Ppartner training service analysis and recommendation method based on big data information processing

Abstract

The invention relates to the technical field of partner training service recommendation, and discloses a partner training service analysis and recommendation method based on big data information processing, and the method comprises the steps: collecting first feature data of a current user and second feature data of N coaches; preprocessing the first feature data and the second feature data to obtain third feature data and fourth feature data; constructing graph structure data according to the third feature data and the fourth feature data, wherein the graph structure data comprises nodes, initial features of the nodes and edges between the nodes; a partner training service recommendation model is constructed and trained, the input of the partner training service recommendation model is graph structure data, and the output value represents the matching value between the current user and the N coaches; and sorting the matching values between the current user and the N coaches from large to small, and obtaining the first M coaches as a partner training recommendation list.

Description

Technical Field

[0001] The present invention relates to the technical field of training service recommendation, and more specifically, to a training service analysis and recommendation method based on big data information processing. Background Art

[0002] With the gradual improvement of people's health awareness, the importance of sports has become increasingly prominent. Faced with the current situation of lack of professional guidance and companionship, some people may fail to achieve the expected results due to improper methods when participating in sports, or even suffer injuries. Therefore, providing professional and high-quality sports training services has become an important way and means to meet the public health needs.

[0003] However, it is not easy to make a suitable choice among the numerous coaching services. Users often face the problem of information asymmetry during the selection process. For example, the coach's teaching qualifications and service quality are not transparent, which may lead to the selected coach not matching their own conditions.

[0004] Therefore, there is an urgent need for a training service analysis and recommendation method to solve the above problems. Summary of the invention

[0005] The present invention provides a training service analysis and recommendation method based on big data information processing, which solves the technical problems of mismatched training partners and low service quality caused by information asymmetry when users select training partners in related technologies.

[0006] The present invention provides a training service analysis and recommendation method based on big data information processing, comprising: step S101, collecting first characteristic data of the current user and second characteristic data of N coaches, wherein N is a custom parameter, and N is set according to the number of coaches; The first characteristic data includes: age, gender, occupation, sports preference and injury status; The second characteristic data includes: age, gender, years of work experience, sports specialty, number of professional certificates and service ratings; Step S102, preprocessing the first characteristic data and the second characteristic data to obtain third characteristic data and fourth characteristic data respectively; Step S103, constructing graph structure data according to the third characteristic data and the fourth characteristic data; Graph structure data includes: nodes, initial features of nodes, and edges between nodes; The nodes include: a first node and a second node; The first node establishes a mapping relationship with the current user; The second node establishes a mapping relationship with the coach; The initial feature of the first node is represented by the third feature data of the current user that establishes a mapping relationship with the first node; The initial feature of the second node is represented by the fourth feature data of the coach that establishes a mapping relationship with the second node; Step S104, constructing and training a training service recommendation model; The input of the training service recommendation model is graph structure data, and the output value represents the matching value between the current user and N coaches; Step S105, sorting the matching values ​​between the current user and the N coaches in descending order, obtaining the first M coaches as a recommended list of sparring partners, where M is a custom parameter.

[0007] Furthermore, the pre-processing step includes: Step S201, filling missing values ​​in the first feature data and the second feature data by using the most frequently occurring value therein; Step S202, performing replacement processing on the abnormal values ​​in the first feature data and the second feature data by calculating the mean of all non-abnormal values; Step S203: normalize the first feature data and the second feature data using a z-score normalization method.

[0008] Furthermore, gender includes: male and female, and gender is represented by real number coding; occupation includes: doctors, teachers, engineers, students and service industry personnel, and occupation is represented by one-hot coding; sports preference and sports specialty are the same feature, including: basketball, billiards, running, fitness and table tennis, represented by one-hot coding; injury status includes: with injury and without injury, and injury status is represented by real number coding; service rating is represented by real number coding, and is represented by the user rating of the coach collected by the platform.

[0009] Furthermore, the ways to construct edges include: Build an edge between the first node and the second node, including: When a user follows a coach, a one-way edge is constructed between the user and the coach, that is, from the user to the coach; When the user's sport preference is the same as the coach's sport specialty, a bidirectional edge is constructed between the user and the coach, and the weight is represented by the same quantity in the sport preference and the sport specialty; When a user has made an appointment or studied a course with the coach, a one-way edge is constructed between the user and the coach, that is, from the user to the coach, and the weight is represented by the number of appointments or studies; When a user interacts with a coach, a bidirectional edge is built between the user and the coach. The weight is represented by the number of interactions. The types of interactions include: comments, likes, and private messages. Construct an edge between the second node and the second node, including: When different coaches have the same sports expertise, a bidirectional edge is constructed between the coaches, and the weight is represented by the same quantity in the sports expertise.

