CN112800141A - On-demand service aggregation and recommendation method based on RGPS meta-model - Google Patents
On-demand service aggregation and recommendation method based on RGPS meta-model Download PDFInfo
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Abstract
In order to solve the problems of the sharp increase of the number of services, the disorder of Service organizations and the diversification of user requirements in an application Service aggregation and recommendation mechanism of 'internet +', the Service meeting the personalized requirements of users is realized by associating the semantics of a Role (Role) -target (Goal) -Process (Process) -Service (Service) requirement meta model. The invention discloses an on-demand service aggregation and recommendation method based on an RGPS (reduced group prediction system) meta-model, and provides an individualized recommendation method for potential services by adopting an LSTM (local Strand TM) neural network based on the reverse thrust of the orientation role and the target in an RGPS associated network.
Description
Technical Field
The invention belongs to the technical field of computers, and particularly relates to an on-demand service aggregation and recommendation method of an RGPS meta-model.
Background
The rise of the concept of 'internet +' and customized services enables web services forming the internet to have the characteristics of distribution, modularization, self description and the like. Meanwhile, the user requirements on the internet tend to be personalized and complicated, so the problem of accurate matching of the service and the user requirements becomes more important. However, in the actual recommendation of the Web service, the problems that the user demand is variable, the business process is complex, the service resources cannot adapt to the change of the user demand and the like occur, so that the service recommendation becomes difficult. The existing software engineering technology does not fully research the problem, so that a governance and management method which can be suitable for user demand personalization becomes an important research hotspot and further development direction in the field of service aggregation and service recommendation.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides an on-demand service aggregation based on an RGPS meta-model and a recommendation method thereof.
The invention adopts the following technical scheme:
1. an on-demand service aggregation and recommendation method based on an RGPS meta-model is characterized by comprising the following steps:
step 2, constructing corresponding SDI according to different types of models obtained in the step 1, wherein the realization method is as follows,
a user demand driven RGPS associated network generation step is given, wherein
Evaluation of the service (QoS value) if
If the satisfaction degree of the user to the service is above the minimum QoS threshold value, the service of role reverse thrust meets the requirement of the user, and the weighted edge between the service and the role node is connected; in the same way, if
Representing the user pairIf the satisfaction degree of the service is above the minimum QoS threshold, the service of the target reverse thrust meets the requirement of the user, and the weighted edge between the service and the target node is connected;
and 3, analyzing the user requirements and recommending related potential services by utilizing R and G reverse-thrust service ways and combining an LSTM neural network model, so that new instant services can be recommended for the user.
2. The on-demand service aggregation and recommendation method based on RGPS meta-model as claimed in claim 1, wherein: in step 2, a user demand driven RGPS associated network generation step is given, wherein
Evaluation of the service (QoS value) if
If the satisfaction degree of the user to the service is above the minimum QoS threshold value, the service of role reverse thrust meets the requirement of the user, and the weighted edge between the service and the role node is connected; in the same way, if
If the satisfaction degree of the user to the service is above the minimum QoS threshold value, the service of the target reverse thrust meets the requirement of the user, and the weighted edge between the service and the target node is connected; the RGPS associated network is continuously modified through the feedback correction mechanism, so that the related services which can well meet the requirements of users are found out, the searched service sets can be sequenced, and scientific basis is provided for the customized recommendation of the services.
3. The on-demand service aggregation and recommendation method based on RGPS meta-model as claimed in claim 1, wherein: the key point of expressing aggregated services by using a directed graph model is how to obtain accurate RGPS elements from the demands, namely, the demands are analyzed and modeled by using an LSTM neural network.
The LSTM neural network processes the core idea of service recommendation: the core of the LSTM is "cell state", which functions as a memory space in the whole model, and the memory space changes with time, and the memory space cannot control which users' demand information is memorized, so that it is a control gate that really plays a control role.
