A Ranker Ensemble for Multi-objective Job Recommendation in an Item Cold Start Setting
A. Murat Yağcı, Fikret Sadik Gürgen · 2017
Real-life recommender systems often have multiple objectives, and considering only a single stakeholder's perspective can be inadequate. ACM Recsys 2017 challenge requires such a multi-objective job recommender system taking into account job seeker satisfaction, recruiter satisfaction, balance between relevance and revenue, and scalability in a primarily item cold start setting. In this paper, we discuss our findings, and a possible solution to this problem. Making use of interaction profiles and content data, a hybrid of ranking algorithms is proposed to optimize the required objectives. This solution was able to achieve 8th position during A/B testing, and some of the ideas can be useful in a more complex ensemble.