[0010] Furthermore, the training service recommendation model includes: a first hidden layer, a second hidden layer, a third hidden layer and an extraction layer; The first hidden layer inputs the initial feature of the first node of the graph structure data and outputs the first updated feature; The second hidden layer includes N first units, the nth first unit inputs the initial feature of the nth second node of the graph structure data, and outputs the second updated feature, where 1≤n≤N; N first units share weight parameters and bias parameters; The first updated feature and the second updated feature have the same size; The third hidden layer inputs the graph structure data and outputs a graph update matrix, wherein each row vector of the graph update matrix corresponds to a third updated feature of the first node or the second node; The extraction layer includes N extractors, the nth extractor is used to extract the third updated feature corresponding to the nth second node and input it into the first classifier, and the classification space of the first classifier represents the matching value between the current user and the nth coach.

[0011] Further, the first hidden layer and the second hidden layer are used to unify the dimensions of the initial features of the first node and the second node.

[0012] Furthermore, the calculation formula of the first hidden layer is: ; in represents the first updated feature of the first hidden layer output, represents the initial features of the first node of the first hidden layer input, , , and are the first weight parameter, the second weight parameter, the first bias parameter and the second bias parameter, ReLU represents the ReLU activation function, and sigmoid represents the sigmoid activation function.

[0013] Furthermore, the calculation formula of the nth first unit of the second hidden layer is: ; in represents the second updated feature of the output of the nth first unit of the second hidden layer, represents the initial features of the second node of the nth first unit input of the second hidden layer, , , and are the third weight parameter, the fourth weight parameter, the third bias parameter and the fourth bias parameter, ReLU represents the ReLU activation function, and sigmoid represents the sigmoid activation function.

[0014] Furthermore, the calculation formula of the third hidden layer is: ; ; ; ; Where S represents the graph update matrix output by the third hidden layer, represents the third updated feature of node i, and Both represent the update characteristics of the nodes. represents the normalized attention weight between node i and its adjacent node j. represents the set of nodes adjacent to node i, ReLU represents the ReLU activation function, represents the attention coefficient between node i and its adjacent node j, represents the attention coefficient between node i and its adjacent node k, LeakyReLU represents the LeakyReLU activation function, exp represents the exponential function, T represents the transposition operation, Represents a splicing operation, , and They respectively represent the fifth weight parameter, the sixth weight parameter and the seventh weight parameter, and the update characteristics of the above-mentioned nodes include: a first update characteristic of the first node and a second update characteristic of the second node.

[0015] Furthermore, it should be noted that the weight parameters and bias parameters in the accompanying service recommendation model are all learnable parameters. During the training process of the accompanying service recommendation model, the gradient information of each weight parameter and bias parameter is calculated through the loss function and the chain rule, and the weight parameters and bias parameters in the accompanying service recommendation model are updated according to the gradient information through back propagation and gradient descent algorithm.

[0016] Furthermore, the loss function of the training service recommendation model is a cross entropy loss function.

[0017] Furthermore, sample labels of training samples used to train the training service recommendation model are obtained through manual labeling.

[0018] The beneficial effect of the present invention is that the present invention constructs a training service recommendation model to analyze the interaction between users and coaches and the relationship between coaches, thereby obtaining the matching value of each coach for the user, and then recommending high-quality and highly matched training services to users, thereby improving user satisfaction. Figure 1 is a flow chart of the training service analysis and recommendation method based on big data information processing of the present invention; Figure 2 is a flow chart of preprocessing characteristic data of a user and a coach according to the present invention; Figure 3 It is a schematic diagram of the method of constructing edges in the graph structure data of the present invention. DETAILED DESCRIPTION

[0019] The subject matter described herein will now be discussed with reference to example embodiments. It should be understood that the discussion of these embodiments is only to enable those skilled in the art to better understand and implement the subject matter described herein, and the functions and arrangements of the elements discussed may be changed without departing from the scope of protection of the contents of this specification. Each example may omit, replace or add various processes or components as needed. In addition, the features described relative to some examples may also be combined in other examples.

