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.

本研究提出了基于随机游走的无监督学习方法，即面向推荐系统的多样性强调节点嵌入div2vec，在离线实验和两项真实世界服务中得出，div2vec方法通过提高recommendation diversity，提高了推荐性能，包括准确性和多样性。

div2vec: 强调多样性的节点嵌入