Splitting approaches for context-aware recommendation: an empirical study

Yong Zheng, R. Burke, B. Mobasher
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引用次数: 66

Abstract

User and item splitting are well-known approaches to context-aware recommendation. To perform item splitting, multiple copies of an item are created based on the contexts in which it has been rated. User splitting performs a similar treatment with respect to users. The combination of user and item splitting: UI splitting, splits both users and items in the data set to boost context-aware recommendations. In this paper, we perform an empirical comparison of these three context-aware splitting approaches (CASA) on multiple data sets, and we also compare them with other popular context-aware collaborative filtering (CACF) algorithms. To evaluate those algorithms, we propose new evaluation metrics specific to contextual recommendation. The experiments reveal that CASA typically outperform other popular CACF algorithms, but there is no clear winner among the three splitting approaches. However, we do find some underlying patterns or clues for the application of CASA.
上下文感知推荐的分离方法:一项实证研究
用户和项目分割是众所周知的上下文感知推荐方法。要执行项目拆分,需要根据对项目进行评级的上下文创建项目的多个副本。用户分割对用户执行类似的处理。用户和项目分离的组合:UI分离,将数据集中的用户和项目分开,以增强上下文感知的推荐。在本文中,我们在多个数据集上对这三种上下文感知分割方法(CASA)进行了实证比较,并将它们与其他流行的上下文感知协同过滤(CACF)算法进行了比较。为了评估这些算法,我们提出了针对上下文推荐的新评估指标。实验表明,CASA通常优于其他流行的CACF算法,但在三种分裂方法中没有明显的赢家。然而,我们确实发现了CASA应用的一些潜在模式或线索。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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