计算机科学 ›› 2019, Vol. 46 ›› Issue (11A): 153-158.
赵海燕1, 汪静1, 陈庆奎1, 曹健2
ZHAO Hai-yan1, WANG Jing1, CHEN Qing-kui1, CAO Jian2
摘要: 近年来,推荐技术迅速发展,日趋成熟。但是,多数推荐算法都建立在一个理想的假设下,即有足够多的样本数据供我们训练出成熟的模型用于预测或推荐。在实际工业化生产中,一方面,大多数的用户和项目只拥有极少量的标签信息;另一方面,即使依靠历史积累形成的数据集,在分布上也十分不均衡,难以学习出可靠的推荐模型。主动学习的思想认为每个项目给系统带来的“好处”是不等的,因而可以通过特定策略选择某些项目,借助用户与项目之间的交互行为来主动获取相关的偏好信息。应用在推荐系统中的主动学习试图选择数量更少、质量更高的样本来训练模型,既能提高用户体验,又能免受数据集不均衡的束缚。文中综述了近年来主动学习在推荐系统中的应用,并对其发展趋势进行分析。
中图分类号:
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