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。