CFP last date
22 June 2026
Call for Paper
July Edition
IJCA solicits high quality original research papers for the upcoming July edition of the journal. The last date of research paper submission is 22 June 2026

Submit your paper
Know more
The week's pick
Responsive image
Multi-Band RLS Estimation with Rank Two Updates: Application to Short-Term Temperature Forecast

Alexander Stotsky
Random Articles
Reseach Article

An Agentic AI Framework for Semantic Workforce Matching in the Hospitality Domain

by Ajinkya Valanjoo, Shreyash Dhoke, Mayank Mankar, Shlok Nandanwar, Tanisha Pradhan
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 187 - Number 104
Year of Publication: 2026
Authors: Ajinkya Valanjoo, Shreyash Dhoke, Mayank Mankar, Shlok Nandanwar, Tanisha Pradhan
10.5120/ijcacf00f23597ac

Ajinkya Valanjoo, Shreyash Dhoke, Mayank Mankar, Shlok Nandanwar, Tanisha Pradhan . An Agentic AI Framework for Semantic Workforce Matching in the Hospitality Domain. International Journal of Computer Applications. 187, 104 ( May 2026), 47-53. DOI=10.5120/ijcacf00f23597ac

@article{ 10.5120/ijcacf00f23597ac,
author = { Ajinkya Valanjoo, Shreyash Dhoke, Mayank Mankar, Shlok Nandanwar, Tanisha Pradhan },
title = { An Agentic AI Framework for Semantic Workforce Matching in the Hospitality Domain },
journal = { International Journal of Computer Applications },
issue_date = { May 2026 },
volume = { 187 },
number = { 104 },
month = { May },
year = { 2026 },
issn = { 0975-8887 },
pages = { 47-53 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume187/number104/an-agentic-ai-framework-for-semantic-workforce-matching-in-the-hospitality-domain/ },
doi = { 10.5120/ijcacf00f23597ac },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2026-05-17T02:29:17.044944+05:30
%A Ajinkya Valanjoo
%A Shreyash Dhoke
%A Mayank Mankar
%A Shlok Nandanwar
%A Tanisha Pradhan
%T An Agentic AI Framework for Semantic Workforce Matching in the Hospitality Domain
%J International Journal of Computer Applications
%@ 0975-8887
%V 187
%N 104
%P 47-53
%D 2026
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Hospitality businesses face a persistent hir-ing crisis characterised by annual front-line turnover ex-ceeding 70%, wildly swinging seasonal demand, and over-reliance on keyword-matching systems that routinely miss qualified candidates. SmartServe is an AI-powered recruit-ment platform that addresses these challenges through se-mantic understanding and autonomous agent orchestration, automating the complete hiring workflow from job posting to offer letter generation. The system combines semantic embeddings (Google Gemini text-embedding-004, 768 dimensions) with domain-specific scoring rules tailored for hospitality roles. A two-tier model pipeline employs Gemini 2.0 Flash for fast structured extraction and GPT-4o-mini for summarisation, reducing per-profile analysis cost from $0.03 to $0.003 — a 90% reduction — while maintaining output quality. A three-month pilot with 12 Mumbai restaurants demon-strated that the semantic matcher achieves 82.4% Preci-sion@10, more than double the 38% from keyword match-ing. Time-to-hire fell from 12.3 days to 3.2 days (74% improvement), and average applications per filled position dropped from 28.5 to 8.7. Role-based model selection re-duced LLM costs by 45%, and batch processing yielded a further 90% saving on API calls.

