Practical Lessons for Job Recommendations in the Cold-Start Scenario

The 2017 ACM RecSys Challenge focuses on the problem of job recommendations on XING in a cold-start scenario. In this paper we describe our solution as well as some practical lessons learned from the competition. We model this task as a binary classification problem. Negative candidate selection is the first key phase in our solution. We design a negative sampling strategy which performs significantly better than taking users' deleted or unclicked items as negative candidates. We then extract comprehensive features to model the relationship between a user-job candidate, including the direct profile similarity between the user and the job, and the profile similarity between the user's historical interested jobs and the target job. To make the whole pipeline scalable and easy to deploy online, we decide to use a single boosting tree model as the final discriminative model, instead of using a stacking ensemble of multiple models. Overall our model ranked 5th on the challenge leaderboard, and our last model has remained in 2nd place during the last two online weeks. We have open-sourced our implementation on https://github.com/Leavingseason/RecsysChallenge2017.