1] Web Sciences Center, University of Electronic Science and Technology of China, Chengdu 611731, P.R. China [2] State Key Laboratory of Networking and Switching Technology, Beijing 100876, P.R. China.
1] Department of Physics, University of Fribourg, Fribourg CH1700, Switzerland [2] School of Systems Science, Beijing Normal University, Beijing 100875, PR China.
Sci Rep. 2014 Aug 21;4:6140. doi: 10.1038/srep06140.
With the rapid growth of the Internet and overwhelming amount of information that people are confronted with, recommender systems have been developed to effectively support users' decision-making process in online systems. So far, much attention has been paid to designing new recommendation algorithms and improving existent ones. However, few works considered the different contributions from different users to the performance of a recommender system. Such studies can help us improve the recommendation efficiency by excluding irrelevant users. In this paper, we argue that in each online system there exists a group of core users who carry most of the information for recommendation. With them, the recommender systems can already generate satisfactory recommendation. Our core user extraction method enables the recommender systems to achieve 90% of the accuracy of the top-L recommendation by taking only 20% of the users into account. A detailed investigation reveals that these core users are not necessarily the large-degree users. Moreover, they tend to select high quality objects and their selections are well diversified.
随着互联网的快速发展和人们面临的海量信息，推荐系统已经被开发出来，以有效地支持在线系统中用户的决策过程。到目前为止，人们已经关注了设计新的推荐算法和改进现有的推荐算法。然而，很少有研究考虑不同用户对推荐系统性能的不同贡献。这些研究可以帮助我们通过排除不相关的用户来提高推荐效率。在本文中，我们认为在每个在线系统中，都存在一群核心用户，他们承载了大部分的推荐信息。有了他们，推荐系统已经可以生成令人满意的推荐。我们的核心用户提取方法使推荐系统仅考虑 20%的用户就能达到 90%的 top-L 推荐精度。详细的调查揭示了这些核心用户不一定是大度数用户。此外，他们倾向于选择高质量的对象，并且他们的选择具有很好的多样性。
