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5861, Shenzhen, China, December 2009. Youll gain an understanding of supervised and unsupervised learning, as well as best practices and case studies for a well-rounded introduction to machine learning. Workflow diagram of human resources recommendation system. Machine learning can take you to career paths such as: Machine learning engineer, data scientist, human-centered machine learning designer, computational linguist, software engineer or developer, and business intelligence developer. Since ML research comes from multiple disciplines and consists of several methods, a semi-systematic approach to literature review was considered appropriate. At the same time, as can be seen from Figure 4, the absolute value of the loss function of the HDCF model is lower than that of the CDL model because the former only trains noncold-start projects, and the overall number of samples is less than the latter. 9, pp. If youre interested in machine learning, the Machine Learning specialization from Stanford University can provide you with job-ready skills to propel your career forward. ML applications are strongest in the areas of recruitment and performance management and the use of decision trees and text-mining algorithms for classification dominate all functions of HRM. The difference from the true value is the predicted rating of job i by user u. The experimental results show that the proposed method is effective for enterprise personnel resource forecasting and can support enterprise managers to carry out scientific personnel planning and allocation. For the companys human resource needs, the historical data are used as training samples to train the two models to obtain a robust prediction model. Step 1: The K-means clustering method is used to solve the center of the radial basis function; generally, after the network initialization, the input data are calculated according to the nearest neighbor rule, and grouping and re-adjusting the clustering center are completed in 3 steps. Management Research Review, 39(6), 655671. (PDF) MACHINE LEARNING IN PERSONNEL SELECTION - Academia.edu Machine learning has become more and more integrated into our lives. In the feedback process, the weight correction amount and threshold correction amount of the hidden layer and the output layer can be written aswhere is the coefficient determining adjustment rate. The experimental results are shown in Table 4. 1, pp. Inclusion in an NLM database does not imply endorsement of, or agreement with, N. Wan, L. Li, C. Ye, and B. Wang, Risk assessment in intelligent manufacturing process: a case study of an optical cable automatic arranging robot, IEEE Access, vol. 2019, Article ID 2430656, p. 12, 2019. In Academy of Management Annual Meeting, 12th-16th August 2011, San Antonio, Texas. By referencing and analyzing the forest quality . The experimental results show that the method proposed in this paper can effectively predict the number of personnel required and can support the planning and allocation of human resources. The current algorithm implementation does not have enough scalability, so it is difficult to be competent for the analysis and processing of a large number of data in the real human resources system. 1295112962, 2020. Lee D. H., Brusilovsky P. Fighting information overflow with personalized comprehensive information access: a proactive job recommender. With these books on your reading list, youll be able to: Determine whether a career in machine learning is right for you, Learn what skills youll need as a machine learning engineer or data scientist, Knowledge that can help you find and prepare for job interviews, Stay on top of the latest trends in machine learning and artificial intelligence. Google Scholar. There will be a large number of vacancies in the matrix, and it is difficult to obtain the user's preference for the position according to the matrix, so it is difficult to improve the accuracy of the algorithm. The prescriber is then left to balance . There is only one output layer in the neural network; denotes the error value. Machine learned job recommendation. Classical, or "non-deep", machine learning is more dependent on human intervention to learn. Its goal is to use the automatic coder to learn the intermediate feature representation and embed them into the classical collaborative filtering model. This section will introduce the experimental results and the conclusions of the analysis. In some cases, the scoring matrix may not be decomposed into job-seeking user feature matrix and job feature matrix. Y. Shao, X. Yao, L. Tian, and H. Chen, A multiswarm optimizer for distributed decision making in virtual enterprise risk management, Discrete Dynamics in Nature and Society, vol. The input layer is composed of signal nodes, and the number of neurons is the dimension of the input sample. 3. sharing sensitive information, make sure youre on a federal The technology itself is not new, but the applications for human resources have only recently started to gain traction, and they are already making a significant impact. On this basis, many excellent recommendation models have been proposed. In order to improve the practicability of human resources recommendation system based on deep learning and apply it in real business system in the future, we also need to try to implement HDCF algorithm on the distributed mxnet framework and design the distributed architecture of recommendation system based on deep learning [19, 20]. Taking the human resources user rating matrix as an example, m represents the number of job-seeker users, n represents the number of positions in the industry, the human resources user rating matrix is due to the scarcity of positions rated by users, a large number of positions have not been selected by users, and the data in the entire matrix is sparse. The basic processing flow of the human resources recommendation system is shown in Figure 3, which shows a series of workflows of the human resources recommendation system from data collection providing recommendation results to users. These studies can be divided into two categories. 