流媒体推荐系统
Streaming Recommender Systems
摘要：
现实世界中推荐系统的日益普及产生的数据连续和快速，它成为更现实的研究流场景下的推荐系统。数据流具有时序性、连续性和高速性等特点，这给传统的推荐系统带来了巨大的挑战。在本文中，我们研究了流输入的推荐问题。特别是，我们提供了一个原则性的框架，称为sRec，它提供了明确的连续时间随机过程模型的创建用户和主题，以及他们的利益的演变。提出了一种称为递归平均场近似的变分贝叶斯方法，它允许计算效率高的瞬时在线推理。几个真实世界的数据集上的实验结果表明，我们的sRec比其他国家的最先进的优势。
英文摘要：
The increasing popularity of real-world recommender systems produces data continuously and rapidly, and it becomes more realistic to study recommender systems under streaming scenarios. Data streams present distinct properties such as temporally ordered, continuous and high-velocity, which poses tremendous challenges to traditional recommender systems. In this paper, we investigate the problem of recommendation with stream inputs. In particular, we provide a principled framework termed sRec, which provides explicit continuous-time random process models of the creation of users and topics, and of the evolution of their interests. A variational Bayesian approach called recursive meanfield approximation is proposed, which permits computationally efficient instantaneous on-line inference. Experimental results on several real-world datasets demonstrate the advantages of our sRec over other state-of-the-arts.
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。