(1) The input node receives the output of the hidden node of the previous time point and the current input as the input, and then passes through an activation function of tanh;
(2) an input gate: the gate input is the output of the hidden node at the last time point and the current input, and the activation function is sigmoid (the reason is that the output of the sigmoid is between 0 and 1, and the multiplication of the output of the input gate and the output of the input node can play a role in controlling the information quantity);
(3) internal state node: the input is the current input filtered by the input gate and the internal state node output of the previous time point;
(4) forgetting to remember the door: the function of controlling the internal state information is realized, and the Gate is opened (set to 1) or closed (set to 0) through the training parameters to protect the Cell;
(5) an output gate: the function of controlling output information is realized, the input of the gate is the output of the hidden node at the last time point and the current input, and the activation function is sigmoid;
for example, in the field of 'travel', a user inputs a demand 'i want to reserve a train ticket on the day, reserve a lodging and provide urban traffic information'; the example sentences are subjected to machine recognition and user requirements are marked off, each vertical rectangular frame of the LSTM model in the attached figure 2 represents a hidden layer of each iteration, each hidden layer is provided with a plurality of neurons, each neuron performs linear matrix operation on an input vector, and an output result (for example, a tanh () function) is generated through nonlinear operation. In each iteration, the output of the previous iteration varies with the word vector of the next word in the document, XtIs the input to the hidden layer and the hidden layer will produce the predicted output value and the output feature vector H that is provided to the hidden layer of the next layert。
The expression formula of each layer model of the LSTM neural network is as follows:
an input gate: the function is to control the output of the cell state:
memory cell: for the time t-1 → t, the memory cell information change process is represented as t-1 time state and is obtained by filtering with forgetting gate
The stored information and the input door information acquired at the time t are compared
Addition gives the transition in cell state:
output of cell status:
then the inverse of the residual is conducted to the output gate to modify the function:
the following error propagates to the cell state:
error passes to forget gate:
after the residual conduction is finished, the residual is directly derived from the weight
The invention has the beneficial effects that:
(1) the user requirements are divided into specific fields, then modeling is carried out through common requirements, analysis is carried out on the aspects of roles, targets and processes related to the user requirements, a corresponding on-demand service aggregation algorithm is designed, and an associated network diagram is drawn.
(2) 2 service searching and recommending methods are designed to meet different requirement expression forms provided by users, and the problem that the users recommend a proper potential service set by using cooperation among services is better solved, so that the requirements are met.
(3) A specific experiment is designed to verify and analyze service searching timeliness, precision, recall rate and F value in four aspects, and relevant definitions and algorithms are verified according to specific fields, so that the reliability of the method is proved by quantitative expression.
Drawings
Figure 1 is a diagram of a service aggregation and recommendation framework based on RGPS on-demand guidance.
FIG. 2 is a diagram of an LSTM neural network analysis model for user demand.
Detailed Description
Aiming at the characteristic of disorder of traditional Service aggregation, the invention provides a R, G, P, S-associated weighted network method for orderly organizing Service aggregation based on the semantic association relationship of a Role (Role) -target (Goal) -Process (Process) -Service (Service) requirement meta model. The following description of the embodiments of the present invention will be made with reference to the accompanying drawings: an on-demand service aggregation and recommendation method based on RGPS meta-model is characterized by comprising the following steps:
step 2, constructing corresponding SDI according to different types of models obtained in the step 1, wherein the realization method is as follows,
a user demand driven RGPS associated network generation step is given, wherein
Evaluation of the service (QoS value) if
If the satisfaction degree of the user to the service is above the minimum QoS threshold value, the service of role reverse thrust meets the requirement of the user, and the weighted edge between the service and the role node is connected; in the same way, if
If the satisfaction degree of the user to the service is above the minimum QoS threshold value, the service of the target reverse thrust meets the requirement of the user, and the weighted edge between the service and the target node is connected;
and 3, analyzing the user requirements and recommending related potential services by utilizing R and G reverse-thrust service ways and combining an LSTM neural network model, so that new instant services can be recommended for the user.
2. The on-demand service aggregation and recommendation method based on RGPS meta-model as claimed in claim 1, wherein: in step 2, a user demand driven RGPS associated network generation step is given, wherein
Evaluation of the service (QoS value) if
Is shown byThe satisfaction degree of the user to the service is above the minimum QoS threshold value, and if the service of role reverse thrust meets the requirement of the user, the weighted edge between the service and the role node is connected; in the same way, if
If the satisfaction degree of the user to the service is above the minimum QoS threshold value, the service of the target reverse thrust meets the requirement of the user, and the weighted edge between the service and the target node is connected; the RGPS associated network is continuously modified through the feedback correction mechanism, so that the related services which can well meet the requirements of users are found out, the searched service sets can be sequenced, and scientific basis is provided for the customized recommendation of the services.
