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首页> 外文会议>2012 6th International Conference on New Trends in Information Science, Service Science and Data Mining >Modeling user's temporal dynamic profile in micro-blogging using clustering method
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Modeling user's temporal dynamic profile in micro-blogging using clustering method

机译：使用聚类方法在微博中建模用户的时间动态轮廓

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To help micro-blogging's users find what they want, we need know what user's interests are, but user's interests are not static but change with time, so it is critical to describe what user's current interests are. The micro-blogging messages published by a user contain the user's interest information; intuitively, the message that was published recently by a user should have a bigger impact on the user's current interest than a message that was published long time ago. This paper uses Latent Dirichlet Allocation to extract topics from all messages posted by a user; then, clusters the user's all messages, and represent user's interests with the centers of the clusters. When clustering objects, if apply a time-varying influence function on each object, each object has different influence on clustering process; we call this clustering process as clustering method with weight. The experiment shows that the centers of clusters generated by clustering method with weight can depict user's temporal dynamic profile more accurately than the centers of clusters generated by clustering method without weight.
机译：为了帮助微博用户找到他们想要的东西，我们需要知道用户的兴趣是什么，但是用户的兴趣不是一成不变的，而是随时间变化的，因此描述用户当前的兴趣是至关重要的。用户发布的微博消息中包含用户的兴趣信息；从直观上讲，与很久以前发布的消息相比，用户最近发布的消息对用户当前的兴趣应该具有更大的影响。本文使用潜在Dirichlet分配从用户发布的所有消息中提取主题。然后，将用户的所有消息聚类，并以聚类中心代表用户的兴趣。在对对象进行聚类时，如果对每个对象应用随时间变化的影响函数，则每个对象对聚类过程都会产生不同的影响。我们将此聚类过程称为具有权重的聚类方法。实验表明，与没有权重的聚类方法所产生的聚类中心相比，具有权重的聚类方法所产生的聚类中心能够更准确地描述用户的时间动态轮廓。

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