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.
翻译:最近,在推荐者系统中,图表代表学习的兴趣迅速增加,然而,大多数现有研究侧重于提高准确性,但在现实世界系统中,建议的多样性应该得到考虑,以改进用户的经验。在本文中,我们建议采用多样性强调的节点嵌div2vec,这是一种随机的、以步行为基础的、不受监督的学习方法,如DeepWalk和Node2vec。当生成随机行走时,DeepWalk和Node2vec等更高层次和较低层次的节点抽样节点。另一方面,div2vec抽样节点的概率与其程度成反比,使每个节点都有可能属于随机行走的收集。这一战略改善了建议模式的多样性。MeopLens数据集的离线实验表明,我们的新方法在准确性和多样性方面提高了建议性。此外,我们评估了两个真实世界服务的拟议模式,WATCHA和LINE Wallet Coup,并观察到div2vec通过使系统多样化来改进建议质量。