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A unified model for recommendation with selective neighborhood modeling
Information Processing & Management ( IF 7.4 ) Pub Date : 2020-08-28 , DOI: 10.1016/j.ipm.2020.102363
Jingwei Ma , Jiahui Wen , Panpan Zhang , Mingyang Zhong , Guangda Zhang , Xue Li

Neighborhood-based recommenders are a major class of Collaborative Filtering models. The intuition is to exploit neighbors with similar preferences for bridging unseen user-item pairs and alleviating data sparseness, in other words, learn the sub-graph representation of each user in a user graph. Many existing works propose neural attention networks to aggregate neighbors and place higher weights on the specific subsets of users for recommendation. However, the neighborhood information is not necessarily always informative, and the noises in the neighborhood can negatively affect the model performance. To address this issue, we propose a novel neighborhood-based recommender, where a hybrid gated network is designed to automatically separate similar neighbors from dissimilar (noisy) ones, and aggregate those similar neighbors to comprise neighborhood representations. The confidence in the neighborhood is also addressed by putting higher weights on the neighborhood representations if we are confident with the neighborhood information, and vice versa. In addition, a user-neighbor component is proposed to explicitly regularize user-neighbor proximity in latent space. These two components are combined into a unified model to complement each other for the recommendation task. Extensive experiments on three public datasets demonstrate that the proposed model consistently outperforms the state-of-the-art neighborhood-based recommenders. Furthermore, we study different variants of the proposed model to justify the underlying intuition of the proposed hybrid gated network and user-neighbor modeling components.



中文翻译:

带有推荐邻域建模的统一推荐模型

基于邻域的推荐者是协作过滤模型的主要类别。直觉是利用具有相似偏好的邻居来桥接看不见的用户-项对并减轻数据稀疏性,换句话说,学习用户图中每个用户的子图表示。许多现有的工作提出了神经注意力网络来聚集邻居,并在特定的用户子集上施加更高的权重以进行推荐。但是,邻域信息不一定总是提供信息,并且邻域中的噪声可能会对模型性能产生负面影响。为了解决这个问题,我们提出了一种新颖的基于邻域的推荐器,其中混合门控网络旨在自动将相似的邻居与不相似的(嘈杂的)邻居分开,并汇总这些相似的邻居以组成邻域表示。如果我们对邻域信息有信心,也可以通过对邻域表示法赋予更高的权重来解决对邻域的信心,反之亦然。另外,提出了一个用户邻居组件来显式规范潜在空间中的用户邻居接近度。这两个组件组合成一个统一的模型,以相互补充以完成推荐任务。在三个公共数据集上进行的大量实验表明,所提出的模型始终优于最新的基于邻域的推荐器。此外,我们研究了提出的模型的不同变体,以证明提出的混合门控网络和用户邻居建模组件的基本直觉是正确的。如果我们对邻域信息有信心,也可以通过对邻域表示法赋予更高的权重来解决对邻域的信心,反之亦然。此外,提出了一个用户邻居组件来显式规范潜在空间中的用户邻居接近度。这两个组件组合成一个统一的模型,以相互补充以完成推荐任务。在三个公共数据集上进行的大量实验表明,所提出的模型始终优于最新的基于邻域的推荐器。此外,我们研究了提出的模型的不同变体,以证明提出的混合门控网络和用户邻居建模组件的基本直觉是正确的。如果我们对邻域信息有信心,也可以通过对邻域表示法赋予更高的权重来解决对邻域的信心,反之亦然。另外,提出了一个用户邻居组件来显式规范潜在空间中的用户邻居接近度。这两个组件组合成一个统一的模型,以相互补充以完成推荐任务。在三个公共数据集上进行的大量实验表明,所提出的模型始终优于最新的基于邻域的推荐器。此外,我们研究了提出的模型的不同变体,以证明提出的混合门控网络和用户邻居建模组件的基本直觉是正确的。

更新日期:2020-08-29
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