Summary of Forecasting Application Counts in Talent Acquisition Platforms: Harnessing Multimodal Signals Using Lms, by Md Ahsanul Kabir et al.
Forecasting Application Counts in Talent Acquisition Platforms: Harnessing Multimodal Signals using LMs
by Md Ahsanul Kabir, Kareem Abdelfatah, Shushan He, Mohammed Korayem, Mohammad Al Hasan
First submitted to arxiv on: 19 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel machine learning (ML) approach for application count forecasting in recruitment, which is essential for designing effective outreach activities to attract qualified applicants. The authors discuss how existing auto-regressive based time series forecasting methods perform poorly for this task and instead propose a multimodal language model-based method that fuses job-posting metadata of various modalities through a simple encoder. Experiments on large real-life datasets from CareerBuilder LLC demonstrate the effectiveness of the proposed method over existing state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper talks about how companies are using machine learning to help them find the right people for jobs. Right now, most of these systems just try to match job descriptions with resumes or skills, but this new approach tries to predict how many applications a company will get based on things like the job posting itself. The authors show that existing methods don’t work very well and then propose their own way of doing it using language models. They test it on real data from CareerBuilder LLC and find that it works better than other approaches. |
Keywords
» Artificial intelligence » Encoder » Language model » Machine learning » Time series
