通过聚合用户上下文进行上下文感知推荐

Dongmin Shin, Jae-won Lee, Jongheum Yeon, Sang-goo Lee
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引用次数: 59

摘要

传统的推荐方法没有考虑用户偏好随上下文的变化。因此,这些方法考虑了用户的总体偏好,尽管用户对项目的偏好会根据他/她的上下文而变化。然而,在我们的上下文感知方法中,我们不仅考虑用户偏好,还考虑上下文信息。我们的方法可以很容易地用于基于内容和基于协作过滤的推荐。为了在推荐中利用原始上下文信息,我们将原始上下文信息抽象到概念级别。此外,通过对上下文信息的聚合,可以提高推荐的质量。几个实验结果表明,我们的方法比传统的推荐方法更精确。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Context-Aware Recommendation by Aggregating User Context
Traditional recommendation approaches do not consider the changes of user preferences according to context. As a result, these approaches consider the user’s overall preferences, although the user preferences on items varies according to his/her context. However, in our context-aware approach, we take into account not only user preferences, but also context information. Our approach can be easily adopted for content-based and collaborative filtering based recommendations. To exploit raw context information in recommendation, we abstract the raw context information to a concept level. Moreover, by aggregating the context information, we can improve the quality of recommendation. The results of several experiments show that our method is more precise than the traditional recommendation approaches.
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