Cross-domain sequential recommendation (CDSR) aims to address the data
sparsity problems that exist in traditional sequential recommendation (SR)
systems.
The existing approaches aim to design a specific cross-domain unit that can
transfer and propagate information across multiple domains by relying on
overlapping users with abundant behaviors. However, in real-world recommender
systems, CDSR scenarios usually consist of a majority of long-tailed users with
sparse behaviors and cold-start users who only exist in one domain. This leads
to a drop in the performance of existing CDSR methods in the real-world
industry platform. Therefore, improving the consistency and effectiveness of
models in open-world CDSR scenarios is crucial for constructing CDSR models
(\textit{1st} CH). Recently, some SR approaches have utilized auxiliary
behaviors to complement the information for long-tailed users. However, these
multi-behavior SR methods cannot deliver promising performance in CDSR, as they
overlook the semantic gap between target and auxiliary behaviors, as well as
user interest deviation across domains (\textit{2nd} CH).

跨领域的顺序推荐（CDSR）的关键问题是解决传统顺序推荐系统中存在的数据稀疏问题。然而，在现实世界的推荐系统中，CDSR 场景通常包含大多数行为稀疏和冷启动用户，这导致现有 CDSR 方法在实际产业平台中性能下降。因此，在开放世界的 CDSR 场景中提高模型的一致性和效果对于构建 CDSR 模型至关重要。

实现开放世界的跨领域顺序推荐： 一种模型无关的对比去噪方法

Towards Open-world Cross-Domain Sequential Recommendation: A  Model-Agnostic Contrastive Denoising Approach

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。