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A Collaborative Problem-Solving Marketplace Platform with Adaptive Skill Credibility Scoring for Industry Talent Discovery
Authors
1 Associate Professor, Department of Information Technology, Er Perumal Manimekalai College of Engineering, Hosur, Tamil Nadu, India. 2 3 4 5 Department of Information Technology, Er Perumal Manimekalai College of Engineering, Hosur, Tamil Nadu, India.
Article Information
- Published In
- Volume 6, Issue 2 (2026)
- Published Online
- March-April 2026
- Pages
- 421-429
- Article Type
- Original Article
- Check for updates
-
References
1. LinkedIn Talent Solutions, “LinkedIn Workforce Report,” LinkedIn Corp., Sunnyvale, CA, USA, Tech. Rep., 2022.
2. McKinsey Global Institute, “The Skill Shift: Automation and the Future of the Workforce,” McKinsey & Co., New York, 2022.
3. Stack Overflow, “Developer Survey 2023,” Stack Exchange Inc., New York, NY, USA, 2023.
4. World Economic Forum, The Future of Jobs Report 2023, WEF, Geneva, Switzerland, 2023.
5. J. J. Horton and L. B. Chilton, “The Online Laboratory: Conducting Experiments in a Real Labor Market,” in Proc. 11th ACM Conf.
Electron. Commerce, pp. 253–262, 2010.
6. F. L. Schmidt and J. E. Hunter, “The Validity and Utility of Selection Methods in Personnel Psychology: 85 Years of Research Findings,”
Psychol. Bull., vol. 124, no. 2, pp. 262– 274, 1998.
7. K. Kroft, F. Lange, and M. J. Notowidigdo, “Duration De- pendence and Labor Market Conditions: Evidence from a Field Experiment,”
Q. J. Econ., vol. 128, no. 3, pp. 1123–1167, 2013.
8. P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom, and J. Riedl, “GroupLens: An Open Architecture for Collabora- tive Filtering of
Netnews,” in Proc. ACM CSCW, pp. 175– 186, 1994.
9. C. Dellarocas, “The Digitization of Word of Mouth: Promise and Challenges of Online Feedback Mechanisms,” Manage. Sci., vol. 49, no.
10, pp. 1407–1424, 2003.
10. A. Josang, R. Ismail, and C. Boyd, “A Survey of Trust and Reputation Systems for Online Service Provision,” Decis. Support Syst., vol.
43, no. 2, pp. 618–644, 2007.
11. LinkedIn Engineering, “Improving Job Recommendations with Skill Embeddings,” LinkedIn Eng. Blog, 2021.
12. G. Adomavicius and A. Tuzhilin, “Toward the Next Gen- eration of Recommender Systems,” IEEE Trans. Knowl. Data Eng., vol. 17no. 6, pp. 734–749, Jun. 2005.
13. G. A. Akerlof, “The Market for Lemons: Quality Uncer- tainty and the Market Mechanism,” Q. J. Econ., vol. 84, no. 3, pp. 488–500,
1970.
14. P. Bajari and A. Hortacsu, “The Winner’s Curse, Reserve Prices, and Endogenous Entry: Empirical Insights from eBay Auctions,” RAND
J. Econ., vol. 34, no. 2, pp. 329– 355, 2003.
15. T. Erl, Service-Oriented Architecture: Concepts, Technol- ogy, and Design. Upper Saddle River, NJ: Prentice Hall, 2005.
16. E. F. Codd, “A Relational Model of Data for Large Shared Data Banks,” Commun. ACM, vol. 13, no. 6, pp. 377–387,1970.
17. OWASP Foundation, “OWASP Top Ten 2021,” Open Web Application Security Project, 2021.
18. M. Kokkodis and P. G. Ipeirotis, “Reputation Transferabil- ity in Online Labor Markets,” Manage. Sci., vol. 62, no. 6, pp. 1687–1706, 2016.
19. D. Autor, “Work of the Past, Work of the Future,” AEA Papers Proc., vol. 109, pp. 1–32, 2019.
20. S. Ba and P. A. Pavlou, “Evidence of the Effect of Trust Building Technology in Electronic Markets,” MIS Q., vol. 26, no. 3, pp. 243–
268, 2002
2. McKinsey Global Institute, “The Skill Shift: Automation and the Future of the Workforce,” McKinsey & Co., New York, 2022.
3. Stack Overflow, “Developer Survey 2023,” Stack Exchange Inc., New York, NY, USA, 2023.
4. World Economic Forum, The Future of Jobs Report 2023, WEF, Geneva, Switzerland, 2023.
5. J. J. Horton and L. B. Chilton, “The Online Laboratory: Conducting Experiments in a Real Labor Market,” in Proc. 11th ACM Conf.
Electron. Commerce, pp. 253–262, 2010.
6. F. L. Schmidt and J. E. Hunter, “The Validity and Utility of Selection Methods in Personnel Psychology: 85 Years of Research Findings,”
Psychol. Bull., vol. 124, no. 2, pp. 262– 274, 1998.
7. K. Kroft, F. Lange, and M. J. Notowidigdo, “Duration De- pendence and Labor Market Conditions: Evidence from a Field Experiment,”
Q. J. Econ., vol. 128, no. 3, pp. 1123–1167, 2013.
8. P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom, and J. Riedl, “GroupLens: An Open Architecture for Collabora- tive Filtering of
Netnews,” in Proc. ACM CSCW, pp. 175– 186, 1994.
9. C. Dellarocas, “The Digitization of Word of Mouth: Promise and Challenges of Online Feedback Mechanisms,” Manage. Sci., vol. 49, no.
10, pp. 1407–1424, 2003.
10. A. Josang, R. Ismail, and C. Boyd, “A Survey of Trust and Reputation Systems for Online Service Provision,” Decis. Support Syst., vol.
43, no. 2, pp. 618–644, 2007.
11. LinkedIn Engineering, “Improving Job Recommendations with Skill Embeddings,” LinkedIn Eng. Blog, 2021.
12. G. Adomavicius and A. Tuzhilin, “Toward the Next Gen- eration of Recommender Systems,” IEEE Trans. Knowl. Data Eng., vol. 17no. 6, pp. 734–749, Jun. 2005.
13. G. A. Akerlof, “The Market for Lemons: Quality Uncer- tainty and the Market Mechanism,” Q. J. Econ., vol. 84, no. 3, pp. 488–500,
1970.
14. P. Bajari and A. Hortacsu, “The Winner’s Curse, Reserve Prices, and Endogenous Entry: Empirical Insights from eBay Auctions,” RAND
J. Econ., vol. 34, no. 2, pp. 329– 355, 2003.
15. T. Erl, Service-Oriented Architecture: Concepts, Technol- ogy, and Design. Upper Saddle River, NJ: Prentice Hall, 2005.
16. E. F. Codd, “A Relational Model of Data for Large Shared Data Banks,” Commun. ACM, vol. 13, no. 6, pp. 377–387,1970.
17. OWASP Foundation, “OWASP Top Ten 2021,” Open Web Application Security Project, 2021.
18. M. Kokkodis and P. G. Ipeirotis, “Reputation Transferabil- ity in Online Labor Markets,” Manage. Sci., vol. 62, no. 6, pp. 1687–1706, 2016.
19. D. Autor, “Work of the Past, Work of the Future,” AEA Papers Proc., vol. 109, pp. 1–32, 2019.
20. S. Ba and P. A. Pavlou, “Evidence of the Effect of Trust Building Technology in Electronic Markets,” MIS Q., vol. 26, no. 3, pp. 243–
268, 2002
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