[0020] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in one or more embodiments of the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The words "first", "second" and similar words used in one or more embodiments of the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0021] like Figure 1 to Figure 3 As shown, the training service analysis and recommendation method based on big data information processing includes: step S101, collecting the first characteristic data of the current user and the second characteristic data of N coaches, where N is a custom parameter, and N is set according to the number of coaches; The first characteristic data includes: age, gender, occupation, sports preference and injury status; The second characteristic data includes: age, gender, years of work experience, sports specialty, number of professional certificates and service ratings; Step S102, preprocessing the first characteristic data and the second characteristic data to obtain third characteristic data and fourth characteristic data respectively; Step S103, constructing graph structure data according to the third characteristic data and the fourth characteristic data; Graph structure data includes: nodes, initial features of nodes, and edges between nodes; The nodes include: a first node and a second node; The first node establishes a mapping relationship with the current user; The second node establishes a mapping relationship with the coach; The initial feature of the first node is represented by the third feature data of the current user that establishes a mapping relationship with the first node; The initial feature of the second node is represented by the fourth feature data of the coach that establishes a mapping relationship with the second node; Step S104, constructing and training a training service recommendation model; The input of the training service recommendation model is graph structure data, and the output value represents the matching value between the current user and N coaches; Step S105, sorting the matching values ​​between the current user and the N coaches in descending order, obtaining the first M coaches as a recommended list of sparring partners, where M is a custom parameter, preferably, M is set to 10.

[0022] In one embodiment of the present invention, the pre-processing step includes: Step S201, filling missing values ​​in the first feature data and the second feature data by using the most frequently occurring value therein; Step S202, replacing the abnormal values ​​in the first feature data and the second feature data by calculating the mean of all non-abnormal values. For example, if the age exceeds a reasonable range, is a negative number, or exceeds 100 years old, the average value of the remaining ages in the first feature data is used for replacement; Step S203: normalize the first feature data and the second feature data using a z-score normalization method.

[0023] In one embodiment of the present invention, gender includes: male and female, and gender is represented by real number coding, for example, male is represented by 1; occupation includes: doctor, teacher, engineer, student and service industry personnel, and occupation is represented by one-hot coding, for example, teacher is represented by [0, 1, 0, 0, 0]; sports preference and sports specialty are the same feature, including: basketball, billiards, running, fitness and table tennis, represented by one-hot coding, for example, running is represented by [0, 0, 1, 0, 0], and basketball and billiards are represented by [1, 1, 0, 0, 0]; injury status includes: with injury and without injury, and injury status is represented by real number coding, for example, with injury is represented by 1; service score is represented by real number coding, and is represented by the user score of the coach collected by the platform.

[0024] In one embodiment of the present invention, the method of constructing an edge includes: Build an edge between the first node and the second node, including: When a user follows a coach, a one-way edge is constructed between the user and the coach, that is, from the user to the coach. Preferably, the weight is set to 1. When the user's sports preference is the same as the coach's sports specialty, a bidirectional edge is constructed between the user and the coach, and the weight is represented by the same number in the sports preference and the sports specialty. For example, the user's sports preference is basketball and billiards, and the coach's sports specialties are billiards and running. The user's and the coach's sports preferences and sports specialties both include billiards, so an edge is constructed between the user and the coach, and the weight is set to 1; When a user has made an appointment or studied a course with the coach, a one-way edge is constructed between the user and the coach, that is, from the user to the coach, and the weight is represented by the number of appointments or studies; When the user has interacted with the coach, a bidirectional edge is constructed between the user and the coach. The weight is represented by the number of interactions. The types of interactions include: comments, likes, and private messages. Preferably, if the number of interactions is 1-3, the weight is 1, if the number of interactions is 4-10, the weight is 3, and if the number of interactions is more than 10, the weight is 5. For example, if the current user has privately communicated with the coach, and the number of private messages is 3, then an edge is constructed between the current user and the coach, and the weight is set to 1; Construct an edge between the second node and the second node, including: When different coaches have the same sports expertise, a bidirectional edge is constructed between the coaches, and the weight is represented by the same quantity in the sports expertise.

[0025] In one embodiment of the present invention, the training service recommendation model includes: a first hidden layer, a second hidden layer, a third hidden layer and an extraction layer; The first hidden layer inputs the initial feature of the first node of the graph structure data and outputs the first updated feature; The second hidden layer includes N first units, the nth first unit inputs the initial feature of the nth second node of the graph structure data, and outputs the second updated feature, where 1≤n≤N; N first units share weight parameters and bias parameters; The first updated feature and the second updated feature have the same size; The third hidden layer inputs the graph structure data and outputs a graph update matrix, wherein each row vector of the graph update matrix corresponds to a third updated feature of the first node or the second node; The extraction layer includes N extractors, the nth extractor is used to extract the third updated feature corresponding to the nth second node and input it into the first classifier, and the classification space of the first classifier represents the matching value between the current user and the nth coach.

