论文标题
DIV2VEC：多样性的节点嵌入
div2vec: Diversity-Emphasized Node Embedding
论文作者
论文摘要
最近，在推荐系统中，图表表示学习的兴趣正在迅速增加。但是，大多数现有的研究都集中在提高准确性上，但是在现实世界中，也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嵌入Div2Vec的节点，这是一种基于步行的无监督学习方法，例如DeepWalk和Node2VEC。当产生随机步行时，较高程度的较高程度和较低程度的节点较小时，深行和节点2VEC样品节点。另一方面，Div2Vec样品段的概率与程度成反比，因此每个节点都可以均匀地属于随机步行的集合。该策略改善了推荐模型的多样性。 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提高了建议性能。此外，我们评估了针对两种现实世界服务（WatchA和Line Wallet Coupon）的拟议模型，并观察到DIV2VEC通过使系统多样化提高了建议质量。
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.
