# Talent Management Recommendation Technology Based on Deep Learning

Authors: Yingying Huo
Journal: Mathematical Problems in Engineering
 (2022)

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## Abstract

Nowadays, with the vigorous development of information management technology, talent management has become a hot field that scholars pay attention to. The flow of talent between companies has become increasingly frequent. A large number of cooperative behaviors have produced a large number of cooperative results and subsequently brought a large amount of data on what to do. A huge network of collaborators has also been quietly formed, and how to mine valuable information from it has become a research hotspot, among which talent recommendation is one of the most important topics. Talent recommendation, when an enterprise introduces high-quality talents, provides valuable reference suggestions and selects candidates. When introducing talents, enterprises should not only consider the ability level of talents but also consider the cooperative relationship between them and enterprise personnel. Therefore, it is necessary to analyze the network of partners to find out the rules. There are only author nodes in the isomorphic collaborator network, and the connection between nodes is the cooperative relationship. On this basis, this paper constructs a heterogeneous collaborator network; that is, there are multiple types of nodes and connections in the network. The main research problem of this paper is to find an indicator to measure the strength of association between scholars in the collaborator network and to recommend potential academic talents for enterprises. Based on the data of academic research cooperation network, we carried out sufficient experiments to demonstrate the effectiveness of the heterogeneous cooperation network model proposed in this paper.


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## Body

## 1. Introduction

Talent management refers to the expectations of the human capital required by the organization and the plans to meet these anticipated needs [1–3]. Over the past decade or so, talent management has grown in importance for both the industry and academia [4–10]. Good talent management is about empowering employees with good skills, knowledge, cognitive abilities, and the potential to do a good job [11–18]. Talent management is also an important and necessary skill for employees themselves. Finding good and talented people is not difficult, but ensuring they are willing to continue serving the same organization is a challenge. Nowadays, with the deepening of cooperation and exchanges between enterprises, cross-enterprise cooperation and exchanges have also promoted the successful implementation of industry-university-research projects [19, 20]. A large number of cooperative behaviors have produced a large number of cooperative results and subsequently brought a large amount of cooperation and exchange data [21–23]. A huge network of collaborators has also been quietly formed, and how to mine valuable information from it has become a research hotspot, among which talent recommendation is one of the most important topics. Talent recommendation, when an enterprise introduces high-quality talents, provides valuable reference suggestions and selects candidates. In addition to measuring the degree of conformity of the candidates’ personal capabilities with the needs of the enterprise, the partnership between the candidates and the enterprise should also be considered. Therefore, it is necessary to analyze the network of partners to find out the rules [4–7]. There are only candidates’ nodes in the homogeneous network, and the connection between the nodes is the cooperative relationship. On this basis, this paper constructs a heterogeneous collaborator network; that is, there are multiple types of nodes and connections in the network. The main research problem of this paper is to find an indicator to measure the strength of association between scholars in the collaborator network and to recommend potential academic talents for enterprises. In summary, the main contributions of this paper are as follows:(1)
Determine the edge weight of the homogeneous partner network by introducing the cooperation effect, propose a link-based talent recommendation model based on the homogeneous cooperation network, give the definition and calculation method of cooperation trust, and discuss cooperation under three constraints’ relationship, and the recommended probability calculation methods are given, respectively.(2)
Based on the heterogeneous collaborator network, the PathSim algorithm based on journal partition is proposed, and the experiment proves that the result of the algorithm is more in line with the actual situation. Considering the semantic information of meta-paths, this paper integrates two meta-paths to measure the similarity between candidates.(3)
Adopt the authoritative ranking algorithm based on the dual-type network to calculate the authoritative value of candidates in the institution and propose a talent recommendation model based on the authoritative ranking of candidates. Experimental results show that our proposed model outperforms other baselines.


