自下而上的工作推荐系统

Sonu K. Mishra, Manoj Reddy
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引用次数: 15

摘要

如今,推荐系统在网络上无处不在。今天的大多数网站都在努力为他们的客户提供高质量的推荐,以增加和留住他们的客户。在本文中,我们提出了为基于职业的社交网站XING设计一个职位推荐系统的方法。我们采用自下而上的方法:我们从深入理解和探索数据开始,逐步构建系统的小部分。我们还考虑了推荐系统的传统方法,如协同过滤,并讨论了其性能。我们得到的最好的模型是基于梯度增强算法的。我们的实验证明了我们方法的有效性。这项工作是基于2016年ACM RecSys会议组织的挑战。我们最终获得了1,411,119.11的满分,在官方排行榜上排名第20位。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A bottom-up approach to job recommendation system
Recommendation Systems are omnipresent on the web nowadays. Most websites today are striving to provide quality recommendations to their customers in order to increase and retain their customers. In this paper, we present our approaches to design a job recommendation system for a career based social networking website - XING. We take a bottom up approach: we start with deeply understanding and exploring the data and gradually build the smaller bits of the system. We also consider traditional approaches of recommendation systems like collaborative filtering and discuss its performance. The best model that we produced is based on Gradient Boosting algorithm. Our experiments show the efficacy of our approaches. This work is based on a challenge organized by ACM RecSys conference 2016. We achieved a final full score of 1,411,119.11 with rank 20 on the official leader board.
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