Cross Domain Recommendation (CDR) has been popularly studied to alleviate the
cold-start and data sparsity problem commonly existed in recommender systems.
CDR models can improve the recommendation performance of a target domain by
leveraging the data of other source domains. However, most existing CDR models
assume information can directly 'transfer across the bridge', ignoring the
privacy issues. To solve the privacy concern in CDR, in this paper, we propose
a novel two stage based privacy-preserving CDR framework (PriCDR). In the first
stage, we propose two methods, i.e., Johnson-Lindenstrauss Transform (JLT)
based and Sparse-awareJLT (SJLT) based, to publish the rating matrix of the
source domain using differential privacy. We theoretically analyze the privacy
and utility of our proposed differential privacy based rating publishing
methods. In the second stage, we propose a novel heterogeneous CDR model
(HeteroCDR), which uses deep auto-encoder and deep neural network to model the
published source rating matrix and target rating matrix respectively. To this
end, PriCDR can not only protect the data privacy of the source domain, but
also alleviate the data sparsity of the source domain. We conduct experiments
on two benchmark datasets and the results demonstrate the effectiveness of our
proposed PriCDR and HeteroCDR.

本文提出了一种新颖的基于差分隐私的两阶段隐私保存交叉领域推荐框架（PriCDR），该框架可以使用 Johnson-Lindenstrauss 变换和深度自动编码器进行数据隐私保护和数据丰富， 通过在两个基准数据集上的实验，验证了该框架的有效性和可行性。

隐私保护的跨领域推荐差分隐私知识迁移

Differential Private Knowledge Transfer for Privacy-Preserving  Cross-Domain Recommendation

Cross domain recommendation (CDR) is one popular research topic in
recommender systems. This paper focuses on a popular scenario for CDR where
different domains share the same set of users but no overlapping items. The
majority of recent methods have explored the shared-user representation to
transfer knowledge across domains. However, the idea of shared-user
representation resorts to learn the overlapped features of user preferences and
suppresses the domain-specific features. Other works try to capture the
domain-specific features by an MLP mapping but require heuristic human
knowledge of choosing samples to train the mapping. In this paper, we attempt
to learn both features of user preferences in a more principled way. We assume
that each user's preferences in one domain can be expressed by the other one,
and these preferences can be mutually converted to each other with the
so-called equivalent transformation. Based on this assumption, we propose an
equivalent transformation learner (ETL) which models the joint distribution of
user behaviors across domains. The equivalent transformation in ETL relaxes the
idea of shared-user representation and allows the learned preferences in
different domains to preserve the domain-specific features as well as the
overlapped features. Extensive experiments on three public benchmarks
demonstrate the effectiveness of ETL compared with recent state-of-the-art
methods. Codes and data are available
online:~https://github.com/xuChenSJTU/ETL-master

本文主要研究了不同领域共享相同用户但没有重叠项目的情况下的交叉领域推荐问题，并提出了一种基于等效转换学习的方法，该方法可以在不压制领域特定特征的前提下，学习用户喜好的两种特征并相互转换，相关实验表明，该方法比当前最先进的方法更加有效。