References
  1. Pillai, M. and Sivathanu, D. 2020. Adoption of AI-based chatbots for hospitality and tourism. Int. J. Con-temporary Hospitality Management 32, 10, 3199–3226.
  2. Tussyadiah, S. and Miller, M. 2021. Perceived im-pacts of artificial intelligence and responses to posi-tive behavior change intervention. J. Travel Research 60, 3, 618–637.
  3. Law, R., Li, G., Fong, D.K., and Han, X. 2019.Tourism demand forecasting: A deep learning ap-proach. Tourism Management 74, 410–423.
  4. Huang, Y. and Rust, R. 2018. Artificial intelligence in service. J. Service Research 21, 2, 155–172.
  5. Sharma, A., Mehta, R., and Patel, K. 2024. AI-driven smart hospitality management systems. Int. J. Multi-disciplinary Research 6, 3, 1–15.
  6. Jarrahi, M.H. 2018. Artificial intelligence and the fu-ture of work: Human-AI symbiosis in organizational decision making. Business Horizons 61, 4, 577–586.
  7. Pereira, K. and Bavik, A. 2021. The impact of artifi-cial intelligence on workers: Evidence from hospital-ity. Int. J. Hospitality Management 98, 103–115.
  8. Prentice, C., Loureiro, X., and Guerreiro, M. 2021. Understanding AI adoption in the hospitality industry. Int. J. Contemporary Hospitality Management 33, 11, 3988–4008.
  9. Reimers, N. and Gurevych, I. 2019. Sentence-BERT: Sentence embeddings using Siamese BERT-networks. In Proc. EMNLP, 3982–3992.
  10. Devlin, J., Chang, M., Lee, K., and Toutanova, K. 2019. BERT: Pre-training of deep bidirectional trans-formers for language understanding. In Proc. NAACL-HLT, 4171–4186.
  11. Vaswani, A. et al. 2017. Attention is all you need. Advances in Neural Information Processing Systems, 5998–6008.
  12. Liu, Y. et al. 2019. RoBERTa: A robustly optimized BERT pretraining approach. arXiv:1907.11692.
  13. Mikolov, T., Chen, K., Corrado, G., and Dean, J. 2013. Efficient estimation of word representations in vector space. In Proc. ICLR.
  14. Bojanowski, P. et al. 2017. Enriching word vectors with subword information. Trans. ACL 5, 135–146.
  15. Peters, M.E. et al. 2018. Deep contextualized word representations. In Proc. NAACL-HLT, 2227–2237.
  16. Wooldridge, M. 2009. An Introduction to MultiAgent Systems, 2nd ed. John Wiley & Sons, Chichester, UK.
  17. Russell, S.J. and Norvig, P. 2020. Artificial Intelli-gence: A Modern Approach, 4th ed. Pearson.
  18. Weiss, G., Ed. 2013. Multiagent Systems, 2nd ed. MIT Press, Cambridge, MA.
  19. Chase, J.S. et al. 2023. LangChain: Building applications with LLMs through composability. arXiv:2310.06770.
  20. Wang, L. et al. 2023. A survey on large language model based autonomous agents. arXiv:2308.11432.
  21. Johnson, J., Douze, M., and Jégou, H. 2021. Billion-scale similarity search with GPUs. IEEE Trans. Big Data 7, 3, 535–547.
  22. Malkov, Y. and Yashunin, D. 2020. Efficient and ro-bust approximate nearest neighbor search using hier-archical navigable small world graphs. IEEE Trans. PAMI 42, 4, 824–836.
  23. Lewis, P. et al. 2020. Retrieval-augmented genera-tion for knowledge-intensive NLP tasks. Advances in NeurIPS, 9459–9474.
  24. Khattab, O. and Zaharia, M. 2020. ColBERT: Effi-cient and effective passage search via contextualized late interaction over BERT. In Proc. ACM SIGIR, 39–48.
  25. Raghavan, M. and Levine, D. 2020. Self-determination, job design and recruitment in gig economy platforms. Organization Science 31, 4, 997–1018.
  26. Chalfin, M. et al. 2016. Productivity and selection of human capital with machine learning. American Eco-nomic Review 106, 5, 124–127.
  27. Autor, D. 2015. Why are there still so many jobs? The history and future of workplace automation. J. Eco-nomic Perspectives 29, 3, 3–30.
  28. Bersin, J. 2024. HR technology disruptions for 2024: The definitive guide. Josh Bersin Company Research.
  29. McKinsey Global Institute. 2021. The future of work after COVID-19. McKinsey & Company Report, February.
  30. Deloitte. 2024. Global Human Capital Trends: The skills-based organization. Deloitte Insights.
Index Terms

Computer Science
Information Sciences

Keywords

Agentic AI Semantic Embeddings FAISS Hospitality Recruitment Multi-Agent Systems LLM Or-chestration Hybrid Scoring

Powered by PhDFocusTM