Recently, the interest of graph representation learning has been rapidly increasing in recommender systems. However, most existing studies have focused on improving accuracy, but in real-world systems, the recommendation diversity should be considered as well to improve user experiences. In this paper, we propose the diversity-emphasized node embedding div2vec, which is a random walk-based unsupervised learning method like DeepWalk and node2vec. When generating random walks, DeepWalk and node2vec sample nodes of higher degree more and nodes of lower degree less. On the other hand, div2vec samples nodes with the probability inversely proportional to its degree so that every node can evenly belong to the collection of random walks. This strategy improves the diversity of recommendation models. Offline experiments on the MovieLens dataset showed that our new method improves the recommendation performance in terms of both accuracy and diversity. Moreover, we evaluated the proposed model on two real-world services, WATCHA and LINE Wallet Coupon, and observed the div2vec improves the recommendation quality by diversifying the system.

中文翻译：

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div2vec：强调多样性的节点嵌入

最近，图表示学习在推荐系统中的兴趣迅速增加。然而，现有的大多数研究都集中在提高准确性上，但在现实世界的系统中，还应考虑推荐多样性以改善用户体验。在本文中，我们提出了强调多样性的节点嵌入 div2vec，这是一种类似于 DeepWalk 和 node2vec 的基于随机游走的无监督学习方法。在生成随机游走时，DeepWalk 和 node2vec 对较高度的节点采样较多，对较低度的节点采样较少。另一方面，div2vec 以与其度数成反比的概率对节点进行采样，以便每个节点都可以均匀地属于随机游走的集合。该策略提高了推荐模型的多样性。在 MovieLens 数据集上的离线实验表明，我们的新方法在准确性和多样性方面都提高了推荐性能。此外，我们在两个现实世界的服务 WATCHA 和 LINE Wallet Coupon 上评估了所提出的模型，并观察到 ​​div2vec 通过使系统多样化来提高推荐质量。