Modeling user interests is crucial in real-world recommender systems. In this
paper, we present a new user interest representation model for personalized
recommendation. Specifically, the key novelty behind our model is that it
explicitly models user interests as a hypercuboid instead of a point in the
space. In our approach, the recommendation score is learned by calculating a
compositional distance between the user hypercuboid and the item. This helps to
alleviate the potential geometric inflexibility of existing collaborative
filtering approaches, enabling a greater extent of modeling capability.
Furthermore, we present two variants of hypercuboids to enhance the capability
in capturing the diversities of user interests. A neural architecture is also
proposed to facilitate user hypercuboid learning by capturing the activity
sequences (e.g., buy and rate) of users. We demonstrate the effectiveness of
our proposed model via extensive experiments on both public and commercial
datasets. Empirical results show that our approach achieves very promising
results, outperforming existing state-of-the-art.

本文提出了一种新的用户兴趣表示模型 —— 超立方体模型，能够较好地建模用户兴趣，通过计算用户超立方体与物品之间的组合距离，可以更好地捕捉用户兴趣的多样性，实验结果表明，该模型优于现有的协同过滤方法并取得了很好的推荐效果。