BCFNet：一个具有注意力机制的平衡协同过滤网络
BCFNet: A Balanced Collaborative Filtering Network with Attention Mechanism
摘要：
基于协同过滤（CF）的推荐方法已经被广泛研究，其通常可以分为两种类型，即，基于表示学习的CF方法和基于匹配函数学习的CF方法。表示学习试图为用户和项目的表示学习一个公共的低维空间。在这种情况下，如果用户和项目在公共空间中具有更高的相似性，则它们匹配得更好。匹配函数学习试图直接学习将用户-项目对映射到匹配分数的复杂匹配函数。虽然这两种方法都发展得很好，但它们有两个基本缺陷，即，表示学习依赖于应用点积，其对用户和项目的潜在特征具有有限的表达性，而匹配函数学习在捕获低秩关系方面具有弱点。为了克服这些缺陷，我们提出了一种新的推荐模型平衡协同过滤网络（BCFNet），它具有两种类型的方法的优点。此外，设计了一种注意力机制，以更好地捕捉隐含反馈中的隐藏信息，增强神经网络的学习能力。此外，设计了一个平衡模块来缓解DNN中的过拟合问题。在8个真实数据集上的实验证明了该模型的有效性。
英文摘要：
Collaborative Filtering (CF) based recommendation methods have been widely studied, which can be generally categorized into two types, i.e., representation learning-based CF methods and matching function learning-based CF methods. Representation learning tries to learn a common low dimensional space for the representations of users and items. In this case, a user and item match better if they have higher similarity in that common space. Matching function learning tries to directly learn the complex matching function that maps user-item pairs to matching scores. Although both methods are well developed, they suffer from two fundamental flaws, i.e., the representation learning resorts to applying a dot product which has limited expressiveness on the latent features of users and items, while the matching function learning has weakness in capturing low-rank relations. To overcome such flaws, we propose a novel recommendation model named Balanced Collaborative Filtering Network (BCFNet), which has the strengths of the two types of methods. In addition, an attention mechanism is designed to better capture the hidden information within implicit feedback and strengthen the learning ability of the neural network. Furthermore, a balance module is designed to alleviate the over-fitting issue in DNNs. Extensive experiments on eight real-world datasets demonstrate the effectiveness of the proposed model.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
