Cross-domain recommendation (CDR) aims to enhance recommendation accuracy in
a target domain with sparse data by leveraging rich information in a source
domain, thereby addressing the data-sparsity problem. Some existing CDR methods
highlight the advantages of extracting domain-common and domain-specific
features to learn comprehensive user and item representations. However, these
methods can't effectively disentangle these components as they often rely on
simple user-item historical interaction information (such as ratings, clicks,
and browsing), neglecting the rich multi-modal features. Additionally, they
don't protect user-sensitive data from potential leakage during knowledge
transfer between domains. To address these challenges, we propose a
Privacy-Preserving Framework with Multi-Modal Data for Cross-Domain
Recommendation, called P2M2-CDR. Specifically, we first design a multi-modal
disentangled encoder that utilizes multi-modal information to disentangle more
informative domain-common and domain-specific embeddings. Furthermore, we
introduce a privacy-preserving decoder to mitigate user privacy leakage during
knowledge transfer. Local differential privacy (LDP) is utilized to obfuscate
the disentangled embeddings before inter-domain exchange, thereby enhancing
privacy protection. To ensure both consistency and differentiation among these
obfuscated disentangled embeddings, we incorporate contrastive learning-based
domain-inter and domain-intra losses. Extensive Experiments conducted on four
real-world datasets demonstrate that P2M2-CDR outperforms other
state-of-the-art single-domain and cross-domain baselines.

跨域推荐的隐私保护框架 (P2M2-CDR) 利用多模态数据来解决数据稀疏问题，通过多模态解耦编码器对域公共和域特定嵌入进行分离，并引入隐私保护解码器进行用户隐私保护，以对抗学习为基础的域间和域内损失函数确保解耦嵌入的一致性和区分度，实验证明 P2M2-CDR 在四个真实数据集上优于其他领先的单域和跨域基准模型。

跨域推荐的多模态数据隐私保护框架

A Privacy-Preserving Framework with Multi-Modal Data for Cross-Domain  Recommendation

Multivariate time-series data in numerous real-world applications (e.g.,
healthcare and industry) are informative but challenging due to the lack of
labels and high dimensionality. Recent studies in self-supervised learning have
shown their potential in learning rich representations without relying on
labels, yet they fall short in learning disentangled embeddings and addressing
issues of inductive bias (e.g., transformation-invariance). To tackle these
challenges, we propose TimeDRL, a generic multivariate time-series
representation learning framework with disentangled dual-level embeddings.
TimeDRL is characterized by three novel features: (i) disentangled derivation
of timestamp-level and instance-level embeddings from patched time-series data
using a [CLS] token strategy; (ii) utilization of timestamp-predictive and
instance-contrastive tasks for disentangled representation learning, with the
former optimizing timestamp-level embeddings with predictive loss, and the
latter optimizing instance-level embeddings with contrastive loss; and (iii)
avoidance of augmentation methods to eliminate inductive biases, such as
transformation-invariance from cropping and masking. Comprehensive experiments
on 6 time-series forecasting datasets and 5 time-series classification datasets
have shown that TimeDRL consistently surpasses existing representation learning
approaches, achieving an average improvement of forecasting by 57.98% in MSE
and classification by 1.25% in accuracy. Furthermore, extensive ablation
studies confirmed the relative contribution of each component in TimeDRL's
architecture, and semi-supervised learning evaluations demonstrated its
effectiveness in real-world scenarios, even with limited labeled data.

TimeDRL 是一个通用的多变量时间序列表示学习框架，通过解耦的双层嵌入技术，利用预测和对比损失来学习时间戳级别和实例级别的嵌入，避免了数据增强方法引起的归纳偏差，并在多个实验中表现出优于现有方法的性能。

TimeDRL：多变量时序数据的解缠结表示学习

TimeDRL: Disentangled Representation Learning for Multivariate  Time-Series

Heterogeneous information network has been widely used to alleviate sparsity
and cold start problems in recommender systems since it can model rich context
information in user-item interactions. Graph neural network is able to encode
this rich context information through propagation on the graph. However,
existing heterogeneous graph neural networks neglect entanglement of the latent
factors stemming from different aspects. Moreover, meta paths in existing
approaches are simplified as connecting paths or side information between node
pairs, overlooking the rich semantic information in the paths. In this paper,
we propose a novel disentangled heterogeneous graph attention network DisenHAN
for top-$N$ recommendation, which learns disentangled user/item representations
from different aspects in a heterogeneous information network. In particular,
we use meta relations to decompose high-order connectivity between node pairs
and propose a disentangled embedding propagation layer which can iteratively
identify the major aspect of meta relations. Our model aggregates corresponding
aspect features from each meta relation for the target user/item. With
different layers of embedding propagation, DisenHAN is able to explicitly
capture the collaborative filtering effect semantically. Extensive experiments
on three real-world datasets show that DisenHAN consistently outperforms
state-of-the-art approaches. We further demonstrate the effectiveness and
interpretability of the learned disentangled representations via insightful
case studies and visualization.

本研究提出了一种新的分离异构图注意力网络 DisenHAN，可以从不同方面学习异构信息网络中的用户 / 项目表示，并使用高阶元关系分解元路径，聚合每个元关系的相应特征，以实现更好的推荐效果。