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摘 要：随着社交网络的发展，特别是伴随着微博客这一新型社交网络媒体的兴起，社交平台已经成为用户发布、获取消息的最重要渠道之一。截止目前，仅新浪微博的注册用户数已经超过5亿人，日均信息发布数量已经超过1亿条。用户可以发布不超过140字的博文，他们发布的内容会出现在关注他们的用户的时间线里。借助转发功能，用户可以使信息在用户间产生滚雪球式级联转发，从而令普通用户也有可能产生巨大的影响力。通过将合适的用户提及（@）在微博中，他们将会收到系统发出的微博提及提醒，他们可能的转发将会帮助微博提升传播力。该工作设计了一种全新算法，通过寻找最合适的提及对象来提升一条微博的传播力。在研究过程中，我们深入调研了微博的提及机制，并且提出了一个推荐算法，来解决在微博中提及谁能使微博传播力最大化的问题。在该工作中我们将微博提及的推荐问题，转换为一个排序问题。该问题与传统问题相比存在着四大全新挑战，包括：排序模型的相关性需要与信息传播相关；需要构建基于话题的用户关系模型；推荐存在严格的长度限制；推荐结果容易造成提及过载等问题。因此我们构建了一个全新的排序模型：我们考虑了用户兴趣与微博内容契合度、基于话题的用户关系、以及用户影响力指标等三大类因素作为排序的特征；我们构建了以信息传播力为标准的新排序相关性模型；我们基于机器学习的方法，训练一个全新的排序函数。在实验过程中，我们搜集了来自新浪微博的大量真实的用户信息，我们设计了多种对照算法，横向测试了算法的表现。同时，我们还针对算法使用的不同属性的效用，针对推荐长度限制、推荐过载等问题，分别设计了对应的实验。经过详尽的实验比较，我们提出的算法的表现要远优于其他对照算法。我们的算法在只推荐极少量用户的情况下，也能取得良好的推荐效果。同时我们设计的算法推荐结果分布较为平滑，不易出现推荐过载的问题。
关键词：信息传播 社交网络 提及推荐 排序
Abstract：Nowadays， micro-blogging systems like Twitter have become one of the most important ways for information sharing. In Twitter， a user posts a message （tweet） and the others can forward the message （retweet）. Mention is a new feature in micro-blogging systems. By mentioning users in a tweet， they will receive notifications and their possible retweets may help to initiate large cascade diffusion of the tweet. To enhance a tweet’s diffusion by finding the right persons to mention， we propose in this paper a novel recommendation scheme named as whom-to-mention. Specifically， we present an in-depth study of mention mechanism and propose a recommendation scheme to solve the essential question of whom to mention in a tweet. In this paper， whom-to-mention is formulated as a ranking problem and we try to address several new challenges which are not well studied in the traditional information retrieval tasks. By adopting features including user interest match， content-dependent user relationship and user influence， a machine learned ranking function is trained based on newly defined information diffusion based relevance. The extensive evaluation using data gathered from real users demonstrates the advantage of our proposed algorithm compared with the traditional recommendation methods.
Key Words：Information diffusion； Social network； Mention recommendation； Ranking
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关键词：信息传播 社交网络 提及推荐 排序
Abstract：Nowadays， micro-blogging systems like Twitter have become one of the most important ways for information sharing. In Twitter， a user posts a message （tweet） and the others can forward the message （retweet）. Mention is a new feature in micro-blogging systems. By mentioning users in a tweet， they will receive notifications and their possible retweets may help to initiate large cascade diffusion of the tweet. To enhance a tweet’s diffusion by finding the right persons to mention， we propose in this paper a novel recommendation scheme named as whom-to-mention. Specifically， we present an in-depth study of mention mechanism and propose a recommendation scheme to solve the essential question of whom to mention in a tweet. In this paper， whom-to-mention is formulated as a ranking problem and we try to address several new challenges which are not well studied in the traditional information retrieval tasks. By adopting features including user interest match， content-dependent user relationship and user influence， a machine learned ranking function is trained based on newly defined information diffusion based relevance. The extensive evaluation using data gathered from real users demonstrates the advantage of our proposed algorithm compared with the traditional recommendation methods.
Key Words：Information diffusion； Social network； Mention recommendation； Ranking
阅读全文链接（需实名注册）：http：//www.nstrs.cn/xiangxiBG.aspx？id=48557&flag=1