Recommendation system has been a widely studied task both in academia and
industry. Previous works mainly focus on homogeneous recommendation and little
progress has been made for heterogeneous recommender systems. However,
heterogeneous recommendations, e.g., recommending different types of items
including products, videos, celebrity shopping notes, among many others, are
dominant nowadays. State-of-the-art methods are incapable of leveraging
attributes from different types of items and thus suffer from data sparsity
problems. And it is indeed quite challenging to represent items with different
feature spaces jointly. To tackle this problem, we propose a kernel-based
neural network, namely deep unified representation (or DURation) for
heterogeneous recommendation, to jointly model unified representations of
heterogeneous items while preserving their original feature space topology
structures. Theoretically, we prove the representation ability of the proposed
model. Besides, we conduct extensive experiments on real-world datasets.
Experimental results demonstrate that with the unified representation, our
model achieves remarkable improvement (e.g., 4.1% ~ 34.9% lift by AUC score and
3.7% lift by online CTR) over existing state-of-the-art models.

该论文提出了一种基于内核的神经网络，即深度统一表示 (DURation)，用于异构推荐，通过联合建模异构项目的统一表示来解决数据稀疏性问题，并在现实世界数据集上进行了广泛的实验，表明该模型比现有的最先进模型具有显着的提升。