JobRecoGPT -- Explainable job recommendations using LLMs (2309.11805v1)
Abstract: In today's rapidly evolving job market, finding the right opportunity can be a daunting challenge. With advancements in the field of AI, computers can now recommend suitable jobs to candidates. However, the task of recommending jobs is not same as recommending movies to viewers. Apart from must-have criteria, like skills and experience, there are many subtle aspects to a job which can decide if it is a good fit or not for a given candidate. Traditional approaches can capture the quantifiable aspects of jobs and candidates, but a substantial portion of the data that is present in unstructured form in the job descriptions and resumes is lost in the process of conversion to structured format. As of late, LLMs have taken over the AI field by storm with extraordinary performance in fields where text-based data is available. Inspired by the superior performance of LLMs, we leverage their capability to understand natural language for capturing the information that was previously getting lost during the conversion of unstructured data to structured form. To this end, we compare performance of four different approaches for job recommendations namely, (i) Content based deterministic, (ii) LLM guided, (iii) LLM unguided, and (iv) Hybrid. In this study, we present advantages and limitations of each method and evaluate their performance in terms of time requirements.
- https://docparser.com/blog/data-extraction-tools/.
- https://openai.com/gpt-4.
- https://www.kaggle.com/datasets/kshitizregmi/jobs-and-job-description.
- Topic modeling driven content based jobs recommendation engine for recruitment industry. Procedia computer science, 122:865–872, 2017.
- Lijar: A system for job application redistribution towards efficient career marketplace. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 1397–1406, 2017.
- Hybrid deep collaborative filtering for job recommendation. In 2017 2nd IEEE International Conference on Computational Intelligence and Applications (ICCIA), pages 275–280. IEEE, 2017.
- A scalable, high-performance algorithm for hybrid job recommendations. In Proceedings of the Recommender Systems Challenge, pages 1–4. 2016.
- e-recruitment recommender systems: a systematic review. Knowledge and Information Systems, 63:1–20, 2021.
- Implicit skills extraction using document embedding and its use in job recommendation. In Proceedings of the AAAI conference on artificial intelligence, volume 34, pages 13286–13293, 2020.
- Learning effective representations for person-job fit by feature fusion. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management, pages 2549–2556, 2020.
- A hybrid approach to managing job offers and candidates. Information processing & management, 48(6):1124–1135, 2012.
- Using autoencoders for session-based job recommendations. User Modeling and User-Adapted Interaction, 30:617–658, 2020.
- Exploiting job transition patterns for effective job recommendation. In 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pages 2414–2419. IEEE, 2017.
- Combination of content-based user profiling and local collective embeddings for job recommendation. 2017.
- Job recommendation based on job seeker skills: An empirical study. In Text2Story@ ECIR, pages 47–51, 2018.
- A survey of large language models. arXiv preprint arXiv:2303.18223, 2023.
- Preetam Ghosh (9 papers)
- Vaishali Sadaphal (1 paper)