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Recently, the interest of graph representation learning has been rapidlyincreasing in recommender systems. However, most existing studies have focusedon improving accuracy, but in real-world systems, the recommendation diversityshould be considered as well to improve user experiences. In this paper, wepropose the diversity-emphasized node embedding div2vec, which is a randomwalk-based unsupervised learning method like DeepWalk and node2vec. Whengenerating random walks, DeepWalk and node2vec sample nodes of higher degreemore and nodes of lower degree less. On the other hand, div2vec samples nodeswith the probability inversely proportional to its degree so that every nodecan evenly belong to the collection of random walks. This strategy improves thediversity of recommendation models. Offline experiments on the MovieLensdataset showed that our new method improves the recommendation performance interms of both accuracy and diversity. Moreover, we evaluated the proposed modelon two real-world services, WATCHA and LINE Wallet Coupon, and observed thediv2vec improves the recommendation quality by diversifying the system.

div2vec: Diversity-Emphasized Node Embedding