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Outer product enhanced heterogeneous information network embedding for recommendation

机译：外部产品增强的异构信息网络嵌入推荐

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摘要

With the rapid development of the internet, more and more sophisticated data can be utilized by recommendation systems to improve their performance. Such data consist of heterogeneous information networks (HINs) made up of multiple nodes and link types. A critical challenge is how to effectively extract and apply the useful HIN information. In particular, the embedding-based recommendation approach has been widely used, as it can extract affluent semantic and structural information from HINs. However, the existing HIN embedding for recommendation methods only combine user embedding and item embedding through a simple concatenation or elementwise product, which does not suffer for an efficient recommendation model. In order to extract and utilize more comprehensive and subtle information from the embedding for recommendation, we propose Outer Product Enhanced Heterogeneous Information Network Embedding for Recommendation, called HopRec. The main idea is to utilize the outer product to model the pairwise relationship between user HIN embedding and item HIN embedding. Specifically, by performing an outer product between user HIN embedding and item HIN embedding, we can obtain a two-dimensional interaction matrix. Subsequently, we can obtain a rating prediction function by integrating matrix factorization (MF), user HIN embedding, item HIN embedding and interaction matrix. The results of experiments conducted on three open benchmark datasets show that HopRec significantly outperforms the state-of-the-art methods.
机译：随着互联网的快速发展，推荐系统可以利用越来越复杂的数据来提高其性能。这些数据包括由多个节点和链路类型组成的异构信息网络（HIN）。临界挑战是如何有效提取和应用有用的帖子信息。特别是，基于嵌入的推荐方法已被广泛使用，因为它可以从何士提取富裕的语义和结构信息。然而，对于推荐方法，现有的HIN嵌入仅将用户嵌入和项目组合通过简单的级联或元素产品，这些产品不会影响有效的推荐模型。为了提取和利用嵌入式建议的更全面和微妙的信息，我们提出了外部产品增强的异构信息网络嵌入推荐，称为HOPREC。主要思想是利用外产物来模拟用户HIN嵌入和项目HIN嵌入之间的成对关系。具体地，通过在用户HIN嵌入和项目HIN嵌入之间执行外部产品，我们可以获得二维交互矩阵。随后，我们可以通过集成矩阵分解（MF），用户HIN嵌入，项目HIN嵌入和交互矩阵来获得评级预测功能。在三个开放基准数据集中进行的实验结果表明，HOPREC显着优于最先进的方法。

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