芭芭拉制造了新闻：挖掘人群的行为，用于时间感知学习排序
Barbara Made the News: Mining the Behavior of Crowds for Time-Aware Learning to Rank
摘要：
在Twitter和其他微博客服务中，由人群产生的新内容往往偏向于即时性：现在正在发生的事情。通过多种媒介传播评论和信息，网络上的用户与有新闻价值的话题互动并产生新的帖子，并产生热门话题。本文提出利用用户的行为动态来估计与主题最相关的时间段。我们的假设源于这样一个事实，即当一个真实世界的事件发生时，它通常在网络上有高峰期：更高的推文量，对相关维基百科文章的新访问和编辑，以及关于该事件的新闻发布。在本文中，我们提出了一种新的时间感知的排名模型，利用多个来源的人群信号。我们的方法建立在两个主要的创新之上。首先，一种统一的方法，给定查询q，挖掘和表示来自多个人群信号源的时间证据。这允许我们预测查询q的文档的时间相关性。第二，一个原则性的检索模型，它集成了时间信号的学习排序框架，排名结果根据预测的时间相关性。在TREC 2013和2014年微博跟踪数据集上的测试结果表明，该模型相对于词汇检索模型和学习排序基线分别提高了13.2%和6.2%.
英文摘要：
In Twitter, and other microblogging services, the generation of new content by the crowd is often biased towards immediacy: what is happening now. Prompted by the propagation of commentary and information through multiple mediums, users on the Web interact with and produce new posts about newsworthy topics and give rise to trending topics. This paper proposes to leverage on the behavioral dynamics of users to estimate the most relevant time periods for a topic. Our hypothesis stems from the fact that when a real-world event occurs it usually has peak times on the Web: a higher volume of tweets, new visits and edits to related Wikipedia articles, and news published about the event. In this paper, we propose a novel time-aware ranking model that leverages on multiple sources of crowd signals. Our approach builds on two major novelties. First, a unifying approach that given query q, mines and represents temporal evidence from multiple sources of crowd signals. This allows us to predict the temporal relevance of documents for query q. Second, a principled retrieval model that integrates temporal signals in a learning to rank framework, to rank results according to the predicted temporal relevance. Evaluation on the TREC 2013 and 2014 Microblog track datasets demonstrates that the proposed model achieves a relative improvement of 13.2% over lexical retrieval models and 6.2% over a learning to rank baseline.
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。
