The initial interaction of a user with a recommender system is problematic because, in such a so-called cold start situation, the recommender system has very little information about the user, if any. Moreover, in collaborative filtering, users need to share their preferences with the service provider by rating items while in content-based filtering there is no need for such information sharing. We have recently shown that a content-based model that uses hypercube graphs can determine user preferences with a very limited number of ratings while better preserving user privacy. In this paper, we confirm these findings on the basis of experiments with more than 1,000 users in the restaurant and movie domains. We show that the proposed method outperforms standard machine learning algorithms when the number of available ratings is at most 10, which often happens, and is competitive with larger training sets. In addition, training is simple and does not require large computational efforts.

翻译：用户与推荐系统的初始交互存在困难，因为在所谓的冷启动情境下，推荐系统对用户的信息几乎一无所知（即使有也非常有限）。此外，在协同过滤中，用户需要通过评分向服务提供商分享其偏好，而基于内容的过滤则无需此类信息共享。我们近期已证明，一种利用超立方体图的基于内容模型能够在仅需极少数评分的情况下确定用户偏好，同时更好地保护用户隐私。本文基于餐厅和电影领域超过1000名用户的实验验证了这些发现。研究表明，当可用评分数量不超过10个（这种情况时常发生）时，所提方法优于标准机器学习算法；而在训练集规模较大时，该方法也具备竞争力。此外，模型训练过程简单，无需大量计算资源。