## 2. Related Work

The importance of scientific research cooperation has been mentioned above, and research on cooperation networks has also emerged. In foreign countries, Watts and Strogatz [8] proposed a small network-coupled dynamical system network for small social network analysis, which enhances the signal propagation speed, computing power, and synchronization in the network. Newman [9] proposed the social network structure of scientific research collaborators and constructed a cooperation network through the document archive to form a small world. Through the small world, scientists can show the path of short-term cooperation, analyze the cooperation between their research fields, and clear differences in patterns. Barabâsi et al. [10] proposed that, by synthesizing the evolution of scientists’ cooperative complex network systems, the dynamics of topologies, and their evolutionary mechanisms through a synthesis of numerical and roles, recommendations or other services that are developed based on co-authors’ networks are also beneficial for the study of other complex networks. Wagner and Leydesdorff [11] hypothesized that the collaboration of scientific researchers is a self-organizing network, applied network analysis tools to show that the growth of international collaborators is based on the organizing principle of limited attachment, and explored the impact of institutional constraints on the self-organizing mechanism. Yoshikane et al. [12] analyzed the activities of scientific researchers from the perspective of the global network structure of scientists and collaborators and compared two kinds of importance of researchers in the network between computer science theory and application. Krichel and Bakkalbasi [13] used RePEc data to construct a high-quality author database and used these data to examine how different network structures affect economists’ rankings through author centrality. Choil et al. [14] studied how co-authorship is initiated at the organizational and sectoral levels of international cooperation networks and showed that the university sector in rich English-speaking countries and the government sector in non-English-speaking nonrich countries are in the OECD that played a key role in international cooperation between countries. Whittington [15] studied the differences in scientific collaboration positioning and collaborator characteristics between men and women, as well as the impact of networks on scientists’ future productivity, and found that some network benefits depend on gender. Rubin et al. [16] used a game-theoretic model to delve into the dynamics of discrimination and academic cooperation and found that, in cooperative networks, minority groups may end up being discriminated against when cooperating. Ribeiro et al. [17] investigated the extent of the development of international scientific research cooperation and the structure of cooperation, showing that international cooperation has been growing in a particular pattern, forming a disproportionate network. For heterogeneous information networks, Sun et al. [18] proposed the problem of co-authorship prediction in heterogeneous document networks, proposed a meta-path-based topological feature, and defined a metric method on the topological feature. After that, Sun et al. [19] proposed a similarity measurement algorithm based on meta-path and gave an optimization algorithm that uses pruning to reduce the time complexity. Krichel et al. [13] proposed a combination of classification and ranking algorithms to improve the performance of the classifier, which can make the ranking results more accurate.


## 3. The Proposed System

This section first introduces the definition of the collaborator network and uses the cooperation relationship matrix to describe the collaborator network; it analyzes and gives the definition of the importance of researchers in the collaborator network and proposes a method for screening high-level researchers based on this; secondly, based on the isomorphic collaborator network, the calculation method of the cooperation edge weight is given, and the similarity measurement method of the academic level of researchers and the cooperation intensity measurement method between universities are analyzed and given. Finally, the recommendation probability model is given for the three cooperation situations, respectively. The second half of this section is mainly based on the analysis of the heterogeneous collaborator network. First, the related concepts of the heterogeneous collaborator network are given, and the similarity algorithm based on the meta-path is introduced. On this basis, the similarity of the impact factors of journals in different regions is proposed. Secondly, based on the semantics of different meta-paths, a more convincing and more realistic meta-path is given; then, a calculation method of authority value based on dual-type networks is proposed, and finally, talent recommendation is discussed and a talent recommendation model is given.


### 3.1. Analysis of the Importance of Partner Networks

In the collaborator network, the scholar node is the bridge of academic links. The higher the importance, the more it can promote the communication and development of the academic circle. There are many evaluation indicators for evaluating a scholar, such as theh-index, which is a method for evaluating the academic level of a scholar, which means that most h papers have been cited at least h times.As shown in Figure1, utilizing CiteSpace to display the original partner network, it can be seen that the nodes closer to the center are more active, and the nodes at the edge of the network are scattered and isolated. Before the experiment, it is necessary to remove the less important nodes in the network, which can not only reduce the time complexity of the subsequent experiments but also filter the nodes that do not meet the requirements in advance because, when making talent recommendation, it tends to recommend researchers with higher academic ability. The researchers at the edge and those close to the center have a big difference in academic ability and scientific research communication, and the possibility of being recommended is low. Only researchers with higher academic ability are recommended to be more versatile and more in line with the actual situation.Figure 1
Original collaborator network.


### 3.2. Betweenness Acceleration Algorithm

For the collaboration network, if the betweenness of the entire network is calculated according to the betweenness definition, the time complexity is high [24–31]. Brandes [20] proposed an accelerated algorithm for betweenness. The algorithm is based on breadth-first search. The principle of this algorithm is described below. For a given graph G=E,V, define pair-dependency; the formula is as follows:(1)δstv=δstvδst,where δstσst is the number of shortest paths from node s to node t and δstv is the number of shortest paths from node s to node t passing through node v.Then, the betweennessBCv of node v is denoted as follows:(2)BCv=∑s≠t≠vδstv.For computingδstv, we introduce predecessor node denoted as follows:(3)Psv=u∈V:u,v∈E,dGs,v=dGs,u+wu,v.Next, we utilize (4) to calculate δstv as follows:(4)δstv=δsv⋅δvt,ds,v+dv,t=ds,t,0,other.


