Locating targets through mention in Twitter
Locating targets through mention in Twitter
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DOI：
10.1007/s11280-014-0299-8
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发表时间：
2015-07
期刊：
影响因子：
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通讯作者：
Liyang Tang;Zhiwei Ni;Hui Xiong;Hengshu Zhu
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文献类型：
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作者：
Liyang Tang;Zhiwei Ni;Hui Xiong;Hengshu Zhu
随着社交网络的爆炸式发展，在社交媒体平台上存在过量的用户生成的内容。事实上，在社交网络中，如何在正确的时间向正确的受众推广正确的信息，现在是一个很大的挑战。为此，在本文中，我们提出了一个综合研究的提及机制，在社交媒体平台，如Twitter，定位目标受众的特定信息。本研究的目的是找出相关性高的有效指标，并达到高的回应率。沿着这条线，我们制定的问题定位目标时，张贴推广为导向的消息作为一个基于排名的推荐任务，并提出了一个上下文感知的推荐框架作为解决方案。具体来说，我们首先提取四类特征，即内容，社会，位置和时间为基础的功能，以衡量出版商，目标和推广信息之间的相关性。然后，我们采用排名支持向量机（SVM）模型作为我们的排名为基础的推荐问题的解决方案。通过引入两个偏置调整参数，即，基于发布者的可信度贡献和目标的响应性，我们的框架可以有效地推荐topKproper用户提及。最后，为了验证所提出的方法，我们进行了广泛的实验从Twitter收集的真实的世界数据集。实验结果清楚地表明，我们的方法优于其他基线具有显着的利润率。
With the explosive development of social networks, there are excessive amount of user-generated contents available on social media platforms. Indeed, in social networks, it is now a big challenge to promote the right information to the right audiences at the right time. To this end, in this paper, we propose an integrated study of the mention mechanism in social media platforms, such as Twitter, towards locating target audiences for specific information. The study goal is to identify effective targets with high relevance and achieve high response rate as well. Along this line, we formulate the problem of locating targets when posting promotion-oriented messages as a ranking based recommendation task, and present a context-aware recommendation framework as a solution. Specifically, we first extract four categories of features, namely content, social, location and time based features, to measure the relevance among publishers, targets and promotion messages. Then, we employ Ranking Support Vector Machine (SVM) model as the solution to our ranking based recommendation problem. By introducing two bias adjustment parameters, i.e., confidence contributions of publishers and the responsiveness of targets, our framework can effectively recommend topKproper users to mention. Finally, to validate the proposed approach, we conduct extensive experiments on a real world dataset collected from Twitter. The experimental results clearly show that our approach outperforms other baselines with a significant margin.