113, 2020. Read more: What Is Deep Learning? Faced with this problem, people propose recommender systems to solve the problem of obtaining preference information, which can better increase the user's experience and meet their own needs more easily. It can be seen from the figure that the performance of various methods is degraded to a certain extent due to the influence of noise. The weights and thresholds are adjusted according to the actual error. A basic model of BPNN is shown in Figure 1. The recall rate recall@200 of CDL and HDCF under different K values. Deep Learning explores key concepts and topics of deep learning, such as linear algebra, probability and information theory, and more.. When using optimization to minimize the loss function, the parameters of the encoder and decoder can be improved by, for example, stochastic gradient descent. Among them, and are the connection weight matrix and bias vector from the input layer to the hidden layer. It still tends to recommend user preferences and behaviors. Step 2: According to the parameters determined in Step 1, the hidden layer output calculation is carried out. B. Memishi, M. S. Prez, and G. Antoniu, Feedback-based resource allocation in MapReduce-based systems, Scientific Programming, vol. Patrick Zschech & Kai Heinrich Electronic Markets 31 , 685-695 ( 2021) Cite this article 77k Accesses 317 Citations 56 Altmetric Metrics This article has been updated Abstract Today, intelligent systems that offer artificial intelligence capabilities often rely on machine learning. The dataset used to support the findings of this study is available from the corresponding author upon request. PDF INTRODUCTION MACHINE LEARNING - Stanford University The intermediate features are obtained by the automatic encoder. In this paper, for each user i, by sorting the predicted scores Ri of the items and recommending the top N items to the user, the recall rate recall@N for user i can be defined as. After receiving the notification from the data preprocessing layer, the content-based filtering algorithm is used for the newly added post item, and its predicted score is obtained according to the basic attributes of the post and updated to the score matrix used to provide personalized recommendation; (3) the latest hot job statistics. Yang, A novel AdaBoost framework with robust threshold and structural optimization, IEEE Transactions on Cybernetics, vol. Malinowski J., Keim T., Wendt O. . Application of Machine Learning (ML) in Human Resource Management Int J Bus Adm Res Rev 1(19):6266, Tursunbayeva A, Di Lauro S, Pagliari C (2018) People analyticsa scoping review of conceptual boundaries and value propositions. After the weights are multiplied and added, the corresponding output is obtained. Machine learning in human resource system of intelligent manufacturing industry. (2) As shown in the data flow shown in labels 23, the ETL server reads the original human resources data from the MySQL database, cleans and converts it, obtains the warehouse data for HDCF algorithm model training, and saves it to in a MySQL database. As a remedy, researchers began to study prediction methods based on machine learning models, which have higher superiority compared with traditional prediction methods [2125]. (2001). As a result, the prediction results are prone to be non-consensual judgments. When L is optimal, the most accurate prediction score can be obtained according to the obtained matrices U and V. The overall architecture of the human resources recommendation system is shown in Figure 2. This is a preview of subscription content, access via your institution. [21] predicted the human resources of enterprises based on backpropagation neural network (BPNN) and believed that network can obtain better prediction results. This paper proposes a human resource prediction method based on machine learning to address the above problems. In fact, the measure of cosine similarity is the magnitude of the cosine of the angle. and transmitted securely. A systolic autoencoder is an unsupervised learning algorithm for generating useful feature representations. It is very pertinent to ensure the workforce in an organization is aligned to its strategy and priority. 2023 Springer Nature Switzerland AG. Qualitative analysis methods mainly include the Delphi method, subjective judgment method, microintegration method, and so on. Given the current focus of organizations on digitalization, this review contributes significantly to the understanding of the current state of ML integration in HRM. Therefore, the combination of quantitative and qualitative methods has naturally become the research direction of enterprise human resource forecasting [1317]. This paper reviews 105 Scopus-indexed articles to identify the degree, scope and purposes of machine learning (ML) adoption in the core functions of human resource management (HRM). Finally, the corresponding predicted value of human resource demand can be obtained under current conditions, which can be used as a reference for enterprise management personnel in personnel planning and configuration. D. P. Fonsecaa, P. F. Wankea, and H. L. Correa, A two-stage fuzzy neural approach for credit risk assessment in a Brazilian credit card company[J], Applied Soft Computing Journal, vol. 8600 Rockville Pike Recall rate recall@200 under different network layers L. Table 4 shows the recall rates of the two algorithm models when the number of network layers is 2, 4, and 6 layers, respectively. The matrix decomposition formula is as follows [1416]. Battlefield: with the development of enterprise information management, using information technology to improve the efficiency of enterprise management has become the consensus of contemporary enterprises. Pavlov D. Y., Pennock D. M. A maximum entropy approach to collaborative