### 3.3. Cooperation Strength Calculation Network Edge Weight

In 2001, Newman proposed a side weight calculation algorithm based on cooperation frequency and cooperation intensity. This method distributes cooperation contributions equally based on the number of cooperators, and the cooperation intensity between authors is inversely proportional to the number of cooperators. The calculation method is that, in a paper withk number of collaborators, each researcher should evenly distribute the cooperation time to other (k−1) collaborators. So, the cooperation between the collaborators in this paper is the intensity, which is 1/(k−1), and the cooperation intensity between researchers i and j in paper m is as follows:(5)Wijm=1nm−1,where nm is the number of collaboration scholars in the mth paper, so the cooperation strength is denoted as follows:(6)Wij=∑kWijk=∑k1nk−1.It is more reasonable to simulate the distribution of cooperation intensity based on the equal share of cooperation contribution. However, there also are shortcomings. In reality, the scientific research value of each paper is different. The scientific research level of the collaborators of the papers published in a journal is higher, the time cost of cooperating to complete the paper is greater, and the cooperation intensity between the collaborators should be greater. Therefore, this method only simulates the cooperation intensity under ideal conditions, and it still needs to be improved (Figure2).Figure 2
Example for collaboration network.


### 3.4. Authority Value Calculation

In a collaborator network, links between objects of different types affect their authoritative rankings under the same type. Drawing on the ideas of PageRank and HITS algorithms, this paper proposes an authoritative ranking algorithm based on dual-type networks. In the dual-type network of scholars and journals, there are two kinds of link relationships in the network: the connection between scholars and journals represents the journals that have included papers published by scholars (the weight of the link is the number of papers included in the journal) and the links between scholars and scholars represent the cooperative relationship (the number of academic collaborations). The authoritative value algorithm proposed in this paper is based on the scholar-journal dual-type network.Although the authoritative value of each scholar can be calculated through the journal-scholar network, because of the journal partition table of the Chinese Academy of Sciences, the first-region journals only account for the top 5% of all journals, which also leads to a relatively high number of papers included in the few first-region journals. In the above algorithm, if all journals are treated the same, the authoritative ranking of journals in lower divisions with more papers will become “inflated,” and it will also lead to scholars who publish more papers to obtain higher rankings, even if these papers were all published in lower-division journals. Therefore, this paper divides the journal-scholar network into the following four categories according to the journal division: the first-region journal-scholar network, the second-region journal-scholar network, the third-region journal-scholar network, and the fourth-region journal-scholar network. The normalized authority values in these four networks areAuthorityAk∗i, and weights are assigned to different authority values to obtain the final scholar authority value. Since the impact factors of journals in each division are almost the same, this can well avoid errors caused by the large difference in the number of papers included:(7)AuthorityA∗i=∑k=14θkAuthorityAk∗i.


### 3.5. Talent Recommendation Model

When introducing talents in a specific scientific research direction, the school first selects the researchers who meet the scientific research direction, obtains the recommendation list through the heterogeneous recommendation algorithm, and finally uses the authoritative value as the weight to obtain the final talent recommendation list. This paper uses the normalized authority value to measure the influence of researcheri and takes it as the weight of each recommendation list. Next, we define the talent factor (TF) denoted as(8)TFj=∑i=1NAuthorityA∗ipi,j,where N is the number of scientific research personnel in the school who meet the school's introduction requirements. In a heterogeneous network, it is the similarity between the two. Finally, sort the TF values from high-to-low to generate a talent recommendation list. The higher the TF value is, the more the researcher meets the school's introduction requirements.This recommendation model is only suggestive. To take an extreme example, an off campus scholar A has cooperated with the scientific research “leaders” of our university for many times and published many important achievements, while another scholar B has also cooperated with several teachers of our university, but only published papers in four regional journals. Relatively speaking, scholar A has a higher academic level and has a close relationship with the scientific research personnel with high authority value in the school. According to the experience, scholar A should be more in line with the requirements of talent introduction. However, because scholar B cooperates with many researchers in the university and finally combines the weights of multiple researchers, the talent coefficient of scholar A is greater than that of scholar B. This is obviously unreasonable, so the talent recommendation model proposed in this paper is only for the talent recommendation mechanism. One way of thinking is that when introducing talents, schools can integrate a variety of selection methods or set certain thresholds to screen out unsuitable candidates in advance.


