Preference elicitation is an active learning approach to tackle the
cold-start problem of recommender systems. Roughly speaking, new users are
asked to rate some carefully selected items in order to compute appropriate
recommendations for them. To the best of our knowledge, we are the first to
propose a method for preference elicitation that is based on SLIM , a
state-of-the-art technique for top-N recommendation. Our approach mainly
consists of a new training technique for SLIM, which we call Greedy SLIM. This
technique iteratively selects items for the training in order to minimize the
SLIM loss greedily. We conduct offline experiments as well as a user study to
assess the performance of this new method. The results are remarkable,
especially with respect to the user study. We conclude that Greedy SLIM seems
to be more suitable for preference elicitation than widely used methods based
on latent factor models.

基于 SLIM 的贪心 SLIM 方法是用于偏好获取的一种新的训练技术，对于偏好获取比基于潜在因素模型的广泛使用的方法更加适用，通过离线实验和用户研究来评估其性能，结果表明该方法在用户研究方面表现出色。