  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.
