Learning User Representations with Hypercuboids for Recommender Systems
This addresses the challenge of capturing diverse user interests for personalized recommendations, though it appears incremental as it builds on existing collaborative filtering with a novel geometric representation.
The paper tackles the problem of modeling user interests in recommender systems by representing users as hypercuboids instead of points, which improves geometric flexibility and modeling capability. Empirical results show the approach outperforms state-of-the-art methods on public and commercial datasets.
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.