## 3.1. Analysis of the Importance of Partner Networks

In the collaborator network, the scholar node is the bridge of academic links. The higher the importance, the more it can promote the communication and development of the academic circle. There are many evaluation indicators for evaluating a scholar, such as theh-index, which is a method for evaluating the academic level of a scholar, which means that most h papers have been cited at least h times.As shown in Figure1, utilizing CiteSpace to display the original partner network, it can be seen that the nodes closer to the center are more active, and the nodes at the edge of the network are scattered and isolated. Before the experiment, it is necessary to remove the less important nodes in the network, which can not only reduce the time complexity of the subsequent experiments but also filter the nodes that do not meet the requirements in advance because, when making talent recommendation, it tends to recommend researchers with higher academic ability. The researchers at the edge and those close to the center have a big difference in academic ability and scientific research communication, and the possibility of being recommended is low. Only researchers with higher academic ability are recommended to be more versatile and more in line with the actual situation.Figure 1
Original collaborator network.


## 3.2. Betweenness Acceleration Algorithm

For the collaboration network, if the betweenness of the entire network is calculated according to the betweenness definition, the time complexity is high [24–31]. Brandes [20] proposed an accelerated algorithm for betweenness. The algorithm is based on breadth-first search. The principle of this algorithm is described below. For a given graph G=E,V, define pair-dependency; the formula is as follows:(1)δstv=δstvδst,where δstσst is the number of shortest paths from node s to node t and δstv is the number of shortest paths from node s to node t passing through node v.Then, the betweennessBCv of node v is denoted as follows:(2)BCv=∑s≠t≠vδstv.For computingδstv, we introduce predecessor node denoted as follows:(3)Psv=u∈V:u,v∈E,dGs,v=dGs,u+wu,v.Next, we utilize (4) to calculate δstv as follows:(4)δstv=δsv⋅δvt,ds,v+dv,t=ds,t,0,other.


## 3.3. Cooperation Strength Calculation Network Edge Weight

In 2001, Newman proposed a side weight calculation algorithm based on cooperation frequency and cooperation intensity. This method distributes cooperation contributions equally based on the number of cooperators, and the cooperation intensity between authors is inversely proportional to the number of cooperators. The calculation method is that, in a paper withk number of collaborators, each researcher should evenly distribute the cooperation time to other (k−1) collaborators. So, the cooperation between the collaborators in this paper is the intensity, which is 1/(k−1), and the cooperation intensity between researchers i and j in paper m is as follows:(5)Wijm=1nm−1,where nm is the number of collaboration scholars in the mth paper, so the cooperation strength is denoted as follows:(6)Wij=∑kWijk=∑k1nk−1.It is more reasonable to simulate the distribution of cooperation intensity based on the equal share of cooperation contribution. However, there also are shortcomings. In reality, the scientific research value of each paper is different. The scientific research level of the collaborators of the papers published in a journal is higher, the time cost of cooperating to complete the paper is greater, and the cooperation intensity between the collaborators should be greater. Therefore, this method only simulates the cooperation intensity under ideal conditions, and it still needs to be improved (Figure2).Figure 2
Example for collaboration network.


## 3.4. Authority Value Calculation

In a collaborator network, links between objects of different types affect their authoritative rankings under the same type. Drawing on the ideas of PageRank and HITS algorithms, this paper proposes an authoritative ranking algorithm based on dual-type networks. In the dual-type network of scholars and journals, there are two kinds of link relationships in the network: the connection between scholars and journals represents the journals that have included papers published by scholars (the weight of the link is the number of papers included in the journal) and the links between scholars and scholars represent the cooperative relationship (the number of academic collaborations). The authoritative value algorithm proposed in this paper is based on the scholar-journal dual-type network.Although the authoritative value of each scholar can be calculated through the journal-scholar network, because of the journal partition table of the Chinese Academy of Sciences, the first-region journals only account for the top 5% of all journals, which also leads to a relatively high number of papers included in the few first-region journals. In the above algorithm, if all journals are treated the same, the authoritative ranking of journals in lower divisions with more papers will become “inflated,” and it will also lead to scholars who publish more papers to obtain higher rankings, even if these papers were all published in lower-division journals. Therefore, this paper divides the journal-scholar network into the following four categories according to the journal division: the first-region journal-scholar network, the second-region journal-scholar network, the third-region journal-scholar network, and the fourth-region journal-scholar network. The normalized authority values in these four networks areAuthorityAk∗i, and weights are assigned to different authority values to obtain the final scholar authority value. Since the impact factors of journals in each division are almost the same, this can well avoid errors caused by the large difference in the number of papers included:(7)AuthorityA∗i=∑k=14θkAuthorityAk∗i.


