Folksonies中的Folks：基于共享元数据的社会链接预测
Folks in Folksonomies: Social Link Prediction from Shared Metadata
摘要：
Web 2.0应用程序吸引了大量的关注，因为它们的开放性允许用户创建轻量级的语义脚手架来组织和共享内容。到目前为止，社交媒体的社会和语义组件的相互作用只被部分探讨。在这里，我们专注于Flickr和Last.fm，这两个社交媒体系统，我们可以将用户的标记活动与其社交网络的显式表示联系起来。我们发现，在社交网络中彼此靠近的用户之间可以观察到大量的局部词汇和主题对齐。我们引入了一个空模型，保留用户活动，同时删除本地相关性，使我们能够解开用户之间的实际本地对齐的统计效果，由于用户活动和中心在社交网络中的强制混合。该分析表明，具有相似主题兴趣的用户更有可能成为朋友，因此仅基于其注释元数据的用户之间的语义相似性度量应该预测社交链接。我们在Last.fm数据集上测试了这一假设，证实了从语义相似性构建的社交网络比Last.fm基于收听模式的建议更准确地捕捉到了实际的友谊。
英文摘要：
Web 2.0 applications have attracted a considerable amount of attention because their open-ended nature allows users to create light-weight semantic scaffolding to organize and share content. To date, the interplay of the social and semantic components of social media has been only partially explored. Here we focus on Flickr and Last.fm, two social media systems in which we can relate the tagging activity of the users with an explicit representation of their social network. We show that a substantial level of local lexical and topical alignment is observable among users who lie close to each other in the social network. We introduce a null model that preserves user activity while removing local correlations, allowing us to disentangle the actual local alignment between users from statistical effects due to the assortative mixing of user activity and centrality in the social network. This analysis suggests that users with similar topical interests are more likely to be friends, and therefore semantic similarity measures among users based solely on their annotation metadata should be predictive of social links. We test this hypothesis on the Last.fm data set, confirming that the social network constructed from semantic similarity captures actual friendship more accurately than Last.fm's suggestions based on listening patterns.
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。
