Collaborative filtering (CF) has been one of the most important and popular recommendation methods, which aims at predicting users’ preferences (ratings) based on their past behaviors. Recently, various types of side information beyond the explicit ratings users give to items, such as social connections among users and metadata of items, have been introduced into CF and shown to be useful for improving recommendation performance. However, previous works process different types of information separately, thus failing to capture the correlations that might exist across them. To address this problem, in this work, we study the application of heterogeneous information network (HIN), which offers a unifying and flexible representation of different types of side information, to enhance CF-based recommendation methods. However, we face challenging issues in HIN-based recommendation, i.e., how to capture similarities of complex semantics between users and items in a HIN, and how to effectively fuse these similarities to improve final recommendation performance. To address these issues, we apply metagraph to similarity computation and solve the information fusion problem with a “matrix factorization (MF) + factorization machine (FM)” framework. For the MF part, we obtain the user-item similarity matrix from each metagraph and then apply low-rank matrix approximation to obtain latent features for both users and items. For the FM part, we apply FM with Group lasso (FMG) on the features obtained from the MF part to train the recommending model and, at the same time, identify the useful metagraphs. Besides FMG, a two-stage method, we further propose an end-to-end method, hierarchical attention fusing, to fuse metagraph-based similarities for the final recommendation. Experimental results on four large real-world datasets show that the two proposed frameworks significantly outperform existing state-of-the-art methods in terms of recommendation performance.

中文翻译：

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异构信息网络上推荐系统的边信息融合

协同过滤（CF）一直是最重要和最流行的推荐方法之一，旨在根据用户过去的行为预测用户的偏好（评分）。最近，除了用户对项目的明确评价之外，各种类型的辅助信息，例如用户之间的社交联系和项目的元数据，已被引入 CF，并被证明对提高推荐性能很有用。然而，以前的工作分别处理不同类型的信息，因此无法捕捉它们之间可能存在的相关性。为了解决这个问题，在这项工作中，我们研究了异构信息网络（HIN）的应用，它提供了不同类型的边信息的统一和灵活的表示，以增强基于 CF 的推荐方法。然而，我们在基于 HIN 的推荐中面临着具有挑战性的问题，即如何捕捉 HIN 中用户和项目之间复杂语义的相似性，以及如何有效地融合这些相似性以提高最终推荐性能。为了解决这些问题，我们将元图应用于相似度计算，并使用“矩阵分解（MF）+分解机（FM）”框架解决信息融合问题。对于 MF 部分，我们从每个元图获得用户-项目相似度矩阵，然后应用低秩矩阵近似来获得用户和项目的潜在特征。对于 FM 部分，我们对从 MF 部分获得的特征应用 FM with Group lasso (FMG) 来训练推荐模型，同时识别有用的元图。除了两阶段方法 FMG 之外，我们还提出了一种端到端方法，分层注意力融合，融合基于元图的相似性以进行最终推荐。在四个大型现实世界数据集上的实验结果表明，这两个提出的框架在推荐性能方面明显优于现有的最先进方法。