Job recommendation based on factorization machine and topic modelling

This paper describes our solution for the RecSys Challenge 2016. In the challenge, several datasets were provided from a social network for business XING. The goal of the competition was to use these data to predict job postings that a user will interact positively with (click, bookmark or reply). Our solution to this problem includes three different types of models: Factorization Machine, item-based collaborative filtering, and content-based topic model on tags. Thus, we combined collaborative and content-based approaches in our solution. Our best submission, which was a blend of ten models, achieved 7th place in the challenge's final leader-board with a score of 1677 898.52. The approaches presented in this paper are general and scalable. Therefore they can be applied to another problem of this type.