Deep learning systems have been phenomenally successful in the fields of computer vision, speech recognition, and natural language processing. Recently, researchers have adopted deep learning techniques to tackle collaborative filtering with implicit feedback. However, the existing methods generally profile both users and items directly, while neglecting the similarities between users’ and items’ neighborhoods. To this end, we propose the neighborhood attentional memory networks (NAMN), a deep learning recommendation model applying two dedicated memory networks to capture users’ neighborhood relations and items’ neighborhood relations respectively. Specifically, we first design the user neighborhood component and the item neighborhood component based on memory networks and attention mechanisms. Then, by the associative addressing scheme with the user and item memories in the neighborhood components, we capture the complex user-item neighborhood relations. Stacking multiple memory modules together yields deeper architectures exploring higher-order complex user-item neighborhood relations. Finally, the output module jointly exploits the user and item neighborhood information with the user and item memories to obtain the ranking score. Extensive experiments on three real-world datasets demonstrate significant improvements of the proposed NAMN method over the state-of-the-art methods.

中文翻译：

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推荐系统的邻域注意力记忆网络

深度学习系统在计算机视觉，语音识别和自然语言处理领域取得了惊人的成功。最近，研究人员已采用深度学习技术来处理具有隐式反馈的协作过滤。然而，现有的方法通常直接描述用户和物品，同时忽略了用户和物品的邻域之间的相似性。为此，我们提出了邻域注意力存储网络（NAMN），这是一种深度学习推荐模型，该模型应用两个专用的存储网络分别捕获用户的邻域关系和项目的邻域关系。具体来说，我们首先基于内存网络和注意力机制设计用户邻域组件和项目邻域组件。然后，通过与邻域组件中的用户和项目存储器的关联寻址方案，我们捕获了复杂的用户-项目邻域关系。将多个内存模块堆叠在一起会产生更深的架构，以探索高阶复杂的用户项邻域关系。最终，输出模块与用户和物品存储器共同利用用户和物品邻域信息以获得排名得分。在三个现实世界的数据集上进行的广泛实验表明，与最新方法相比，拟议的NAMN方法得到了显着改进。输出模块与用户和物品存储器共同利用用户和物品邻域信息以获得排名得分。在三个现实世界的数据集上进行的广泛实验表明，与最新方法相比，拟议的NAMN方法得到了显着改进。输出模块与用户和物品存储器共同利用用户和物品邻域信息以获得排名得分。在三个现实世界的数据集上进行的广泛实验表明，与最新方法相比，拟议的NAMN方法得到了显着改进。