Cross-domain collaborative filtering has attracted a great deal of attention for its capability of dealing with data sparsity by transferring valuable knowledge from auxiliary domains to assist in recommendation in a target domain. State-of-the-art research has mainly focused on the scenario in which auxiliary domains share users or items with a target domain. However, such auxiliary data is rare or the acquisition of them is limited due to privacy concerns in real-world applications. We investigate a more realistic scenario in which auxiliary domains have neither users nor items overlapped with a target domain, thereby facilitating the collection of auxiliary data. In order to extract and transfer the sharing knowledge between auxiliary domains and target domain, we assume the user-item feedback in each domain consist of two parts: domain-transferrable information containing the shared knowledge and domain-reserved information reflecting the domain-specific characteristics. Correspondingly, we propose a mixed heterogeneous factorization model to capture the sharing knowledge and the domain-specific characteristics based on adapted tensor factorization and biased matrix factorization respectively and then combine them together in an accumulative way. Meanwhile, three types of domain heterogeneity including preference heterogeneity, characteristics heterogeneity and rating bias are taken into account in this model. Experimenting on four publicly available datasets across different domains, we show that our model is superior to state-of-the-art methods in rating prediction and top-N recommendation tasks.

跨域协同过滤因其通过从辅助域转移有价值的知识来辅助目标域中的推荐来处理数据稀疏性的能力而引起了极大的关注。最先进的研究主要集中在辅助域与目标域共享用户或项目的场景。然而，由于现实世界应用中的隐私问题，这种辅助数据很少见，或者它们的获取受到限制。我们研究了一个更现实的场景，其中辅助域既没有用户也没有项目与目标域重叠，从而促进辅助数据的收集。为了提取和传递辅助域和目标域之间的共享知识，我们假设每个域中的用户-项目反馈由两部分组成：包含共享知识和反映特定领域特征的领域保留信息的领域可转移信息。相应地，我们提出了一种混合异构分解模型，分别基于自适应张量分解和偏置矩阵分解来捕获共享知识和特定领域的特征，然后以累积的方式将它们组合在一起。同时，该模型考虑了偏好异质性、特征异质性和评级偏差三类领域异质性。在不同领域的四个公开可用数据集上进行实验，我们表明我们的模型在评级预测和顶级方法方面优于最先进的方法。我们提出了一种混合异构分解模型，分别基于自适应张量分解和偏置矩阵分解来捕获共享知识和特定领域的特征，然后以累积的方式将它们组合在一起。同时，该模型考虑了偏好异质性、特征异质性和评级偏差三类领域异质性。在不同领域的四个公开可用数据集上进行实验，我们表明我们的模型在评级预测和顶级方法方面优于最先进的方法。我们提出了一种混合异构分解模型，分别基于自适应张量分解和偏置矩阵分解来捕获共享知识和特定领域的特征，然后以累积的方式将它们组合在一起。同时，该模型考虑了偏好异质性、特征异质性和评级偏差三类领域异质性。在不同领域的四个公开可用数据集上进行实验，我们表明我们的模型在评级预测和顶级方法方面优于最先进的方法。该模型考虑了特征异质性和评级偏差。在不同领域的四个公开可用数据集上进行实验，我们表明我们的模型在评级预测和顶级方法方面优于最先进的方法。该模型考虑了特征异质性和评级偏差。在不同领域的四个公开可用数据集上进行实验，我们表明我们的模型在评级预测和顶级方法方面优于最先进的方法。N 个推荐任务。