## 3.5. Talent Recommendation Model

When introducing talents in a specific scientific research direction, the school first selects the researchers who meet the scientific research direction, obtains the recommendation list through the heterogeneous recommendation algorithm, and finally uses the authoritative value as the weight to obtain the final talent recommendation list. This paper uses the normalized authority value to measure the influence of researcheri and takes it as the weight of each recommendation list. Next, we define the talent factor (TF) denoted as(8)TFj=∑i=1NAuthorityA∗ipi,j,where N is the number of scientific research personnel in the school who meet the school's introduction requirements. In a heterogeneous network, it is the similarity between the two. Finally, sort the TF values from high-to-low to generate a talent recommendation list. The higher the TF value is, the more the researcher meets the school's introduction requirements.This recommendation model is only suggestive. To take an extreme example, an off campus scholar A has cooperated with the scientific research “leaders” of our university for many times and published many important achievements, while another scholar B has also cooperated with several teachers of our university, but only published papers in four regional journals. Relatively speaking, scholar A has a higher academic level and has a close relationship with the scientific research personnel with high authority value in the school. According to the experience, scholar A should be more in line with the requirements of talent introduction. However, because scholar B cooperates with many researchers in the university and finally combines the weights of multiple researchers, the talent coefficient of scholar A is greater than that of scholar B. This is obviously unreasonable, so the talent recommendation model proposed in this paper is only for the talent recommendation mechanism. One way of thinking is that when introducing talents, schools can integrate a variety of selection methods or set certain thresholds to screen out unsuitable candidates in advance.


## 4. Empirical Study

### 4.1. Dataset Analysis

We have captured the SCI data of 985 colleges and universities from 2010 to present. The paper data cover 13 disciplines. In order to avoid the influence of the paper data of other disciplines on the results, we screened out the paper data of the computer discipline. The filter conditions are computer science and artificial intelligence, computer hardware and structure, computer information system, computer application, software engineering, and a computer theory method; a total of 42723 SCI papers were screened.Before the experiment, we removed the edge nodes by the betweenness of the nodes in the network. As shown in Figure3, we used NetworkX to construct a simple visual construction of the collaborator network after the edge nodes were partially removed. It can be seen that the cooperation between college teachers tends to be within the school cooperate. We got 5012 researchers. We compared the author’s name and abbreviation with the teacher information of the school and matched the teachers of the school of computer science. It was found that except for the teachers of the school, the rest were doctoral students with a high volume of collaborative publications. The academic ability is strong. When the school introduces talents, the cooperative relationship between these doctoral students and off-campus researchers cannot be ignored. Strong links can also be used as the basis for talent recommendation and indicators and give the recommended results.Figure 3
Example for the collaboration network without edge nodes.


### 4.2. Experimental Results

This section adopts the NDCG [20] evaluation metric to evaluate the computational results of the two recommendation algorithms. NDCG is often used as a ranking evaluation index. The more coincident the sorting result is with the desired result, the higher the NDCG value. Its value is between 0 and 1. IDCG is the ideal DCG value, that is, the maximum DCG value obtained in descending order of correlation. NDCG normalizes the DCG value in this way.In this experiment, the PathSim algorithm is selected as the benchmark, and the top-10, top-20, and top-30 lists of the experimental results under the network recommendation algorithm and the NDCG value of the marked ranking are calculated, respectively, so as to evaluate the recommendation quality of the two algorithms. The experimental results are shown in Table1. It can be seen from the experimental results that, in three cases, the recommendation quality of the network recommendation algorithm is better than that of the PathSim algorithm. This is because the PathSim algorithm ignores the influence factors of nodes in the meta-path, and the results contain a lot of scientific research level. Lower researchers, so the results are quite different from the marked rankings. In the heterogeneous network recommendation algorithm, the PathSim algorithm based on impact factor is used, which considers the importance of nodes in the meta-path and is more in line with the reality, so the results of the algorithm are significantly better than the traditional PathSim algorithm. Under top-20 and top-30, the recommendation quality of the network algorithm decreases. The main reason is that the recommendation list of the algorithm recommends more indirect/link collaborators in these two ranges, these collaborators, and the university’s researchers. The number of papers published in the same journal is small, so the recommendation results are not ideal.Table 1
The comparison results of our proposed model and PathSim.
NDCGPathSimOur methodTop-100.45320.7175Top-200.51540.6426Top-300.48620.6544