3. The on-demand service aggregation and recommendation method based on RGPS meta-model as claimed in claim 1, wherein: the key point of expressing aggregated services by using a directed graph model is how to obtain accurate RGPS elements from the demands, namely, the demands are analyzed and modeled by using an LSTM neural network.
The LSTM neural network processes the core idea of service recommendation: the core of the LSTM is "cell state", which functions as a memory space in the whole model, and the memory space changes with time, and the memory space cannot control which users' demand information is memorized, so that it is a control gate that really plays a control role.
(1) The input node receives the output of the hidden node of the previous time point and the current input as the input, and then passes through an activation function of tanh;
(2) an input gate: the gate input is the output of the hidden node at the last time point and the current input, and the activation function is sigmoid (the reason is that the output of the sigmoid is between 0 and 1, and the multiplication of the output of the input gate and the output of the input node can play a role in controlling the information quantity);
(3) internal state node: the input is the current input filtered by the input gate and the internal state node output of the previous time point;
(4) forgetting to remember the door: the function of controlling the internal state information is realized, and the Gate is opened (set to 1) or closed (set to 0) through the training parameters to protect the Cell;
(5) an output gate: the function of controlling output information is realized, the input of the gate is the output of the hidden node at the last time point and the current input, and the activation function is sigmoid;
for example, in the field of 'travel', a user inputs a demand 'i want to reserve a train ticket on the day, reserve a lodging and provide urban traffic information'; the example sentences are subjected to machine recognition and user requirements are marked off, each vertical rectangular frame of the LSTM model in the attached figure 2 represents a hidden layer of each iteration, each hidden layer is provided with a plurality of neurons, each neuron performs linear matrix operation on an input vector, and an output result (for example, a tanh () function) is generated through nonlinear operation. In each iteration, the output of the previous iteration varies with the word vector of the next word in the document, XtIs the input to the hidden layer and the hidden layer will produce the predicted output value and the output feature vector H that is provided to the hidden layer of the next layert。
The expression formula of each layer model of the LSTM neural network is as follows:
an input gate: the function is to control the output of the cell state:
memory cell: for the time t-1 → t, the memory cell information change process is represented as t-1 time state and is obtained by filtering with forgetting gate
The stored information and the input door information acquired at the time t are compared
Addition gives the transition in cell state:
output of cell status:
then the inverse of the residual is conducted to the output gate to modify the function:
the following error propagates to the cell state:
error passes to forget gate:
after the residual conduction is finished, the residual is directly derived from the weight
Example 1
The following are specific examples of the application of the present invention:
in the specific field of travel, accurate service recommendation is carried out on user requirements, and the method comprises the following steps: user requirements are identified according to the algorithm and a correlation network is established, semantic decomposition is carried out on the user requirements through a requirement acquisition and analysis tool developed by the subject group, 4 roles corresponding to the requirements are provided, namely 'train passenger', 'accommodation person', 'consultant' and 'tourist', and 5 targets are respectively: "order train tickets", "booking hotels", "urban traffic", "urban weather", "urban scenic spots";
Step 2 is executed, if the goal is realized, the process decomposition can be carried out, for example, the process of ordering the train ticket can be decomposed into two processes of inquiring the ticket and purchasing the ticket; "booking a hotel" may be broken down into "querying a hotel", "online payment system"; "urban traffic" can be decomposed into "calling maps", "navigation route generation"; "City weather" can be broken down into "Call weather forecast"; the "city attractions" can be broken down into "generate hot attractions" and "ticket reservations".
Step 3 is executed, the service recommendation process is started by the role layer, the purpose of the train passenger is to order the train ticket, the process flow is the steps of inquiring the ticket price and purchasing the ticket, and the sub-process of purchasing the ticket can be divided into providing the ticket information and paying online. Through roles, targets and processes, the services can be classified accurately, through a sequential logic diagram of an RGPS (geographic grouping service) association network, in order to recommend better services such as 'tourist' roles to users, recommended sub-services can be selected from three service clusters of 'payment system', 'navigation software' and 'travel services', and the method can effectively improve the rationality of service recommendation.
After step 3, the model is evaluated with the test set after the model parameters are substantially fixed.
It is to be understood that the above description is not intended to limit the present invention, and the present invention is not limited to the above examples, and those skilled in the art may make modifications, alterations, additions or substitutions within the spirit and scope of the present invention.
