Curriculum Vitae Recommendation Based on Text Mining

• URL: http://arxiv.org/abs/2007.11053v1
• Date: Tue, 21 Jul 2020 19:29:26 GMT
• Title: Curriculum Vitae Recommendation Based on Text Mining
• Authors: Honorio Apaza Alanoca, Americo A. Rubin de Celis Vidal, and Josimar Edinson Chire Saire
• Abstract summary: This research focuses on the problem: how we can take advantage from the growth of unstructured information about job offers and curriculum vitae on different websites for CV recommendation.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: During the last years, the development in diverse areas related to computer science and internet, allowed to generate new alternatives for decision making in the selection of personnel for state and private companies. In order to optimize this selection process, the recommendation systems are the most suitable for working with explicit information related to the likes and dislikes of employers or end users, since this information allows to generate lists of recommendations based on collaboration or similarity of content. Therefore, this research takes as a basis these characteristics contained in the database of curricula and job offers, which correspond to the Peruvian ambit, which highlights the experience, knowledge and skills of each candidate, which are described in textual terms or words. This research focuses on the problem: how we can take advantage from the growth of unstructured information about job offers and curriculum vitae on different websites for CV recommendation. So, we use the techniques from Text Mining and Natural Language Processing. Then, as a relevant technique for the present study, we emphasize the technique frequency of the Term - Inverse Frequency of the documents (TF-IDF), which allows identifying the most relevant CVs in relation to a job offer of website through the average values (TF-IDF). So, the weighted value can be used as a qualification value of the relevant curriculum vitae for the recommendation.

Related papers

• A Survey on Data Selection for Language Models [148.300726396877]
  Data selection methods aim to determine which data points to include in a training dataset. Deep learning is mostly driven by empirical evidence and experimentation on large-scale data is expensive. Few organizations have the resources for extensive data selection research.
  arXiv  Detail & Related papers  (2024-02-26T18:54:35Z)
• Learning to Retrieve for Job Matching [22.007634436648427]
  We discuss applying learning-to-retrieve technology to enhance LinkedIns job search and recommendation systems. We leverage confirmed hire data to construct a graph that evaluates a seeker's qualification for a job, and utilize learned links for retrieval. 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.
  arXiv  Detail & Related papers  (2024-02-21T00:05:25Z)
• JobRecoGPT -- Explainable job recommendations using LLMs [1.6317061277457001]
  Large Language Models (LLMs) have taken over the AI field by storm with extraordinary performance in fields where text-based data is available. In this study, we compare performance of four different approaches for job recommendations.
  arXiv  Detail & Related papers  (2023-09-21T06:25:28Z)
• Impression-Aware Recommender Systems [57.38537491535016]
  Novel data sources bring new opportunities to improve the quality of recommender systems. Researchers may use impressions to refine user preferences and overcome the current limitations in recommender systems research. We present a systematic literature review on recommender systems using impressions.
  arXiv  Detail & Related papers  (2023-08-15T16:16:02Z)
• Exploring Large Language Model for Graph Data Understanding in Online Job Recommendations [63.19448893196642]
  We present a novel framework that harnesses the rich contextual information and semantic representations provided by large language models to analyze behavior graphs. By leveraging this capability, our framework enables personalized and accurate job recommendations for individual users.
  arXiv  Detail & Related papers  (2023-07-10T11:29:41Z)
• Generative Job Recommendations with Large Language Model [32.99532175346021]
  GIRL (GeneratIve job Recommendation based on Large language models) is a novel approach inspired by recent advancements in the field of Large Language Models (LLMs) We employ a Supervised Fine-Tuning (SFT) strategy to instruct the LLM-based generator in crafting suitable Job Descriptions (JDs) based on the Curriculum Vitae (CV) of a job seeker. In particular, GIRL serves as a job seeker-centric generative model, providing job suggestions without the need of a candidate set.
  arXiv  Detail & Related papers  (2023-07-05T09:58:08Z)
• Turtle Score -- Similarity Based Developer Analyzer [0.0]
  This research aims to comprehend, analyze and automatically produce convincing outcomes to find a candidate who perfectly fits right in the company. Data is examined and collected for each employee who works in the IT domain focusing on their performance measure. It's been proven that the efficiency and capability of a particular worker go higher when working with a person of a similar personality.
  arXiv  Detail & Related papers  (2022-05-10T13:22:11Z)
• JobBERT: Understanding Job Titles through Skills [12.569546741576515]
  Job titles form a cornerstone of today's human resources (HR) processes. Job titles are a compact, convenient, and readily available data source. We propose a neural representation model for job titles, by augmenting a pre-trained language model with co-occurrence information from skill labels extracted from vacancies.
  arXiv  Detail & Related papers  (2021-09-20T15:00:10Z)
• Reasoning over Vision and Language: Exploring the Benefits of Supplemental Knowledge [59.87823082513752]
  This paper investigates the injection of knowledge from general-purpose knowledge bases (KBs) into vision-and-language transformers. We empirically study the relevance of various KBs to multiple tasks and benchmarks. The technique is model-agnostic and can expand the applicability of any vision-and-language transformer with minimal computational overhead.
  arXiv  Detail & Related papers  (2021-01-15T08:37:55Z)
• Curriculum Manager for Source Selection in Multi-Source Domain Adaptation [65.22251010276652]
  We propose an adversarial agent that learns a dynamic curriculum for source samples, called Curriculum Manager for Source Selection (CMSS) CMSS does not require any knowledge of the domain labels, yet it outperforms other methods on four well-known benchmarks by significant margins.
  arXiv  Detail & Related papers  (2020-07-02T17:15:01Z)
• Context-Based Quotation Recommendation [60.93257124507105]
  We propose a novel context-aware quote recommendation system. It generates a ranked list of quotable paragraphs and spans of tokens from a given source document. We conduct experiments on a collection of speech transcripts and associated news articles.
  arXiv  Detail & Related papers  (2020-05-17T17:49:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.