## 4.1. Dataset Analysis

We have captured the SCI data of 985 colleges and universities from 2010 to present. The paper data cover 13 disciplines. In order to avoid the influence of the paper data of other disciplines on the results, we screened out the paper data of the computer discipline. The filter conditions are computer science and artificial intelligence, computer hardware and structure, computer information system, computer application, software engineering, and a computer theory method; a total of 42723 SCI papers were screened.Before the experiment, we removed the edge nodes by the betweenness of the nodes in the network. As shown in Figure3, we used NetworkX to construct a simple visual construction of the collaborator network after the edge nodes were partially removed. It can be seen that the cooperation between college teachers tends to be within the school cooperate. We got 5012 researchers. We compared the author’s name and abbreviation with the teacher information of the school and matched the teachers of the school of computer science. It was found that except for the teachers of the school, the rest were doctoral students with a high volume of collaborative publications. The academic ability is strong. When the school introduces talents, the cooperative relationship between these doctoral students and off-campus researchers cannot be ignored. Strong links can also be used as the basis for talent recommendation and indicators and give the recommended results.Figure 3
Example for the collaboration network without edge nodes.


## 4.2. Experimental Results

This section adopts the NDCG [20] evaluation metric to evaluate the computational results of the two recommendation algorithms. NDCG is often used as a ranking evaluation index. The more coincident the sorting result is with the desired result, the higher the NDCG value. Its value is between 0 and 1. IDCG is the ideal DCG value, that is, the maximum DCG value obtained in descending order of correlation. NDCG normalizes the DCG value in this way.In this experiment, the PathSim algorithm is selected as the benchmark, and the top-10, top-20, and top-30 lists of the experimental results under the network recommendation algorithm and the NDCG value of the marked ranking are calculated, respectively, so as to evaluate the recommendation quality of the two algorithms. The experimental results are shown in Table1. It can be seen from the experimental results that, in three cases, the recommendation quality of the network recommendation algorithm is better than that of the PathSim algorithm. This is because the PathSim algorithm ignores the influence factors of nodes in the meta-path, and the results contain a lot of scientific research level. Lower researchers, so the results are quite different from the marked rankings. In the heterogeneous network recommendation algorithm, the PathSim algorithm based on impact factor is used, which considers the importance of nodes in the meta-path and is more in line with the reality, so the results of the algorithm are significantly better than the traditional PathSim algorithm. Under top-20 and top-30, the recommendation quality of the network algorithm decreases. The main reason is that the recommendation list of the algorithm recommends more indirect/link collaborators in these two ranges, these collaborators, and the university’s researchers. The number of papers published in the same journal is small, so the recommendation results are not ideal.Table 1
The comparison results of our proposed model and PathSim.
NDCGPathSimOur methodTop-100.45320.7175Top-200.51540.6426Top-300.48620.6544


## 5. Conclusion

In the face of the increasingly complex talent market, both enterprises and colleges and universities have continuously strengthened the needs of college management. Based on this background, this paper starts from the perspective of academic talent recommendation and builds a recommendation model based on the paper cooperation network. Firstly, the importance of nodes in the collaborator network is discussed, the betweenness acceleration algorithm is selected to calculate the importance of nodes in the network, and the original collaborator network is filtered according to the node betweenness, and cooperation with a relatively high academic level is obtained. After that, the edge weight of the partner network is discussed, and the edge weight calculation method that introduces the cooperation effect is used. Then, the characteristics of the cooperation relationship under the homogeneous partner network are analyzed, and probability models are recommended, respectively. Then, a heterogeneous network of partners is proposed. A similarity measurement algorithm, based on journal impact factor, discusses and selects three more convincing meta-paths to comprehensively consider the similarity between researchers. Finally, an authoritative value calculation method based on a heterogeneous network is proposed. Based on the authoritative value of scholars, the final talent recommendation list is constructed. Future research can be extended from the academic talent market to the more general university employment talent market. At the same time, the effective mechanism in talent recommendation is found by exploring the internal mechanism of the cooperation network.



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*Source: 7697192-2022-09-07.xml*