Claims (3)
1. An on-demand service aggregation and recommendation method based on an RGPS meta-model is characterized by comprising the following steps:
step 1, according to the target set related in the specific field problem, firstly adding the Association with the specific targetSDIThe role of the relationship and the association between the two, then the relationship between the role and the target is added, and the ontology (O), the role and the target model (R) are respectively extracted&G) Common core metadata and management information of the process model (P) and the service model (S) establish corresponding SDIs for different types of models so as to promote cross-domain or cross-system query and multi-granularity multiplexing of heterogeneous information models;
step 2, constructing corresponding SDI according to different types of models obtained in the step 1, wherein the realization method is as follows,
a user demand driven RGPS associated network generation step is given, wherein
Evaluation of the service (QoS value) if
If the satisfaction degree of the user to the service is above the minimum QoS threshold value, the service of role reverse thrust meets the requirement of the user, and the weighted edge between the service and the role node is connected; in the same way, if
If the satisfaction degree of the user to the service is above the minimum QoS threshold value, the service of the target reverse thrust meets the requirement of the user, and the weighted edge between the service and the target node is connected;
and 3, analyzing the user requirements and recommending related potential services by utilizing R and G reverse-thrust service ways and combining an LSTM neural network model, so that new instant services can be recommended for the user.
2. The on-demand service aggregation and recommendation method based on RGPS meta-model as claimed in claim 1, wherein: in step 2, a user demand driven RGPS associated network generation step is given, wherein
Evaluation of the service (QoS value) if
If the satisfaction degree of the user to the service is above the minimum QoS threshold value, the service of role reverse thrust meets the requirement of the user, and the weighted edge between the service and the role node is connected; in the same way, if
If the satisfaction degree of the user to the service is above the minimum QoS threshold value, the service of the target reverse thrust meets the requirement of the user, and the weighted edge between the service and the target node is connected; the RGPS associated network is continuously modified through the feedback correction mechanism, so that the related services which can well meet the requirements of users are found out, the searched service sets can be sequenced, and scientific basis is provided for the customized recommendation of the services.
3. The on-demand service aggregation and recommendation method based on RGPS meta-model as claimed in claim 1, wherein: the key point of expressing aggregated services by using a directed graph model is how to obtain accurate RGPS elements from the demands, namely, the demands are analyzed and modeled by using an LSTM neural network.
The LSTM neural network processes the core idea of service recommendation: the core of the LSTM is "cell state", which functions as a memory space in the whole model, and the memory space changes with time, and the memory space cannot control which users' demand information is memorized, so that it is a control gate that really plays a control role.
(1) The input node receives the output of the hidden node of the previous time point and the current input as the input, and then passes through an activation function of tanh;
(2) an input gate: the gate input is the output of the hidden node at the last time point and the current input, and the activation function is sigmoid (the reason is that the output of the sigmoid is between 0 and 1, and the multiplication of the output of the input gate and the output of the input node can play a role in controlling the information quantity);
(3) internal state node: the input is the current input filtered by the input gate and the internal state node output of the previous time point;
(4) forgetting to remember the door: the function of controlling the internal state information is realized, and the Gate is opened (set to 1) or closed (set to 0) through the training parameters to protect the Cell;
(5) an output gate: the function of controlling output information is realized, the input of the gate is the output of the hidden node at the last time point and the current input, and the activation function is sigmoid;
for example, in the field of 'travel', a user inputs a demand 'i want to reserve a train ticket on the day, reserve a lodging and provide urban traffic information'; the example sentences are subjected to machine recognition and user requirements are marked off, each vertical rectangular frame of the LSTM model in the attached figure 2 represents a hidden layer of each iteration, each hidden layer is provided with a plurality of neurons, each neuron performs linear matrix operation on an input vector, and an output result (for example, a tanh () function) is generated through nonlinear operation. In each iteration, the output of the previous iteration varies with the word vector of the next word in the document, XtIs the input to the hidden layer and the hidden layer will produce the predicted output value and the output feature vector H that is provided to the hidden layer of the next layert。
The expression formula of each layer model of the LSTM neural network is as follows:
an input gate: the function is to control the output of the cell state:
memory cell: for the time t-1 → t, the memory cell information change process is represented as t-1 time state and is obtained by filtering with forgetting gate
The stored information and the input door information acquired at the time t are compared
Addition gives the transition in cell state:
output of cell status:
then the inverse of the residual is conducted to the output gate to modify the function:
the following error propagates to the cell state:
error passes to forget gate:
after the residual conduction is finished, the residual is directly derived from the weight
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