• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Knowledge-Based Systems >Predicting individual retweet behavior by user similarity: A multi-task learning approach
【24h】

Predicting individual retweet behavior by user similarity: A multi-task learning approach

机译：通过用户相似性预测个人转推行为：一种多任务学习方法

获取原文
获取原文并翻译 | 示例
       
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Users read microblogs and retweet the most "interesting" tweets to their friends in online social networks. Predicting retweet behavior is extremely challenging due to various reasons. First, the most of existing approaches primarily discuss a global retweet predicting model, with a goal of finding a uniform model that fits all users, but ignore individual behavior. And while social influence plays an important role in information diffusion, this fact has been largely ignored in conventional research. In this paper, we adopt a "microeconomics" approach to a model, and predict the individual retweet behavior. We study relationships between users by considering social similarity, which reflects how a particular retweeting action affects both the originator and the receiver of the retweet. To address the individual and social challenges, we analyze the effect of social similarity on retweet behavior based on a real dataset. Moreover, we cast our predicting problem as a multi-task learning problem. Combining the social and individual understanding, we then propose a novel model for predicting individual retweet behavior. We conduct extensive experiments on a Weibo (http://weibo.com, the largest microblogging service in China) dataset to validate the effectiveness of the proposed model. Our results demonstrate the superior performance of the proposed model, compared with several alternative classification methods. (C) 2015 Elsevier B.V. All rights reserved.
机译：用户阅读微博，并将最“有趣”的推文转发给在线社交网络中的朋友。由于各种原因，预测转发行为非常具有挑战性。首先，大多数现有方法主要讨论全局转推预测模型，其目标是找到适合所有用户但忽略个人行为的统一模型。尽管社会影响力在信息传播中起着重要作用，但这一事实在传统研究中已被很大程度上忽略。在本文中，我们对模型采用“微观经济学”方法，并预测个人的转发行为。我们通过考虑社交相似性来研究用户之间的关系，这反映了特定的转发操作如何影响转发的发起者和接收者。为了解决个人和社会挑战，我们基于真实数据集分析了社会相似性对转推行为的影响。此外，我们将预测问题转换为多任务学习问题。结合社会和个人的理解，然后我们提出了一种预测个人转发行为的新颖模型。我们在微博（http://weibo.com，中国最大的微博服务）数据集上进行了广泛的实验，以验证该模型的有效性。与几种替代分类方法相比，我们的结果证明了所提出模型的优越性能。 （C）2015 Elsevier B.V.保留所有权利。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号