New Hybrid Techniques for Business Recommender Systems

Job recommender systems (JRS) are a subclass of information filtering systems that aims to help job seekers identify what might match their skills and experiences and prevent them from being lost in the vast amount of information available on job boards that aggregates postings from many sources such as LinkedIn or Indeed. A variety of strategies used as part of JRS have been implemented, most of them failed to recommend job vacancies that fit properly to the job seekers profiles when dealing with more than one job offer. They consider skills as passive entities associated with the job description, which need to be matched for finding the best job recommendation. This paper provides a recommender system to assist job seekers in finding suitable jobs based on their resumes. The proposed system recommends the top-n jobs to the job seekers by analyzing and measuring similarity between the job seeker’s skills and explicit features of job listing using content-based filtering. First-hand information was gathered by scraping jobs description from Indeed from major cities in Saudi Arabia (Dammam, Jeddah, and Riyadh). Then, the top skills required in job offers were analyzed and job recommendation was made by matching skills from resumes to posted jobs. To quantify recommendation success and error rates, we sought to compare the results of our system to reality using decision support measures.

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“…As for hybrid recommender systems, they combine the two approaches: use of evaluations and information given by the user [28][29][30][31][32].…”

Section: Recommender Systems Methodsmentioning

confidence: 99%

Learning-Based Matched Representation System for Job Recommendation

et al. 2022

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“…Wang and Su (2015) in their architecture iGeoTutor assisted students in mastering geometry theorems by implementing search strategies (e.g., DFS) from partially complete proofs. Pande et al (2021) aims to improve individual and self-regulated learning in group assignments through a conversational system built using NLU and dialogue management systems that prompts the students to reflect on lessons learnt while directing them to partial solutions.…”

Section: Reactive Engagement Of Ai For Education Tutoring Aidsmentioning

confidence: 99%

Proactive and reactive engagement of artificial intelligence methods for education: a review

Mallik

Gangopadhyay

2023

Front. Artif. Intell.

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The education sector has benefited enormously through integrating digital technology driven tools and platforms. In recent years, artificial intelligence based methods are being considered as the next generation of technology that can enhance the experience of education for students, teachers, and administrative staff alike. The concurrent boom of necessary infrastructure, digitized data and general social awareness has propelled these efforts further. In this review article, we investigate how artificial intelligence, machine learning, and deep learning methods are being utilized to support the education process. We do this through the lens of a novel categorization approach. We consider the involvement of AI-driven methods in the education process in its entirety—from students admissions, course scheduling, and content generation in the proactive planning phase to knowledge delivery, performance assessment, and outcome prediction in the reactive execution phase. We outline and analyze the major research directions under proactive and reactive engagement of AI in education using a representative group of 195 original research articles published in the past two decades, i.e., 2003–2022. We discuss the paradigm shifts in the solution approaches proposed, particularly with respect to the choice of data and algorithms used over this time. We further discuss how the COVID-19 pandemic influenced this field of active development and the existing infrastructural challenges and ethical concerns pertaining to global adoption of artificial intelligence for education.

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“…Similarly, Niwash et al (2022) found that BI has a positive effect on competitive advantage and business performance. BI tools can track, analyse, and display key performance indicators (KPI) metrics to monitor the health of a business (Pande et al, 2022). Recent innovative technologies, such as artificial intelligence (AI), can support businesses through disruption (Naz et al, 2022).…”

Section: Business Intelligence and Construction Companiesmentioning

confidence: 99%

Managerial Opportunities in Application of Business Intelligence in Construction Companies

Golestanizadeh

Sarvari

Chan

et al. 2023

Journal of Civil Engineering and Management

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In construction projects, managers make multiple decisions every day. Most of these decisions are relatively unimportant; some of them are critical and could lead to the success or failure of a construction project. To ensure construction companies make effective managerial decisions, decision making requires performing an initial technical and economic analysis, comparing different decision-making solutions, using a planning system, and ensuring project implementation based on the provided plans. Therefore, the use of powerful systems such as business intelligence (BI), which play a central role in management and decision-making, is essential in project-based companies. The current study aims to determine and evaluate the main managerial opportunities in the application of BI in project-based construction companies using a descriptive survey approach. An empirical research questionnaire consisting of 60 factors and 7 categories was adopted. The questionnaire, after confirming its validity and reliability, was distributed to 100 experts engaged in 5 active project-based construction companies who were familiar with BI topics. To analyse the data, a one-sample t-test and the Friedman test were performed using the SPSS software. The findings indicated that the importance of the identified opportunities for the use of BI in project-based construction companies is above average and that, in the case of using BI in such companies, these opportunities can be used to improve project performance. The results of the current study can help managers and other stakeholders as an effective decision-making tool to better implement BI in project-based companies.

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New Hybrid Techniques for Business Recommender Systems

Cited by 4 publications

References 51 publications

Learning-Based Matched Representation System for Job Recommendation

Learning-Based Matched Representation System for Job Recommendation

Proactive and reactive engagement of artificial intelligence methods for education: a review

Managerial Opportunities in Application of Business Intelligence in Construction Companies

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