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.
翻译:在学术界和行业中,建议系统都是一项广泛研究的任务。以前的工作主要侧重于同质建议,对异质建议系统进展甚微。然而,各种建议,例如建议产品、视频、名人购物说明等不同种类的项目,如今占主导地位。最先进的方法无法利用不同种类项目的属性,从而受数据分散问题的影响。联合代表不同特征空间的项目确实非常困难。为了解决这一问题,我们提议一个内核神经网络,即对异质建议进行深度统一代表(或说明),联合建模异质项目的统一表述,同时保留其原有的地貌空间表层结构。理论上,我们证明拟议模型的代表性。此外,我们还对真实世界数据集进行了广泛的实验。实验结果表明,通过统一代表,我们的模型在现有状态模型上取得了显著的改进(例如,4.1% 至 34.9% 由奥地利联合公司分数提升,3.7% 由在线CTR提升)。