Interview process learning for top-n recommendation

In the field of recommendation system research, a key challenge is how to effectively recommend items for new users, a problem generally known as cold-start recommendation. In order to alleviate cold-start problem, recently systems try to get the users' interests by progressively querying users' preference on predefined items. Constructing the query process via machine learning based techniques becomes an important direction to solve cold-start problem. In this paper, we propose a novel interview process learning algorithm. Different from previous approaches which focus on rate prediction, our model is able to handle wide ranges of loss functions and can be used in collaborative ranking task. Experimental results on three real world recommendation dataset demonstrate that our proposed method outperforms several baseline methods.