div2vec：强调多样性的节点嵌入
div2vec: Diversity-Emphasized Node Embedding
摘要：
近年来，图表示学习在推荐系统中的应用越来越受到人们的关注。然而，大多数现有的研究都集中在提高准确性，但在现实世界的系统中，建议的多样性，以及应考虑改善用户体验。在本文中，我们提出了强调多样性的节点嵌入div2vec，这是一种基于随机行走的无监督学习方法，类似于DeepWalk和node2vec。在生成随机游走时，DeepWalk和node2vec对度高的节点多采样，度低的节点少采样。另一方面，div2vec以与其度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。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，我们评估了两个现实世界的服务，WATCHA和LINE钱包优惠券，并观察到div2vec通过多样化的系统提高了推荐质量。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
