...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Journal of software >Development of an Intelligent Job Recommender System for Freelancers using Client’s Feedback Classification and Association Rule Mining Techniques
【24h】

Development of an Intelligent Job Recommender System for Freelancers using Client’s Feedback Classification and Association Rule Mining Techniques

机译：使用客户的反馈分类和关联规则挖掘技术为自由职业者开发智能工作推荐系统

获取原文
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Most of the freelancer's time is killed in finding suitable jobs due to the huge number of freelance marketplaces. Freelancing sites send email notifications or show in newsfeed about posted jobs but most of them are irrelevant. Recommending relevant jobs to freelancers to minimize job finding time has drawn the attraction of researchers. Here, in this paper, we propose a recommender system to find out appropriate jobs for freelancers using client’s feedback classification and Association rule mining techniques. After collecting the previous work history of freelancers, we analyze the sentiment of client's feedback using Logistic Regression and Linear Support Vector Machine model to classify the completed jobs into two categories: positive and negative. We apply the Association rule mining technique to find out freelancer's frequent skillsets used in both categories of completed jobs. Then, we find out the jobs matched with the positive frequent skillsets using set operations. We also discard jobs that contain negative frequent skillsets. Finally, a collaborative filtering algorithm is applied considering the client's overall rating, the minimum budget/ hourly rate, deadline, re-hire, etc. to generate a more accurate recommendation. After extensive experiments on the real dataset collected from different online marketplaces, we are able to prove that our proposed method correctly recommends the appropriate jobs with 83.40% (Logistic Regression) and 84.03% (Linear SVM) accuracy.
机译：由于大量的自由职业者市场，自由职业者的大部分时间都被浪费在寻找合适的工作上。自由职业者网站发送有关已发布工作的电子邮件通知或在新闻源中显示，但大多数与无关。向自由职业者推荐相关工作以最大程度地减少找工作的时间，吸引了研究人员的注意。在这里，本文提出了一种推荐系统，该系统使用客户的反馈分类和关联规则挖掘技术为自由职业者找到合适的工作。在收集了自由职业者的先前工作历史之后，我们使用Logistic回归和线性支持向量机模型分析了客户反馈的情绪，将完成的工作分为积极和消极两类。我们应用协会规则挖掘技术来找出在两类已完成工作中使用的自由职业者的常用技能。然后，我们使用集合操作找出与积极的频繁技能组匹配的工作。我们还会丢弃包含负面技能的作业。最后，考虑客户的整体评分，最低预算/每小时费用，截止日期，重新雇用等情况，应用协作过滤算法以生成更准确的推荐。经过对从不同在线市场收集的真实数据集进行的广泛实验，我们能够证明我们提出的方法正确地推荐了正确的工作，其准确度为83.40％（逻辑回归）和84.03％（线性SVM）。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号