in the node community indicates the influence depth range of the node, and the Euclidean distance between each node and the neighborhood nodes represents the influence on the neighborhood nodes. Under the condition, the influence use capacity value of each node on other nodes in the node relation network is quantized, the nodes are not simply classified by service classes, so that the nodes can be accurately recommended by the capacity values, the user requirements are accurately responded, and the user service experience is improved.
The foregoing is a more detailed description of the invention in connection with specific preferred embodiments and it is not intended that the invention be limited to these specific details. For those skilled in the art to which the invention pertains, several simple deductions or substitutions can be made without departing from the spirit of the invention, and all should be considered as belonging to the protection scope of the present invention.
Claims (10)
1. A method for recommending a service node, comprising:
receiving a user input;
responding to the user input, acquiring user requirement information carrying a service class, extracting node relation data of the service class from a database, constructing a node relation network according to the node relation data, and connecting a service demander node and a service provider node together through a connecting edge in the node relation network;
clustering nodes in the node relation network by using node attribute data of the nodes in the node relation network to obtain at least one node community;
acquiring neighborhood node information of each node in the node community in the node relation network, calculating the Euclidean distance between each node and the neighborhood nodes according to the neighborhood node information, and assigning the capacity value of the node according to the Euclidean distance;
and outputting node recommendation information according to the capability value of each node in the node relation network.
2. The service node recommendation method according to claim 1, wherein before clustering nodes in the node relationship network using node attribute data of the nodes in the node relationship network, the service node recommendation method further comprises:
vectorizing each node in the node relation network according to the node attribute data to obtain a network representation vector;
clustering each node in the node relationship network by using the node attribute data of each node in the node relationship network, including:
and clustering each node in the node relation network by using the network characterization vector of each node in the node relation network.
3. The service node recommendation method according to claim 2, wherein before vectorizing each node in the node relationship network according to node attribute data, the service node recommendation method comprises:
each node in the node relation network is characterized by using a network adjacency matrix, each value in the network adjacency matrix is used for characterizing the number of business services to adjacent nodes, and the number of the business services is counted into the node attribute data;
the vectorization representation of each node in the node relationship network according to the node attribute data includes:
and compressing the network adjacency matrix of each node in the node relation network into a vector space to obtain the network characterization vector.
4. The service node recommendation method according to claim 3, wherein obtaining neighborhood node information in the node community in which each node in the node relationship network is located and calculating an euclidean distance between each node and a neighborhood node according to the neighborhood node information comprises:
acquiring, at each node i in the node relationship network, a node community CK (i) where the node i is located, acquiring all node neighborhoods S (i) in neighborhoods of which shortest path distances to the node i are not more than a target distance from the node i in a clustering result, and acquiring target neighborhoods SC (i) ═ CK (i) # S (i), wherein the SC (i) describes the node neighborhood information;
calculating Euclidean distance d of a neighborhood node j in the node i and a target neighborhood SC (i)ij＝(xi-xj)2J ∈ SC (i), xi is the network characterization vector of the node i.
5. The service node recommendation method according to claim 4, wherein said assigning a capability value to said node according to said euclidean distance comprises:
calculating the sum of influence of the node i in the target neighborhood
And expressing the proportion of the number of the nodes in the node community to the number of the nodes in the whole network, wherein the calculation result is the capacity value.
6. The service node recommendation method according to claim 1, wherein said constructing a node relationship network according to said node relationship data comprises:
and constructing a node relation network based on the undirected weight-containing complex network according to the node relation data.
7. The method of claim 1, wherein the clustering nodes in the node relationship network using the node attribute data of the nodes in the node relationship network comprises:
and clustering the nodes in the node relation network by using the node attribute data of the nodes in the node relation network according to the set number of the node communities, and sequencing the node communities according to the number of the nodes in the node communities.
8. A service node recommendation system, comprising:
a receiving module for receiving user input;
the response module is used for responding to the user input, acquiring user requirement information carrying a service class, extracting node relation data of the service class from a database, constructing a node relation network according to the node relation data, and connecting a service requirement party node and a service provider node together through a connecting edge in the node relation network;
the clustering module is used for clustering each node in the node relation network by using the node attribute data of each node in the node relation network to obtain at least one node community;
the capacity value assignment module is used for acquiring neighborhood node information in the node community where each node in the node relation network is located, calculating the Euclidean distance between each node and the neighborhood node according to the neighborhood node information, and assigning the capacity value of the node according to the Euclidean distance;
and the node recommendation module is used for outputting node recommendation information according to the capability value of each node in the node relation network.
9. A service node recommendation device, comprising:
a processor;
a memory having stored therein executable instructions of the processor;
wherein the processor is configured to perform the steps of the service node recommendation method of any one of claims 1 to 7 via execution of the executable instructions.
10. A computer-readable storage medium storing a program which, when executed by a processor, performs the steps of the service node recommendation method of any one of claims 1 to 7.
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| CN114707040B (en) * | 2022-04-08 | 2023-08-18 | 中国电信股份有限公司 | Enterprise cooperation group data classification method and device, storage medium and electronic equipment |
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