A bottom-up approach to job recommendation system

Abstract

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.