Recently, data sparsity is still one of the critical problems faced by recommendation systems. Although many existing methods based on cross-domain can alleviate it to a certain extent, these methods only use the information of single-domain (e.g., user-side, item-side and rating-side) or dual-domain (e.g., user-rating-side, user-item-side and item-rating-side) to make recommendations, which results in performance degradation. In this paper, we propose a triple cross-domain collaborative filtering method to alleviate data sparsity, named TCD-CF. In TCD-CF method, the triple-side intrinsic characteristics are first obtained by using the joint nonnegative matrix factorization to integrate the user-side, item-side and rating-side domain knowledge. Then the extended codebook (as knowledge to transfer) based on these intrinsic characteristics is constructed by using the orthogonal nonnegative matrix tri-factorization. Finally, the codebook-based transfer method for cross-system CF is applied into the source domain and target domain to predict the missing ratings and perform recommendation in the target domain. Extensive experiments on two real-world datasets demonstrate that the proposed method outperforms the state-of-the-art methods for the cross-domain recommendation task.

最近，数据稀疏性仍然是推荐系统面临的关键问题之一。虽然现有的许多基于跨域的方法可以在一定程度上缓解这种情况，但这些方法仅使用单域（如用户侧、物品侧和评分侧）或双域（如用户-rating-side、user-item-side 和 item-rating-side）来提出建议，这会导致性能下降。在本文中，我们提出了一种三重跨域协同过滤方法来缓解数据稀疏性，称为 TCD-CF。在 TCD-CF 方法中，首先通过使用联合非负矩阵分解来整合用户端、项目端和评级端领域知识来获得三边内在特征。然后通过使用正交非负矩阵三因子分解构造基于这些内在特征的扩展码本（作为知识转移）。最后，将跨系统CF的基于码本的传输方法应用于源域和目标域，以预测丢失的评分并在目标域中进行推荐。对两个真实世界数据集的大量实验表明，所提出的方法优于跨域推荐任务的最新方法。