Modeling social strength in social media community via kernel-based learning
Modeling social strength in social media community via kernel-based learning
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DOI：
10.1145/2072298.2072315
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发表时间：
2011-11
期刊：
影响因子：
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通讯作者：
Jinfeng Zhuang;Tao Mei;S. Hoi;Xiansheng Hua;Shipeng Li
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文献类型：
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作者：
Jinfeng Zhuang;Tao Mei;S. Hoi;Xiansheng Hua;Shipeng Li
对社会网络中的连续社会力量而不是传统的二元社会关系进行建模，可以更精确地描述人与人之间的社会关系。在本文中，我们研究的问题，社会力量建模（SSM）的用户在社会媒体社区，谁是典型的与不同形式的数据。特别是，我们以Flickr-最流行的在线照片共享社区-为例，其中用户通过大量的多模态内容（例如，照片、标签、地理位置、朋友列表）和社交行为（例如，评论和加入兴趣小组）。Flickr中的异构数据为SSM研究社区带来了机遇和挑战。如何有效地挖掘异质数据，并将其最优地联合收割机组合起来以衡量社会力量，是社会力量评估的关键问题之一。在本文中，我们提出了一个基于内核的学习排名框架推断Flickr用户的社会力量，这涉及到两个学习阶段。第一阶段采用核目标对齐算法将异构数据整合到一个整体的相似性空间。第二阶段利用学习后的核函数，将两两学习的方法修正为排序方法来估计社会强度。通过学习社会强度图，我们能够进行协同推荐和集体分类。实验结果表明，基于学习的方法是有效的。尽管专注于Flickr，我们的技术可以应用于任何其他社交媒体社区的用户的社会力量模型。
Modeling continuous social strength rather than conventional binary social ties in the social network can lead to a more precise and informative description of social relationship among people. In this paper, we study the problem of social strength modeling (SSM) for the users in a social media community, who are typically associated with diverse form of data. In particular, we take Flickr---the most popular online photo sharing community---as an example, in which users are sharing their experiences through substantial amounts of multimodal contents (e.g., photos, tags, geo-locations, friend lists) and social behaviors (e.g., commenting and joining interest groups). Such heterogeneous data in Flickr bring opportunities yet challenges to the research community for SSM. One of the key issues in SSM is how to effectively explore the heterogeneous data and how to optimally combine them to measure the social strength. In this paper, we present a kernel-based learning to rank framework for inferring the social strength of Flickr users, which involves two learning stages. The first stage employs a kernel target alignment algorithm to integrate the heterogeneous data into a holistic similarity space. With the learned kernel, the second stage rectifies the pair-wise learning to rank approach to estimating the social strength. By learning the social strength graph, we are able to conduct collaborative recommendation and collective classification. The promising results show that the learning-based approach is effective for SSM. Despite being focused on Flickr, our technique can be applied to model social strength of users in any other social media community.