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.

翻译：偏好引导是一种主动学习方法，用于应对推荐系统的冷启动问题。简而言之，该方法通过要求新用户对若干精心选择的物品进行评分，从而为其计算合适的推荐。据我们所知，本文首次提出了一种基于SLIM（一种先进的Top-N推荐技术）的偏好引导方法。我们的方法主要包括一种称为贪婪SLIM的新型SLIM训练技术。该技术通过迭代选择训练物品，以贪婪方式最小化SLIM损失函数。我们通过离线实验和用户研究评估了这一新方法的性能。实验结果显著，特别是在用户研究方面表现突出。我们得出结论：与广泛使用的基于隐因子模型的方法相比，贪婪SLIM似乎更适合用于偏好引导任务。