[0026] The first hidden layer and the second hidden layer are used to unify the dimensions of the initial features of the first node and the second node.

[0027] In one embodiment of the present invention, the calculation formula of the first hidden layer is: in represents the first updated feature of the first hidden layer output, represents the initial features of the first node of the first hidden layer input, , , and are the first weight parameter, the second weight parameter, the first bias parameter and the second bias parameter, ReLU represents the ReLU activation function, and sigmoid represents the sigmoid activation function. In one embodiment of the present invention, the calculation formula of the nth first unit of the second hidden layer is: ; in represents the second updated feature of the output of the nth first unit of the second hidden layer, represents the initial characteristics of the second node of the nth first unit input, , , and are the third weight parameter, the fourth weight parameter, the third bias parameter and the fourth bias parameter, ReLU represents the ReLU activation function, and sigmoid represents the sigmoid activation function.

[0028] It should be noted that the first node of the first hidden layer input The initial feature is a 1*5 feature vector, and the first weight parameter is the first feature matrix of 5*16, and the second weight parameter The second feature matrix is ​​16*32, and the first updated feature is obtained by calculation is a 1*32 feature vector; the second node of the nth first unit input of the second hidden layer The initial feature is a 1*6 feature vector, and the third weight parameter is the third characteristic matrix of 6*16, and the fourth weight parameter The fourth characteristic matrix of 16*32 is calculated to obtain the second updated characteristic of the nth first unit output of the second hidden layer It is a 1*32 feature vector.

[0029] In one embodiment of the present invention, the calculation formula of the third hidden layer is: ; ; ; ; Where S represents the graph update matrix output by the third hidden layer, represents the third updated feature of node i, and Both represent the update characteristics of the nodes. represents the normalized attention weight between node i and its adjacent node j. represents the set of nodes adjacent to node i, ReLU represents the ReLU activation function, represents the attention coefficient between node i and its adjacent node j, represents the attention coefficient between node i and its adjacent node k, LeakyReLU represents the LeakyReLU activation function, exp represents the exponential function, T represents the transposition operation, Represents a splicing operation, , and Respectively represent the fifth weight parameter, the sixth weight parameter and the seventh weight parameter, and the update features of the above nodes include: the first update feature of the first node and the second update feature of the second node. It should be noted that the weight parameters and bias parameters in the training service recommendation model are all learnable parameters. During the training of the training service recommendation model, the gradient information of each weight parameter and bias parameter is calculated by the loss function and the chain rule, and the weight parameters and bias parameters in the training service recommendation model are updated according to the gradient information through back propagation and gradient descent algorithms.

[0030] In one embodiment of the present invention, the loss function of the training service recommendation model is a cross entropy loss function.

[0031] In one embodiment of the present invention, sample labels of training samples used to train the training service recommendation model are obtained by manual labeling.

[0032] The above describes an embodiment of the present embodiment, but the present embodiment is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present embodiment, ordinary technicians in this field can also make many forms, all of which are within the protection of the present embodiment.

Claims

1. A training service analysis and recommendation method based on big data information processing, characterized in that: include: Step S101, collecting the first characteristic data of the current user and the second characteristic data of N coaches, where N is a custom parameter and N is set according to the number of coaches; The first characteristic data includes: age, gender, occupation, sports preference and injury status; The second characteristic data includes: age, gender, years of work experience, sports specialty, number of professional certificates and service ratings; Step S102, preprocessing the first characteristic data and the second characteristic data to obtain third characteristic data and fourth characteristic data respectively; Step S103, constructing graph structure data according to the third characteristic data and the fourth characteristic data; Graph structure data includes: nodes, initial features of nodes, and edges between nodes; The nodes include: a first node and a second node; The first node establishes a mapping relationship with the current user; The second node establishes a mapping relationship with the coach; The initial feature of the first node is represented by the third feature data of the current user that establishes a mapping relationship with the first node; The initial feature of the second node is represented by the fourth feature data of the coach that establishes a mapping relationship with the second node; Step S104, constructing and training a training service recommendation model; The input of the training service recommendation model is graph structure data, and the output value represents the matching value between the current user and N coaches; Step S105, sorting the matching values ​​between the current user and the N coaches in descending order, obtaining the first M coaches as a recommended list of sparring partners, where M is a custom parameter.

2. The method for analyzing and recommending training services based on big data information processing according to claim 1, characterized in that: The preprocessing steps include: Step S201, filling missing values ​​in the first feature data and the second feature data by using the most frequently occurring value therein; Step S202, performing replacement processing on the abnormal values ​​in the first feature data and the second feature data by calculating the mean of all non-abnormal values; Step S203: normalize the first feature data and the second feature data using a z-score normalization method.