filtering in dynamic, sparse, high-dimensional domains. The core competitiveness of an enterprise depends not only on sufficient capital and advanced technology but also on the human capital it has. With the help of the ability of deep learning feature extraction, this paper overcomes the main problems of traditional collaborative filtering algorithms such as data sparseness and cold start and improves the quality of human resource management. RBFNN is a function approximation network that non-linearly maps the input space to the output space. A Beginners Guide, This book offers a beginner-friendly introduction for those of you more interested in the deep learning aspect of machine learning. The experiment uses human resource data of a certain company as the samples. In Machine Learning designer, creating and using a machine learning model is typically a three-step process: Configure a model, by choosing a particular type of algorithm, and then defining its parameters or hyperparameters. GPT, or Generative Pre-trained Transformer, is a language model that has shown remarkable progress in various vertical industries. The other part is to calculate the latest popular weights of jobs and get the recommendation list of the latest popular jobs. Step 3: The least squares algorithm is employed to calculate the weight between the hidden layer and the output layer. By continuously adjusting the user feature matrix and the job feature matrix and calculating the predicted value. O Discurso e a prtica: O que nos dizem os especialistas e o que nos mostram as prticas das empresas sobre os modelos de gesto de recursos humanos. The prediction logic is rigorous and overcomes the shortcomings of qualitative methods. As can be seen from Figure 5, with the increase of the latent semantic vector dimension K, the recall of the recommended results first increases and then decreases, indicating that the performance of the algorithm shows a trend of first increasing and then decreasing. In recent years, the rapid development of the Internet and information technology has eased many repetitive HR processes, such as ATS (automatic candidate management system), which greatly accelerated the interview process and facilitated the management of candidates. 6, no. The first category focuses on designing recommendation models based only on autoencoders without using any components of traditional recommendation models. X. Wei, A method of enterprise financial risk analysis and early warning based on decision tree model, Security and Communication Networks, vol. Collaborative filtering algorithms are mainly based on similar users or items to recommend. Based on the prediction results, the company managers can carry out targeted human resource planning and allocation to improve the efficiency of enterprise operations. 12, pp. The RBFNN obtains a value by calculating the norm of the input sample and the hidden layer point (center point) and substituting it into the radial basis function (Gaussian function, quadratic function, inverse quadratic function, and so on). Human resource demand forecasting generally needs to follow the principle of correlation and the principle of inertia. Classification and statistics of human resources data. Linden G., Smith B., York J. Amazon.com recommendations: item-to-item collaborative filtering. This content has been made available for informational purposes only. This applies to all departments in the company, and human resources is no exception. The principle of correlation is based on the correlation between the research objects and uses other objects to predict the targeted object [512]. the contents by NLM or the National Institutes of Health. 2020, Article ID 8880786, p. 13, 2020. Other sci-fi authors like Ted Chiang explore our relationship with AI technology in stories like The Lifecycle of Software Objects. According to the current enterprise situation, the current enterprise human resource forecast value can be obtained. The basic business application of human resources includes functions such as applicant registration, login, job browsing, collection, and job application; job recommendation application includes personalized job recommendation and the latest popular job recommendation, which is responsible for the list of job positions recommended by the system to users presented to the user. A Beginners Guide, What Is Deep Learning? Available at SSRN 3319897, Xu Z, Song B (2006) A machine learning application for human resource data mining problem. Authors would like to acknowledge the Department of Management Studies, Indian Institute of Technology Delhi for their support for this study. 2, pp. 44, no. Among them, measuring the matching degree between talent qualification and post requirements, that is, the research of person post matching, has become one of the important topics. (3) As shown in the data flow shown in labels 45, the recommended computing server reads the warehouse data from the MySQL database and performs two parts of computing work: one part is offline model training, and the predicted score matrix is obtained for personality. OECD Science, Technology and Industry Working Papers, No. Melville P., Mooney R. J., Nagarajan R. Content-boosted collaborative filtering for improved recommendations. PDF Lecture #25: Artificial Intelligence and Machine Learning N. Arora and P. D. Kaur, A Bolasso based consistent feature selection enabled random forest classification algorithm: an application to credit risk assessment, Applied Soft Computing Journal, vol. As can be seen from the data labels in Figure 3, the basic workflow of the recommender system includes the following five steps: (1) as shown in the data flow shown in label 1, the Java Web application provides basic business functions to candidates and shows the user a list of jobs recommended by the recommender system to the user. If it is not over, return to Step 2 to continue network training. https://doi.org/10.1080/2156857X.2015.1065902, CrossRef