Attributed Heterogeneous Information Network Embedding with Self-Attention Mechanism for Product Recommendation
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摘要: 基于异构信息网络嵌入的推荐技术能够有效地捕捉网络中的结构信息，从而提升推荐性能.然而现有的基于异构信息网络嵌入的推荐技术不仅忽略了节点的属性信息与节点间多种类型的边关系，还忽略了节点不同的属性信息对推荐结果不同的影响.为了解决上述问题，提出一个自注意力机制的属性异构信息网络嵌入的商品推荐(attributed heterogeneous information network embedding with self-attention mechanism for product recommendation, AHNER)框架.该框架利用属性异构信息网络嵌入学习用户与商品统一、低维的嵌入表示，并在学习节点嵌入表示时，考虑到不同属性信息对推荐结果的影响不同和不同边关系反映用户对商品不同程度的偏好，引入自注意力机制挖掘节点属性信息与不同边类型所蕴含的潜在信息并学习属性嵌入表示.与此同时，为了克服传统点积方法作为匹配函数的局限性，该框架还利用深度神经网络学习更有效的匹配函数解决推荐问题.AHNER在3个公开数据集上进行大量的实验评估性能，实验结果表明AHNER的可行性与有效性.Abstract: Heterogeneous network embedding based recommendation technology has the capability to capture the structural information in the network effectively, thus improving the recommendation performance. However, the existing recommendation technology based on heterogeneous network embedding not only ignores the attribute information of nodes and various types of edge relations between nodes, but also ignores the diverse influences of different nodes’ attribute information on recommendation results. To address the above issues, a product recommendation framework based on attributed heterogeneous information network embedding with self-attention mechanism (AHNER) is proposed. The framework utilizes attributed heterogeneous information network embedding to learn the unified low-dimensional embedding representations of users and products. When learning node embedding representation, considering that different attribute information has different effects on recommendation results and different edge relations between nodes reflect users’ different preferences for products, self-attention mechanism is exploited to mine the latent information of node attribute information and different edge types and learn attribute embedding representation is learned. Meanwhile, in order to overcome the limitation of traditional dot product method as matching function, the framework also exploits deep neural network to learn more effective matching function to solve the recommendation problem. We conduct extensive experiments on three public datasets to evaluate the performance of AHNER. The experimental results reveal that AHNER is feasible and effective.
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期刊类型引用(2)
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百度学术
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