Learning to Retrieve for Job Matching
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , , , , , , , , , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2024
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866910338880897024 |
|---|---|
| author | Shen, Jianqiang Juan, Yuchin Zhang, Shaobo Liu, Ping Pu, Wen Vasudevan, Sriram Song, Qingquan Borisyuk, Fedor Shen, Kay Qianqi Wei, Haichao Ren, Yunxiang Chiou, Yeou S. Kuang, Sicong Yin, Yuan Zheng, Ben Wu, Muchen Gharghabi, Shaghayegh Wang, Xiaoqing Xue, Huichao Guo, Qi Hewlett, Daniel Simon, Luke Hong, Liangjie Zhang, Wenjing |
| author_facet | Shen, Jianqiang Juan, Yuchin Zhang, Shaobo Liu, Ping Pu, Wen Vasudevan, Sriram Song, Qingquan Borisyuk, Fedor Shen, Kay Qianqi Wei, Haichao Ren, Yunxiang Chiou, Yeou S. Kuang, Sicong Yin, Yuan Zheng, Ben Wu, Muchen Gharghabi, Shaghayegh Wang, Xiaoqing Xue, Huichao Guo, Qi Hewlett, Daniel Simon, Luke Hong, Liangjie Zhang, Wenjing |
| contents | Web-scale search systems typically tackle the scalability challenge with a two-step paradigm: retrieval and ranking. The retrieval step, also known as candidate selection, often involves extracting standardized entities, creating an inverted index, and performing term matching for retrieval. Such traditional methods require manual and time-consuming development of query models. In this paper, we discuss applying learning-to-retrieve technology to enhance LinkedIns job search and recommendation systems. In the realm of promoted jobs, the key objective is to improve the quality of applicants, thereby delivering value to recruiter customers. To achieve this, we leverage confirmed hire data to construct a graph that evaluates a seeker's qualification for a job, and utilize learned links for retrieval. Our learned model is easy to explain, debug, and adjust. On the other hand, the focus for organic jobs is to optimize seeker engagement. We accomplished this by training embeddings for personalized retrieval, fortified by a set of rules derived from the categorization of member feedback. In addition to a solution based on a conventional inverted index, we developed an on-GPU solution capable of supporting both KNN and term matching efficiently. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_13435 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Learning to Retrieve for Job Matching Shen, Jianqiang Juan, Yuchin Zhang, Shaobo Liu, Ping Pu, Wen Vasudevan, Sriram Song, Qingquan Borisyuk, Fedor Shen, Kay Qianqi Wei, Haichao Ren, Yunxiang Chiou, Yeou S. Kuang, Sicong Yin, Yuan Zheng, Ben Wu, Muchen Gharghabi, Shaghayegh Wang, Xiaoqing Xue, Huichao Guo, Qi Hewlett, Daniel Simon, Luke Hong, Liangjie Zhang, Wenjing Information Retrieval Machine Learning Web-scale search systems typically tackle the scalability challenge with a two-step paradigm: retrieval and ranking. The retrieval step, also known as candidate selection, often involves extracting standardized entities, creating an inverted index, and performing term matching for retrieval. Such traditional methods require manual and time-consuming development of query models. In this paper, we discuss applying learning-to-retrieve technology to enhance LinkedIns job search and recommendation systems. In the realm of promoted jobs, the key objective is to improve the quality of applicants, thereby delivering value to recruiter customers. To achieve this, we leverage confirmed hire data to construct a graph that evaluates a seeker's qualification for a job, and utilize learned links for retrieval. Our learned model is easy to explain, debug, and adjust. On the other hand, the focus for organic jobs is to optimize seeker engagement. We accomplished this by training embeddings for personalized retrieval, fortified by a set of rules derived from the categorization of member feedback. In addition to a solution based on a conventional inverted index, we developed an on-GPU solution capable of supporting both KNN and term matching efficiently. |
| title | Learning to Retrieve for Job Matching |
| topic | Information Retrieval Machine Learning |
| url | https://arxiv.org/abs/2402.13435 |