Various kinds of auxiliary data in web services have been proved to be valuable to handler data sparsity and cold-start problems of recommendation. However, it is challenging to develop effective approaches to model and utilize these various and complex information. Due to the flexibility in modelling data heterogeneity, heterogeneous information network (HIN) has been adopted to model auxiliary data for sparsity recommendation, named HIN based recommendation. But most of these HIN based methods rely on meta path-based similarity or graph embedding, which cannot fully mine global structure and semantic features of users and items. Besides, these methods, utilizing extended matrix factorization model or deep learning model, suffer expensive model-building problem and cannot treat personal latent factors carefully since their global objective functions. In this paper, we model both rate and auxiliary data through a unified graph and propose a graph filtering (GF) recommendation method on HINs. Distinct from traditional HIN based methods, GF uses a rate pair structure to represent user's feedback information and predict the rating that says, “a predicted rating depends on its similar rating pairs.” Concretely, we design a semantic and sign value-aware similarity measure based on SimRank, named Constrained SimRank, to weight rating pair similarities on the unified graph and compute the predicting rate score for an active user via weighted average of all similar ratings. Various semantics behind edges of the unified graph have different contributions for the prediction. Thus, an adaptive framework is proposed to learn the weights of different semantic edges and products an optimized predicted rating. Finally, experimental studies on various real-world datasets demonstrate that GF is effective to handler the sparsity issue of recommendation and outperforms the state-of-the-art techniques.

中文翻译：

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异构信息网络推荐的图过滤


Web服务中的各种辅助数据已被证明对于处理数据稀疏和推荐的冷启动问题很有价值。然而，开发有效的方法来建模和利用这些多样化且复杂的信息具有挑战性。由于数据异构性建模的灵活性，异构信息网络（HIN）被用来对稀疏推荐的辅助数据进行建模，称为基于HIN的推荐。但这些基于 HIN 的方法大多数依赖于基于元路径的相似性或图嵌入，无法充分挖掘用户和项目的全局结构和语义特征。此外，这些方法利用扩展矩阵分解模型或深度学习模型，存在昂贵的模型构建问题，并且由于其全局目标函数而无法仔细对待个人潜在因素。在本文中，我们通过统一的图对速率数据和辅助数据进行建模，并提出了一种 HIN 上的图过滤（GF）推荐方法。与传统的基于 HIN 的方法不同，GF 使用评分对结构来表示用户的反馈信息并预测评分，即“预测的评分取决于其相似的评分对”。具体来说，我们设计了一种基于 SimRank 的语义和符号值感知相似性度量，名为 Constrained SimRank，在统一图上对评分对相似性进行加权，并通过所有相似评分的加权平均值计算活跃用户的预测率得分。统一图边缘背后的各种语义对预测有不同的贡献。因此，提出了一种自适应框架来学习不同语义边缘的权重并产生优化的预测评分。 最后，对各种真实数据集的实验研究表明，GF 可以有效处理推荐的稀疏性问题，并且优于最先进的技术。