3. The method for analyzing and recommending training services based on big data information processing according to claim 1, characterized in that: Gender includes: Male and female, gender is represented by real number coding; Occupations include: doctors, teachers, engineers, students and service industry personnel, and occupations are represented by one-hot coding; sports preferences and sports specialties are the same feature, including: basketball, billiards, running, fitness and table tennis, represented by one-hot coding; injury conditions include: with injuries and without injuries, and injury conditions are represented by real number coding; service ratings are represented by real number coding, which is represented by the user ratings of coaches collected by the platform.

4. The method for analyzing and recommending training services based on big data information processing according to claim 1, characterized in that: Ways to construct edges include: Build an edge between the first node and the second node, including: When a user follows a coach, a one-way edge is constructed between the user and the coach, that is, from the user to the coach; When the user's sport preference is the same as the coach's sport specialty, a bidirectional edge is constructed between the user and the coach, and the weight is represented by the same quantity in the sport preference and the sport specialty; When a user has made an appointment or studied a course with the coach, a one-way edge is constructed between the user and the coach, that is, from the user to the coach, and the weight is represented by the number of appointments or studies; When a user interacts with a coach, a bidirectional edge is built between the user and the coach. The weight is represented by the number of interactions. The types of interactions include: comments, likes, and private messages. Construct an edge between the second node and the second node, including: When different coaches have the same sports expertise, a bidirectional edge is constructed between the coaches, and the weight is represented by the same quantity in the sports expertise.

5. The method for analyzing and recommending training services based on big data information processing according to claim 1, characterized in that: The training service recommendation model includes: a first hidden layer, a second hidden layer, a third hidden layer and an extraction layer; The first hidden layer inputs the initial feature of the first node of the graph structure data and outputs the first updated feature; The second hidden layer includes N first units, the nth first unit inputs the initial feature of the nth second node of the graph structure data, and outputs the second updated feature, where 1≤n≤N; N first units share weight parameters and bias parameters; The first updated feature and the second updated feature have the same size; The third hidden layer inputs the graph structure data and outputs a graph update matrix, wherein each row vector of the graph update matrix corresponds to a third updated feature of the first node or the second node; The extraction layer includes N extractors, the nth extractor is used to extract the third updated feature corresponding to the nth second node and input it into the first classifier, and the classification space of the first classifier represents the matching value between the current user and the nth coach.

6. The method for analyzing and recommending training services based on big data information processing according to claim 5 is characterized in that: The calculation formula for the first hidden layer is: ； in represents the first updated feature of the first hidden layer output, represents the initial features of the first node of the first hidden layer input, , , and are the first weight parameter, the second weight parameter, the first bias parameter and the second bias parameter, ReLU represents the ReLU activation function, and sigmoid represents the sigmoid activation function.

7. The method for analyzing and recommending training services based on big data information processing according to claim 5 is characterized in that: The calculation formula for the nth first unit of the second hidden layer is: ； in represents the second updated feature of the output of the nth first unit of the second hidden layer, represents the initial features of the second node of the nth first unit input of the second hidden layer, , , and are the third weight parameter, the fourth weight parameter, the third bias parameter and the fourth bias parameter, ReLU represents the ReLU activation function, and sigmoid represents the sigmoid activation function.

8. The method for analyzing and recommending training services based on big data information processing according to claim 5, characterized in that: The calculation formula for the third hidden layer is: ； ； ； ； Where S represents the graph update matrix output by the third hidden layer, represents the third updated feature of node i, and Both represent the update characteristics of the nodes. represents the normalized attention weight between node i and its adjacent node j. represents the set of nodes adjacent to node i, ReLU represents the ReLU activation function, represents the attention coefficient between node i and its adjacent node j, represents the attention coefficient between node i and its adjacent node k, LeakyReLU represents the LeakyReLU activation function, exp represents the exponential function, T represents the transposition operation, Represents a splicing operation, , and They respectively represent the fifth weight parameter, the sixth weight parameter and the seventh weight parameter, and the update characteristics of the above-mentioned nodes include: a first update characteristic of the first node and a second update characteristic of the second node.

9. The method for analyzing and recommending training services based on big data information processing according to claim 1, characterized in that: The loss function of the training service recommendation model is the cross entropy loss function.

10. The method for analyzing and recommending training services based on big data information processing according to claim 1, characterized in that: The sample labels of the training samples used to train the training service recommendation model are obtained through manual labeling.

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